A basic problem facing the future in wireless systems is where to find suitable spectrum bands to fulfill the
demand of future services. While all of the radio spectrum is allocated to different services, applications
and users, observation show that usage of the spectrum is actually quite low. To overcome this problem
and improve the spectrum utilization, cognitive radio concept has been evolved. Wireless communication,
in which a transmitter and receiver can detect intelligently communication channels that are in use and
those which are not in use are known as Cognitive Radio, and it can move to unused channels. This makes
possible the use of available radio frequency spectrum while minimizing interference with other users. CRs
must have the capability to learn and adapt their wireless transmission according to the surrounding radio
environment. The application of Artificial Intelligence approaches in the Cognitive Radio is very promising
since they have a great importance for the implementation of Cognitive Radio networks architecture.
Dynamic spectrum access is a promising approach to make less severe the spectrum scarcity that wireless
communications face now. It aims at reusing sparsely occupied frequency bands and does not interfere to
the actual licensees. This paper is a review and comparison of different DSA models and methods.
This document discusses cognitive radio and dynamic spectrum access (DSA). It defines cognitive radio as a new spectrum sharing paradigm that allows secondary users to access spectrum holes or white spaces. DSA allows for improved spectrum utilization by having secondary users dynamically search for and access idle spectrum bands, while avoiding interference to primary users through continuous monitoring. The key components and functions of cognitive radio are described, including spectrum awareness, cognitive processing, and spectrum access. Different DSA models like dynamic exclusive use, open sharing, and hierarchical access are also summarized.
Cognitive radio allows unlicensed secondary users to access licensed spectrum bands not currently in use by licensed primary users through spectrum sensing and dynamic spectrum access. It aims to improve spectrum utilization efficiency by exploiting spectrum holes - unused spectrum portions in time, frequency or space. Key techniques for cognitive radio include spectrum sensing to detect spectrum holes, spectrum sharing which allocates holes to secondary users while avoiding interference to primary users, and spectrum mobility which allows secondary users to handoff between bands when primary users become active. Challenges include hidden terminal problems, synchronization issues and dealing with uncertainties from noise, fading and shadowing.
Cognitive radio is a type of wireless communication that senses its operational environment and can change its transmission parameters accordingly. It allows unlicensed users to access portions of the radio spectrum normally reserved for licensed users, provided they do not cause harmful interference. Key functions of cognitive radio include spectrum sensing, analysis, management and sharing to efficiently utilize available spectrum. It provides benefits like optimal diversity, improved spectrum efficiency and quality of service. However, issues like spectrum management and ensuring co-existence with other systems need to be addressed for cognitive radio to be effectively implemented. Potential applications include use in emergency communications and wireless regional area networks.
CR : smart radio that has the ability to sense the external environment, learn from the history and make intelligent decisions to adjust its transmission parameters according
to the current state of the environment.
Cognitive radio is an enhancement of software defined radio that allows radios to sense their environment and change operating parameters accordingly. It was first proposed in 1998 as a way to more efficiently utilize limited radio frequency spectrum. A cognitive radio can sense available portions of spectrum, then dynamically use those available channels while avoiding occupied ones. This allows for greater spectrum utilization and more flexible interoperability between different wireless technologies. However, cognitive radio also faces significant hardware and software challenges around dynamic reconfiguration, interference avoidance, and security that must be addressed for it to be fully realized.
Multi Channel Protocols In Cognitive Radio NetworksMuhammad Mustafa
Cognitive radio is a paradigm for wireless communication in which either a network or a wireless node changes its transmission or reception parameters to communicate efficiently avoiding interference with licensed or unlicensed users. This alteration of parameters is based on the active monitoring of several factors in the external and internal radio environment, such as radio frequency spectrum, user behaviour and network state. this presentation discusses main approaches and protocols of multichannel cognitive radio networks.
The document discusses spectrum usage and dynamic spectrum access. It notes that current spectrum policies result in inefficient usage, with some blocks saturated and others underused. Improved flexibility and regulation are needed. Context-aware applications and cross-layer optimization, including flexible spectrum use, could enhance service. Most discussions focus on cognitive radio and how it can intelligently manage radio systems and networks to better utilize spectrum.
This document discusses cognitive radio and dynamic spectrum access (DSA). It defines cognitive radio as a new spectrum sharing paradigm that allows secondary users to access spectrum holes or white spaces. DSA allows for improved spectrum utilization by having secondary users dynamically search for and access idle spectrum bands, while avoiding interference to primary users through continuous monitoring. The key components and functions of cognitive radio are described, including spectrum awareness, cognitive processing, and spectrum access. Different DSA models like dynamic exclusive use, open sharing, and hierarchical access are also summarized.
Cognitive radio allows unlicensed secondary users to access licensed spectrum bands not currently in use by licensed primary users through spectrum sensing and dynamic spectrum access. It aims to improve spectrum utilization efficiency by exploiting spectrum holes - unused spectrum portions in time, frequency or space. Key techniques for cognitive radio include spectrum sensing to detect spectrum holes, spectrum sharing which allocates holes to secondary users while avoiding interference to primary users, and spectrum mobility which allows secondary users to handoff between bands when primary users become active. Challenges include hidden terminal problems, synchronization issues and dealing with uncertainties from noise, fading and shadowing.
Cognitive radio is a type of wireless communication that senses its operational environment and can change its transmission parameters accordingly. It allows unlicensed users to access portions of the radio spectrum normally reserved for licensed users, provided they do not cause harmful interference. Key functions of cognitive radio include spectrum sensing, analysis, management and sharing to efficiently utilize available spectrum. It provides benefits like optimal diversity, improved spectrum efficiency and quality of service. However, issues like spectrum management and ensuring co-existence with other systems need to be addressed for cognitive radio to be effectively implemented. Potential applications include use in emergency communications and wireless regional area networks.
CR : smart radio that has the ability to sense the external environment, learn from the history and make intelligent decisions to adjust its transmission parameters according
to the current state of the environment.
Cognitive radio is an enhancement of software defined radio that allows radios to sense their environment and change operating parameters accordingly. It was first proposed in 1998 as a way to more efficiently utilize limited radio frequency spectrum. A cognitive radio can sense available portions of spectrum, then dynamically use those available channels while avoiding occupied ones. This allows for greater spectrum utilization and more flexible interoperability between different wireless technologies. However, cognitive radio also faces significant hardware and software challenges around dynamic reconfiguration, interference avoidance, and security that must be addressed for it to be fully realized.
Multi Channel Protocols In Cognitive Radio NetworksMuhammad Mustafa
Cognitive radio is a paradigm for wireless communication in which either a network or a wireless node changes its transmission or reception parameters to communicate efficiently avoiding interference with licensed or unlicensed users. This alteration of parameters is based on the active monitoring of several factors in the external and internal radio environment, such as radio frequency spectrum, user behaviour and network state. this presentation discusses main approaches and protocols of multichannel cognitive radio networks.
The document discusses spectrum usage and dynamic spectrum access. It notes that current spectrum policies result in inefficient usage, with some blocks saturated and others underused. Improved flexibility and regulation are needed. Context-aware applications and cross-layer optimization, including flexible spectrum use, could enhance service. Most discussions focus on cognitive radio and how it can intelligently manage radio systems and networks to better utilize spectrum.
This document discusses cognitive radio networks (CR). CR networks are defined as networks that are aware of their surroundings and can dynamically reconfigure their characteristics. The document outlines topics that will be discussed including CR antennas, mechanisms, accessibility, adaptivity, scalability, reliability and interconnectivity. It provides examples of how CR networks improve these qualities and can sense spectrum usage, analyze it, decide on parameters, and tune transmissions accordingly using reconfigurable antennas. The document compares CRs to conventional radios and outlines benefits of CRs such as functioning in challenging conditions and identifying unused spectrum. It mentions xG Technology developed CR network technology called xMax.
This document provides an overview of cognitive radio networks including: the objectives of allowing unlicensed secondary users to access licensed spectrum; the centralized and distributed architectures; main issues like sensing, signaling, and spectrum decision; standards like IEEE 802.22; techniques for spectrum sensing, allocation, and sharing; cognitive radio platforms; future research directions; and conclusions. It surveys the technology and challenges of cognitive radio networks to enable efficient spectrum utilization.
Cognitive radio network_MS_defense_presentationIffat Anjum
The document appears to be a student thesis that discusses medium access control (MAC) protocols for coexisting cognitive radio networks (CCRNs). It proposes a new distributed and quality of service aware MAC protocol called WF-MAC. WF-MAC aims to enable fair channel sharing among multiple CCRNs while maintaining QoS sensitivity and maximizing spectrum utilization. It achieves this through a two dimensional learning mechanism for channel selection based on perception and availability prediction.
Cognitive radio is a form of wireless communication that detects available channels in the radio spectrum and moves into vacant channels to avoid occupied ones. It senses its operational environment to detect unused spectrum and adjusts transmission parameters accordingly. This allows for more efficient use of the spectrum by taking advantage of spectrum white spaces. The cognitive cycle involves spectrum sensing, decision, sharing, and mobility. Cognitive radio networks apply these principles across network resources as well to optimize performance. Techniques like software-defined radio, machine learning, game theory, and cross-layer design help enable cognitive capabilities. Cognitive radio could help address increasing mobile data usage and be applied in 5G networks and new applications.
This document discusses security issues in cognitive radio networks. It introduces cognitive radio and describes its ability to sense available spectrum and adapt to optimize usage. The document outlines techniques for spectrum sensing, including matched filtering, cyclostationary feature detection, and energy detection. It also discusses the problem of primary user emulation attacks, where secondary users pretend to be primary users to prevent other secondary users from accessing idle spectrum. Simulation results are presented showing the effects of these malicious users. An altered system model is then proposed to help counteract primary user emulation attacks.
Cognitive Radio: When might it Become Economically and Technically Feasible? Jeffrey Funk
My Master's students use ideas from my (Jeff Funk) forthcoming book (Technology Change and the Rise of New Industries) to analyze the economic and technical feasibility of cognitive radio. See my other slides for details on concepts, methodology, and other new industries.
Spectrum Sensing in Cognitive Radio Networks : QoS Considerations csandit
This document discusses spectrum sensing methods in cognitive radio networks and their impact on quality of service (QoS). It analyzes several spectrum sensing methods including energy detection, covariance-based detection, cyclostationarity feature detection, correlation detection, radio identification based sensing, and matched filtering. These methods are categorized as requiring no prior information, requiring prior information, or being based on cooperation between secondary users. The document notes that imperfect spectrum sensing can degrade QoS for both primary and secondary users. It also discusses how increasing sensing time and frequency improves detection of primary users but reduces data transmission time and degrades QoS for secondary users.
Cognitive radio is an intelligent wireless communication system that is aware of its environment and can learn and adapt to better utilize available spectrum. It aims for highly reliable communication and efficient spectrum usage. Cognitive radios use radio scene analysis to detect spectrum holes by analyzing signals over time and space. They also estimate interference temperature using spectral estimation and adaptive beamforming. Transmit power control and dynamic spectrum management allow cognitive radios to opportunistically access spectrum holes while avoiding interference. Future work may focus on language understanding, MIMO techniques, and nanoscale processing to improve cognitive radio capabilities.
The document discusses the fundamental limits of cognitive radios. It analyzes the capacity of three types of cognitive radios - overlay, interweave, and underlay cognitive radio. For overlay cognitive radio, it presents an outer bound and achievable scheme for the partially cognitive radio channel and evaluates it numerically. The analysis provides insights into the capacity limits of cognitive radios under different channel conditions and levels of cognitive information.
This document discusses cognitive radio, including its definition, history, key concepts, and applications. It begins with an introduction to software defined radio and cognitive radio. It then covers spectrum sensing, management, and different sensing techniques. The document discusses how cognitive radios know their environment and can adapt based on learning. It also describes cooperative and non-cooperative sensing approaches and lists some challenges of cognitive radio technology. The applications and advantages of cognitive radio are summarized before concluding with an admission that cognitive radio is still an area of active research.
This document discusses cognitive radio and its radio frequency (RF) challenges. It begins by introducing dynamic spectrum access and the concept of cognitive radio as a way to opportunistically access unused licensed spectrum. It then discusses the key elements of cognitive radio systems, including spectrum sensing to detect unused spectrum and flexible waveforms. It focuses on the interweave approach where secondary users access spectral holes not in use by primary licensed users. Finally, it discusses some of the RF challenges in implementing cognitive radio, particularly wideband spectrum sensing to reliably detect primary user signals across frequency bands.
Cognitive radio is a type of software-defined radio that can be configured dynamically depending on its surroundings. It aims to improve spectrum utilization by detecting unused spectrum and adapting intelligently. Key aspects include sensing the environment, evaluating options, and implementing the chosen waveform. Cognitive radio allows for open spectrum sharing by avoiding interference through sensing available spectrum, while intelligent antennas focus on spatial reuse through techniques like beamforming and interference cancellation. Some applications of cognitive radio include extending mobile networks coverage, providing connectivity at open-air events, and enabling multi-technology phones.
Dynamic Spectrum Allocation in Wireless sensor NetworksIJMER
Radio frequency spectrum is considered the most expensive and scarce resource among all wireless
network resources, and it is closely followed by the energy consumption, especially in low energy, battery powered
wireless sensor network devices. These days, there is a tremendous growth in the applications of wireless sensor
networks (WSNs) operating in unlicensed spectrum bands (ISM). Moreover, due to the rapid growth of wireless
devices that are designed to be operated in unlicensed spectrum bands, these spectrum bands have been overcrowded.
The problem with overcrowded spectrum or scarcity of spectrum can be solved by Dynamic Allocation of Spectrum.
In this paper we have presented the implementation and analysis of dynamic spectrum allocation in Wireless Sensors
Networks using the concept of Cognitive Radio Ad Hoc Network.
1) Cognitive radio is a smart radio that can identify idle spectrum to transmit its own signals. It is based on software-defined radio and allows for opportunistic usage of available frequencies not being used by primary users.
2) Spectrum sensing techniques like cyclostationary feature detection can be used to detect primary user transmissions by analyzing the cyclic spectral correlation function. This method is more reliable and provides noise immunity.
3) Cooperative spectrum sensing allows multiple cognitive radios to cooperate and share sensing results to overcome issues like shadowing and multipath fading. This improves detection accuracy and agility.
The document provides an overview of cognitive radio networks and spectrum sharing. It discusses how cognitive radio allows for opportunistic and adaptive usage of spectrum. It defines primary and secondary users and describes the cognitive radio network architecture. It then covers spectrum sensing, management, mobility and sharing in cognitive radio systems. Game theory approaches to modeling spectrum sharing are also summarized.
Simulation and analysis of cognitive radioijngnjournal
The increasing demand of wireless applications has put a lot of limitations on the use of available
radio spectrum is limited and precious resource. Many survey of spectrum utilization shows that entire
spectrum is not used at all the times, so many of the radio spectrum is underutilized. Some of the frequency
bands in the spectrum are unoccupied, some of the frequency bands less occupied and few bands are over
utilized. Cognitive radio system is a technique which overcomes that spectrum underutilization. Cognitive
radio is a technique where secondary user looks for a free band to use when primary user is not in use of
its licensed band. A function of cognitive radio is called Spectrum sensing which enables to search for the
free bands and it helps to detect the spectrum hole (frequency band which is free enough to be used) which
can be utilized by secondary user with high spectral resolution capability. The idea of simulation and
analysis of Cognitive Radio System to reuse unused spectrum to increase the total system capacity was
brought in this paper and this work digs into the practical implementation of a Cognitive radio system.
MATLAB R2007b (version7.5) has been used to test the performance of Cognitive radio dynamically.
NetSim(http://www.tetcos.com/ ) Simulator provide Cognative Radio network
follow this link for more Details
http://www.tetcos.com/
Cognitive radio (CR) is a form of wireless communication in which a transceiver can intelligently detect which communication channels are in use and which are not, and instantly move into vacant channels while avoiding occupied ones
This document provides an overview of cognitive radio and dynamic spectrum access. It discusses how cognitive radio can detect unused portions of the radio frequency spectrum and allow secondary users to access those "spectrum holes" without interfering with primary licensed users. The document outlines the key functions of cognitive radio including spectrum sensing, spectrum management, and dynamic spectrum access. It also describes different models for dynamic spectrum access, including dynamic exclusive use, open sharing, and hierarchical access models.
A SURVEY ON DYNAMIC SPECTRUM ACCESS TECHNIQUES IN COGNITIVE RADIOijngnjournal
Wireless networks are characterized by fixed spectrum policy. With increasing demands for wireless communication efficiently using the spectrum resources has become an essential issue. Cognitive radio is a form of wireless communication which is used to sense the spectrum and find the free spectrum. It is used by unlicensed users without causing interference to the licensed user. Cognitive radio with the dynamic spectrum access is key technology which provides the best solution by allowing a group of Secondary users to share the radio spectrum originally allocated to the primary users. Dynamically accessing the unused spectrum is known as dynamic spectrum access (DSA) which becomes a promising approach to increase
the efficiency of spectrum usage. In this paper, DSA models are discussed along with different methods such as game theory based method, a measurement-based model, network coded cognitive control channel, Markovian Queuing model, the Delay performance of threshold policies, fuzzy logic based method and spatio-temporal spectrum management model.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
This document discusses cognitive radio networks (CR). CR networks are defined as networks that are aware of their surroundings and can dynamically reconfigure their characteristics. The document outlines topics that will be discussed including CR antennas, mechanisms, accessibility, adaptivity, scalability, reliability and interconnectivity. It provides examples of how CR networks improve these qualities and can sense spectrum usage, analyze it, decide on parameters, and tune transmissions accordingly using reconfigurable antennas. The document compares CRs to conventional radios and outlines benefits of CRs such as functioning in challenging conditions and identifying unused spectrum. It mentions xG Technology developed CR network technology called xMax.
This document provides an overview of cognitive radio networks including: the objectives of allowing unlicensed secondary users to access licensed spectrum; the centralized and distributed architectures; main issues like sensing, signaling, and spectrum decision; standards like IEEE 802.22; techniques for spectrum sensing, allocation, and sharing; cognitive radio platforms; future research directions; and conclusions. It surveys the technology and challenges of cognitive radio networks to enable efficient spectrum utilization.
Cognitive radio network_MS_defense_presentationIffat Anjum
The document appears to be a student thesis that discusses medium access control (MAC) protocols for coexisting cognitive radio networks (CCRNs). It proposes a new distributed and quality of service aware MAC protocol called WF-MAC. WF-MAC aims to enable fair channel sharing among multiple CCRNs while maintaining QoS sensitivity and maximizing spectrum utilization. It achieves this through a two dimensional learning mechanism for channel selection based on perception and availability prediction.
Cognitive radio is a form of wireless communication that detects available channels in the radio spectrum and moves into vacant channels to avoid occupied ones. It senses its operational environment to detect unused spectrum and adjusts transmission parameters accordingly. This allows for more efficient use of the spectrum by taking advantage of spectrum white spaces. The cognitive cycle involves spectrum sensing, decision, sharing, and mobility. Cognitive radio networks apply these principles across network resources as well to optimize performance. Techniques like software-defined radio, machine learning, game theory, and cross-layer design help enable cognitive capabilities. Cognitive radio could help address increasing mobile data usage and be applied in 5G networks and new applications.
This document discusses security issues in cognitive radio networks. It introduces cognitive radio and describes its ability to sense available spectrum and adapt to optimize usage. The document outlines techniques for spectrum sensing, including matched filtering, cyclostationary feature detection, and energy detection. It also discusses the problem of primary user emulation attacks, where secondary users pretend to be primary users to prevent other secondary users from accessing idle spectrum. Simulation results are presented showing the effects of these malicious users. An altered system model is then proposed to help counteract primary user emulation attacks.
Cognitive Radio: When might it Become Economically and Technically Feasible? Jeffrey Funk
My Master's students use ideas from my (Jeff Funk) forthcoming book (Technology Change and the Rise of New Industries) to analyze the economic and technical feasibility of cognitive radio. See my other slides for details on concepts, methodology, and other new industries.
Spectrum Sensing in Cognitive Radio Networks : QoS Considerations csandit
This document discusses spectrum sensing methods in cognitive radio networks and their impact on quality of service (QoS). It analyzes several spectrum sensing methods including energy detection, covariance-based detection, cyclostationarity feature detection, correlation detection, radio identification based sensing, and matched filtering. These methods are categorized as requiring no prior information, requiring prior information, or being based on cooperation between secondary users. The document notes that imperfect spectrum sensing can degrade QoS for both primary and secondary users. It also discusses how increasing sensing time and frequency improves detection of primary users but reduces data transmission time and degrades QoS for secondary users.
Cognitive radio is an intelligent wireless communication system that is aware of its environment and can learn and adapt to better utilize available spectrum. It aims for highly reliable communication and efficient spectrum usage. Cognitive radios use radio scene analysis to detect spectrum holes by analyzing signals over time and space. They also estimate interference temperature using spectral estimation and adaptive beamforming. Transmit power control and dynamic spectrum management allow cognitive radios to opportunistically access spectrum holes while avoiding interference. Future work may focus on language understanding, MIMO techniques, and nanoscale processing to improve cognitive radio capabilities.
The document discusses the fundamental limits of cognitive radios. It analyzes the capacity of three types of cognitive radios - overlay, interweave, and underlay cognitive radio. For overlay cognitive radio, it presents an outer bound and achievable scheme for the partially cognitive radio channel and evaluates it numerically. The analysis provides insights into the capacity limits of cognitive radios under different channel conditions and levels of cognitive information.
This document discusses cognitive radio, including its definition, history, key concepts, and applications. It begins with an introduction to software defined radio and cognitive radio. It then covers spectrum sensing, management, and different sensing techniques. The document discusses how cognitive radios know their environment and can adapt based on learning. It also describes cooperative and non-cooperative sensing approaches and lists some challenges of cognitive radio technology. The applications and advantages of cognitive radio are summarized before concluding with an admission that cognitive radio is still an area of active research.
This document discusses cognitive radio and its radio frequency (RF) challenges. It begins by introducing dynamic spectrum access and the concept of cognitive radio as a way to opportunistically access unused licensed spectrum. It then discusses the key elements of cognitive radio systems, including spectrum sensing to detect unused spectrum and flexible waveforms. It focuses on the interweave approach where secondary users access spectral holes not in use by primary licensed users. Finally, it discusses some of the RF challenges in implementing cognitive radio, particularly wideband spectrum sensing to reliably detect primary user signals across frequency bands.
Cognitive radio is a type of software-defined radio that can be configured dynamically depending on its surroundings. It aims to improve spectrum utilization by detecting unused spectrum and adapting intelligently. Key aspects include sensing the environment, evaluating options, and implementing the chosen waveform. Cognitive radio allows for open spectrum sharing by avoiding interference through sensing available spectrum, while intelligent antennas focus on spatial reuse through techniques like beamforming and interference cancellation. Some applications of cognitive radio include extending mobile networks coverage, providing connectivity at open-air events, and enabling multi-technology phones.
Dynamic Spectrum Allocation in Wireless sensor NetworksIJMER
Radio frequency spectrum is considered the most expensive and scarce resource among all wireless
network resources, and it is closely followed by the energy consumption, especially in low energy, battery powered
wireless sensor network devices. These days, there is a tremendous growth in the applications of wireless sensor
networks (WSNs) operating in unlicensed spectrum bands (ISM). Moreover, due to the rapid growth of wireless
devices that are designed to be operated in unlicensed spectrum bands, these spectrum bands have been overcrowded.
The problem with overcrowded spectrum or scarcity of spectrum can be solved by Dynamic Allocation of Spectrum.
In this paper we have presented the implementation and analysis of dynamic spectrum allocation in Wireless Sensors
Networks using the concept of Cognitive Radio Ad Hoc Network.
1) Cognitive radio is a smart radio that can identify idle spectrum to transmit its own signals. It is based on software-defined radio and allows for opportunistic usage of available frequencies not being used by primary users.
2) Spectrum sensing techniques like cyclostationary feature detection can be used to detect primary user transmissions by analyzing the cyclic spectral correlation function. This method is more reliable and provides noise immunity.
3) Cooperative spectrum sensing allows multiple cognitive radios to cooperate and share sensing results to overcome issues like shadowing and multipath fading. This improves detection accuracy and agility.
The document provides an overview of cognitive radio networks and spectrum sharing. It discusses how cognitive radio allows for opportunistic and adaptive usage of spectrum. It defines primary and secondary users and describes the cognitive radio network architecture. It then covers spectrum sensing, management, mobility and sharing in cognitive radio systems. Game theory approaches to modeling spectrum sharing are also summarized.
Simulation and analysis of cognitive radioijngnjournal
The increasing demand of wireless applications has put a lot of limitations on the use of available
radio spectrum is limited and precious resource. Many survey of spectrum utilization shows that entire
spectrum is not used at all the times, so many of the radio spectrum is underutilized. Some of the frequency
bands in the spectrum are unoccupied, some of the frequency bands less occupied and few bands are over
utilized. Cognitive radio system is a technique which overcomes that spectrum underutilization. Cognitive
radio is a technique where secondary user looks for a free band to use when primary user is not in use of
its licensed band. A function of cognitive radio is called Spectrum sensing which enables to search for the
free bands and it helps to detect the spectrum hole (frequency band which is free enough to be used) which
can be utilized by secondary user with high spectral resolution capability. The idea of simulation and
analysis of Cognitive Radio System to reuse unused spectrum to increase the total system capacity was
brought in this paper and this work digs into the practical implementation of a Cognitive radio system.
MATLAB R2007b (version7.5) has been used to test the performance of Cognitive radio dynamically.
NetSim(http://www.tetcos.com/ ) Simulator provide Cognative Radio network
follow this link for more Details
http://www.tetcos.com/
Cognitive radio (CR) is a form of wireless communication in which a transceiver can intelligently detect which communication channels are in use and which are not, and instantly move into vacant channels while avoiding occupied ones
This document provides an overview of cognitive radio and dynamic spectrum access. It discusses how cognitive radio can detect unused portions of the radio frequency spectrum and allow secondary users to access those "spectrum holes" without interfering with primary licensed users. The document outlines the key functions of cognitive radio including spectrum sensing, spectrum management, and dynamic spectrum access. It also describes different models for dynamic spectrum access, including dynamic exclusive use, open sharing, and hierarchical access models.
A SURVEY ON DYNAMIC SPECTRUM ACCESS TECHNIQUES IN COGNITIVE RADIOijngnjournal
Wireless networks are characterized by fixed spectrum policy. With increasing demands for wireless communication efficiently using the spectrum resources has become an essential issue. Cognitive radio is a form of wireless communication which is used to sense the spectrum and find the free spectrum. It is used by unlicensed users without causing interference to the licensed user. Cognitive radio with the dynamic spectrum access is key technology which provides the best solution by allowing a group of Secondary users to share the radio spectrum originally allocated to the primary users. Dynamically accessing the unused spectrum is known as dynamic spectrum access (DSA) which becomes a promising approach to increase
the efficiency of spectrum usage. In this paper, DSA models are discussed along with different methods such as game theory based method, a measurement-based model, network coded cognitive control channel, Markovian Queuing model, the Delay performance of threshold policies, fuzzy logic based method and spatio-temporal spectrum management model.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
With cloud computing, users can remotely store their data into the cloud and use on-demand high-quality applications. Data outsourcing: users are relieved from the burden of data storage and maintenance When users put their data (of large size) on the cloud, the data integrity protection is challenging enabling public audit for cloud data storage security is important Users can ask an external audit party to check the integrity of their outsourced data. Purpose of developing data security for data possession at un-trusted cloud storage servers we are often limited by the resources at the cloud server as well as at the client. Given that the data sizes are large and are stored at remote servers, accessing the entire file can be expensive in input output costs to the storage server. Also transmitting the file across the network to the client can consume heavy bandwidths. Since growth in storage capacity has far outpaced the growth in data access as well as network bandwidth, accessing and transmitting the entire archive even occasionally greatly limits the scalability of the network resources. Furthermore, the input output to establish the data proof interferes with the on-demand bandwidth of the server used for normal storage and retrieving purpose. The Third Party Auditor is a respective person to manage the remote data in a global manner.
A review paper based on spectrum sensing techniques in cognitive radio networksAlexander Decker
This document summarizes different spectrum sensing techniques for cognitive radio networks. It discusses cooperative detection techniques which involve multiple cognitive radios sharing sensing information, and non-cooperative detection where radios act independently. Specific techniques covered include centralized, distributed, and relay-assisted cooperative sensing as well as blind sensing, energy detection, and eigenvalue-based sensing. The document concludes that cooperative sensing performs better than non-cooperative sensing, especially for low signal-to-noise ratio primary user signals.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Project:- Spectral occupancy measurement and analysis for Cognitive Radio app...Aastha Bhardwaj
This document provides a summary of a report on spectral occupancy measurement and analysis for cognitive radio applications. It discusses using a discone antenna and RF Explorer spectrum analyzer to collect wireless spectrum usage data sets over time. MATLAB is then used to analyze the data sets, including plotting power spectral density vs. frequency graphs and waterfall charts of power spectral density over frequency and time. Time series analysis techniques are applied to model the radio spectrum occupancy for purposes like predicting spectrum availability to enable opportunistic spectrum access in cognitive radio systems. The goal is to study 24-hour spectrum usage patterns and identify underutilized bands that could be accessed without interfering with primary users.
A STUDY ON QUANTITATIVE PARAMETERS OF SPECTRUM HANDOFF IN COGNITIVE RADIO NET...ijwmn
The innovation of wireless technologies requires dynamic allocation of spectrum band in an efficient
manner. This has been achieved by Cognitive Radio (CR) networks which allow unlicensed users to make
use of free licensed spectrum, when the licensed users are kept away from that spectrum. The cognitive
radio makes decision, switching from primary user to secondary user and vice-versa, based on its built-in
interference engine. It allows secondary users to makes use of a channel based on its availability i.e. on the
absence of the primary user and they should vacate the channel once the primary user re-enters and
continue their communication on another available channel and this process in the cognitive radio is
known as spectrum mobility. The main objective of spectrum mobility is that, there is no interruption
caused due to the channel occupied by secondary users and maintains a good quality of service. In order to
achieve better spectrum mobility, it is mandatory to choose an effective spectrum handoff strategy with the
capability of predicting spectrum mobility. The handoff strategy with its parameters and its impact is an
important concept in spectrum mobility but fairly explored. In this paper an empirical study on quantitative
parameters involved in spectrum mobility prediction are discussed in detail. These parameters are studied
extensively because they play a vital role in the spectrum handoff process moreover the impact of these
parameters in various handoff methods can be used to predict the effectiveness of the system.
A STUDY ON QUANTITATIVE PARAMETERS OF SPECTRUM HANDOFF IN COGNITIVE RADIO NET...ijwmn
The innovation of wireless technologies requires dynamic allocation of spectrum band in an efficient
manner. This has been achieved by Cognitive Radio (CR) networks which allow unlicensed users to make
use of free licensed spectrum, when the licensed users are kept away from that spectrum. The cognitive
radio makes decision, switching from primary user to secondary user and vice-versa, based on its built-in
interference engine. It allows secondary users to makes use of a channel based on its availability i.e. on the
absence of the primary user and they should vacate the channel once the primary user re-enters and
continue their communication on another available channel and this process in the cognitive radio is
known as spectrum mobility. The main objective of spectrum mobility is that, there is no interruption
caused due to the channel occupied by secondary users and maintains a good quality of service. In order to
achieve better spectrum mobility, it is mandatory to choose an effective spectrum handoff strategy with the
capability of predicting spectrum mobility. The handoff strategy with its parameters and its impact is an
important concept in spectrum mobility but fairly explored. In this paper an empirical study on quantitative
parameters involved in spectrum mobility prediction are discussed in detail. These parameters are studied
extensively because they play a vital role in the spectrum handoff process moreover the impact of these
parameters in various handoff methods can be used to predict the effectiveness of the system.
A SURVEY ON DYNAMIC SPECTRUM ACCESS TECHNIQUES FOR COGNITIVE RADIOijngnjournal
Cognitive radio (CR) is a new paradigm that utilizes the available spectrum band. The key characteristic of CR system is to sense the electromagnetic environment to adapt their operation and dynamically vary its radio operating parameters. The technique of dynamically accessing the unused spectrum band is known as Dynamic Spectrum Access (DSA). The dynamic spectrum access technology helps to minimize unused spectrum bands. In this paper, main functions of Cognitive Radio (CR) i.e. spectrum sensing, spectrum management, spectrum mobility and spectrum sharing are discussed. Then DSA models are discussed along with different methods of DSA such as Command and Control, Exclusive-Use, Shared Use of Primary Licensed User and Commons method. Game-theoretic approach using Bertrand game model, Markovian Queuing Model for spectrum allocation in centralized architecture and Fuzzy logic based method are also discussed and result are shown.
Cognitive Radio: An Emerging trend for better Spectrum UtilizationEditor IJCATR
Due to the rapid development of wireless communications in recent years, the demand on wireless spectrum has been growing dramatically, resulting in the spectrum scarcity problem. Works have shown that the fixed spectrum allocation policy commonly adopted today suffer from the low spectrum utilization problem. Both academic and regulatory bodies have focused on dynamic spectrum access to fully utilize the scarce spectrum resource. Cognitive radio, with the capability to flexibly adapt its parameters, has been proposed as the enabling technology for unlicensed secondary users to dynamically access the licensed spectrum owned by legacy primary users on a negotiated or an opportunistic basis. In this paper we present a volumetric survey on various methods used to adapt changes used in cognitive radio.
This document discusses cooperative spectrum sensing in cognitive radio networks to improve energy efficiency and throughput. It proposes deriving the optimal number of cooperating cognitive radios under two scenarios: 1) minimizing radios needed for a given detection performance to maximize energy efficiency, and 2) maximizing throughput by optimizing the reporting time given a detection constraint. Computer simulations show that an OR fusion rule outperforms AND in both scenarios using fewer radios.
This document provides an overview of cognitive radio, including its definition, characteristics, applications, and architecture. Cognitive radio is a form of wireless communication that can detect unused portions of the radio spectrum and move between them, improving spectrum efficiency. It has the ability to sense its environment, learn from interactions, and adapt transmission parameters accordingly. Potential applications of cognitive radio discussed include improving regulation and access to spectrum, enhancing public safety networks, and improving cellular networks by offloading data. The document outlines a cognitive radio architecture with components like an RF front-end, software-defined radio modem, and general processor to support reconfigurability and spectrum etiquette policies.
A comprehensive study of signal detection techniques for spectrum sensing in ...IAEME Publication
This document summarizes several signal detection techniques for spectrum sensing in cognitive radio systems. It begins with an introduction to cognitive radio and spectrum sensing. It then describes and compares three main non-cooperative (transmitter-based) detection techniques: matched filter detection, energy detection, and cyclostationary feature detection. Matched filter detection provides optimal detection but requires prior knowledge of the primary user's signal. Energy detection has lower complexity but cannot differentiate signals from noise. Cyclostationary feature detection can detect periodic signals but has higher complexity than energy detection.
A Mathematical Approach for Hidden Node Problem in Cognitive Radio NetworksTELKOMNIKA JOURNAL
Cognitive radio (CR) technology has emerged as a realistic solution to the spectrum scarcity
problem in present day wireless networks. A major challenge in CR radio networks is the hidden node
problem, which is the inability of the CR nodes to detect the primary user. This paper proposes energy
detector-based distributed sequential cooperative spectrum sensing over Nakagami-m fading, as a tool to
solve the hidden node problem. The derivation of energy detection performance over Nakagami-m fading
channel is presented. Since the observation represents a random variable, likelihood ratio test (LRT) is
known to be optimal in this type of detection problem. The LRT is implemented using the Neyman-Pearson
Criterion (maximizing the probability of detection but at a constraint of false alarm probability). The
performance of the proposed method has been evaluated both by numerical analysis and simulations. The
effect of cooperation among a group of CR nodes and system parameters such as SNR, detection
threshold and number of samples per CR nodes is investigated. Results show improved detection
performance by implementing the proposed model.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Spectrum Sensing using Cooperative Energy Detection Method for Cognitive RadioSaroj Dhakal
This document summarizes cooperative spectrum sensing using energy detection in cognitive radio networks. It discusses how cooperative sensing can improve detection performance by exploiting spatial diversity among cognitive radio users. The key points are:
1. Cooperative sensing allows cognitive radio users to share sensing information to make a combined decision that is more accurate than individual decisions. This mitigates issues like multipath fading and shadowing.
2. Energy detection is commonly used for cooperative sensing due to its simplicity. However, its performance depends on noise power uncertainty. Cooperative sensing addresses this by fusing observations from multiple spatially distributed users.
3. The document also discusses challenges in spectrum sensing like hardware requirements, hidden primary users, and detecting spread spectrum
Spectrum Sensing using Cooperative Energy Detection Method for Cognitive RadioSaroj Dhakal
This document summarizes cooperative spectrum sensing using energy detection in cognitive radio networks. It discusses how cooperative sensing can improve detection performance by exploiting spatial diversity among cognitive radio users. The key points are:
1. Cooperative sensing allows cognitive radio users to share sensing information to make a combined decision that is more accurate than individual decisions. This mitigates issues like multipath fading and shadowing.
2. Energy detection is commonly used for cooperative spectrum sensing due to its simplicity. However, its performance depends on noise power uncertainty.
3. Cooperative sensing involves local sensing by each cognitive radio, reporting results to a fusion center, and data fusion to make a combined decision. Centralized, distributed, and relay-
A framework for data traffic in cognitive radio net works using trusted token...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Similar to A cognitive radio and dynamic spectrum access – a study (20)
Trend-Based Networking Driven by Big Data Telemetry for Sdn and Traditional N...ijngnjournal
This document describes a system that uses big data telemetry from networks to enable trend-based networking decisions in SDN and traditional networks. It presents the design of the system, including functional block diagrams and descriptions of the design solution and its levels. It also discusses the implementation including data collection, processing, storage and analysis, and how trends are identified and used to make network configuration changes to balance traffic load. Future work areas are identified such as using machine learning on historical data and connecting traditional and SDN network topologies in hybrid network configurations.
TREND-BASED NETWORKING DRIVEN BY BIG DATA TELEMETRY FOR SDN AND TRADITIONAL N...ijngnjournal
Organizations face a challenge of accurately analyzing network data and providing automated action
based on the observed trend. This trend-based analytics is beneficial to minimize the downtime and
improve the performance of the network services, but organizations use different network management
tools to understand and visualize the network traffic with limited abilities to dynamically optimize the
network. This research focuses on the development of an intelligent system that leverages big data
telemetry analysis in Platform for Network Data Analytics (PNDA) to enable comprehensive trendbased networking decisions. The results include a graphical user interface (GUI) done via a web
application for effortless management of all subsystems, and the system and application developed in
this research demonstrate the true potential for a scalable system capable of effectively benchmarking
the network to set the expected behavior for comparison and trend analysis. Moreover, this research
provides a proof of concept of how trend analysis results are actioned in both a traditional network and
a software-defined network (SDN) to achieve dynamic, automated load balancing.
PERFORMANCE PREDICTION OF 5G: THE NEXT GENERATION OF MOBILE COMMUNICATIONijngnjournal
The 5G standard is a mobile communication of the 5th generation, which presupposes an increase of the information exchange speed up to 10 Gbit/s. It is 30 times quicker than the speed of 4G network. It is a new stage in the development of technologies connecting society. This standard will provide an unlimited access to the network for individual users and devices. When developing the 5G standard, the advanced opportunities of LTE and HSPA, as well as other technologies of a radio access focused on the solution of specific objectives are considered. The main advantage of the mass introduction of the 5G communication development represents the so-called Internet of Things (IoT). There the devices and not people will be the main consumers of traffic. The functional requirements of5G networks, their speed, and its traffic parameters for HD video services and massifs of M2M-devices are analyzed in the paper. They will have been the most demandedones by 2020.
PERFORMANCE EVALUATION OF VERTICAL HARD HANDOVERS IN CELLULAR MOBILE SYSTEMSijngnjournal
With the rapid increase of new and diverse cellular mobile services, the overlapping of cells has become typical in the majority of the coverage area of the network. Vertical handovers occur between two layers of cells when a user is switched from one layer to the other. In this paper we investigate the influence of network parameters on vertical hard handover performance in a cell environment. The work considers two layers of cells: a layer of macrocells and a layer of microcells. Handover requests enter the macrocell from neighbor macrocells and from microcells that belong to a different layer. Using Markov chain analysis and simulation we calculate network performance parameters such as mean queue delay, handover dropping probability and channel utilization. We also compare the handover performance for the macrocell and macrocell traffic separately. Our results show the influence of total channels, maximum queue size and handover request arrival rate on handover performance. They also show that when the traffic from each layer is treated with equal priority in the system, the performance of each layer is comparable.
PERFORMANCE EVALUATION OF VERTICAL HARD HANDOVERS IN CELLULAR MOBILE SYSTEMSijngnjournal
With the rapid increase of new and diverse cellular mobile services, the overlapping of cells has become typical in the majority of the coverage area of the network. Vertical handovers occur between two layers of cells when a user is switched from one layer to the other. In this paper we investigate the influence of network parameters on vertical hard handover performance in a cell environment. The work considers two layers of cells: a layer of macrocells and a layer of microcells. Handover requests enter the macrocell from neighbor macrocells and from microcells that belong to a different layer. Using Markov chain analysis and simulation we calculate network performance parameters such as mean queue delay, handover dropping probability and channel utilization. We also compare the handover performance for the macrocell and macrocell traffic separately. Our results show the influence of total channels, maximum queue size and handover request arrival rate on handover performance. They also show that when the traffic from each layer is treated with equal priority in the system, the performance of each layer is comparable.
COMPARISON OF RADIO PROPAGATION MODELS FOR LONG TERM EVOLUTION (LTE) NETWORKijngnjournal
This paper concerns about the radio propagation models used for the upcoming 4th Generation (4G) of cellular networks known as Long Term Evolution (LTE). The radio wave propagation model or path loss model plays a very significant role in planning of any wireless communication systems. In this paper, a comparison is made between different proposed radio propagation models that would be used for LTE, like Stanford University Interim (SUI) model, Okumura model, Hata COST 231 model, COST Walfisch-Ikegami & Ericsson 9999 model. The comparison is made using different terrains e.g. urban, suburban and rural area.SUI model shows the lowest path lost in all the terrains while COST 231 Hata model illustrates highest path loss in urban area and COST Walfisch-Ikegami model has highest path loss for suburban and rural environments.
IMPLEMENTATION AND COMPARISION OF DATA LINK QUALITY SCHEME ON ODMRP AND ADMR ...ijngnjournal
An ad hoc network is a collection of wireless mobile nodes dynamically forming a temporary network without the use of any fixed network infrastructure or centralized administration. In order to enable communication within the network, a routing protocol is needed to discover routes between nodes. The primary goal of ad hoc network routing protocols is to establish routes between node pairs so that messages may be delivered reliably and in a timely manner. The objective of any routing protocol is to have packet delivered with least possible cost in terms of receiving power, transmission power, battery energy consumption and distance. All these factors basically effect the establishment of link between the mobile nodes and liability and stability of these links. In this paper, we implement a data link quality scheme on two protocols ODMRP and ADMR and compare them on the bases link quality and link stability.
The Performance of a Cylindrical Microstrip Printed Antenna for TM10 Mode as...ijngnjournal
A temperature is one of the parameters that have a great effect on the performance of microstrip antennas for TM10 mode at 2.4 GHz frequency range. The effect of temperature on a resonance frequency, input impedance, voltage standing wave ratio, and return loss on the performance of a cylindrical microstrip printed antenna is studied in this paper. The effect of temperature on electric and magnetic fields are also studied. Three different substrate materials RT/duroid-5880 PTFE, K-6098 Teflon/Glass, and Epsilam-10 ceramic-filled Teflon are used for verifying the new model.
Optimization of Quality of Service Parameters for Dynamic Channel Allocation ...ijngnjournal
This document summarizes a research paper that proposes optimizing quality of service parameters for dynamic channel allocation in cellular networks using a genetic algorithm. It discusses fixed and dynamic channel allocation schemes and describes three quality of service parameters - call duration, number of users, and residual bandwidth - that are considered for optimization. It then provides an overview of genetic algorithms and describes how one was implemented to optimize the three parameters. The genetic algorithm encoded the parameters as bit strings, calculated a fitness function, selected individuals for reproduction probabilistically based on fitness, and applied crossover and mutation over generations to arrive at an optimized allocation scheme. The optimized scheme was then compared to a non-optimized one to evaluate the genetic algorithm's effectiveness.
PURGING OF UNTRUSTWORTHY RECOMMENDATIONS FROM A GRIDijngnjournal
In grid computing, trust has massive significance. There is lot of research to propose various models in providing trusted resource sharing mechanisms. The trust is a belief or perception that various researchers have tried to correlate with some computational model. Trust on any entity can be direct or indirect. Direct trust is the impact of either first impression over the entity or acquired during some direct interaction. Indirect trust is the trust may be due to either reputation gained or recommendations received from various recommenders of a particular domain in a grid or any other domain outside that grid or outside that grid itself. Unfortunately, malicious indirect trust leads to the misuse of valuable resources of the grid. This paper proposes the mechanism of identifying and purging the untrustworthy recommendations in the grid environment. Through the obtained results, we show the way of purging of untrustworthy entities.
HYBRID LS-LMMSE CHANNEL ESTIMATION Technique for LTE Downlink Systemsijngnjournal
- The document proposes a hybrid LS-LMMSE channel estimation technique for LTE downlink systems that is robust to the effect of channel length.
- The technique chooses between LS and LMMSE estimation depending on whether the cyclic prefix is longer than or shorter than the channel length, and on the SNR value.
- When the cyclic prefix is longer than the channel length, LMMSE is used directly. When it is shorter, LMMSE is used for low SNR and LS is used for high SNR.
- Simulation results show the hybrid technique performs better than LMMSE alone, especially at high SNR values when the cyclic prefix is shorter than the channel length.
SERVICES AS PARAMETER TO PROVIDE BEST QOS : AN ANALYSIS OVER WIMAXijngnjournal
In this paper it is proposed to provide the QoS to the user by using the degradation of service under hostile environment being itself be a parameter to improve the QoS. Here the relation between the service and environment of its best performance drawn on the basis of simulation and analysis .The service then taken as a parameter to decide present environment of the user and to take measurable steps to improve the QoS either doing handover to nearby station or increasing power or to provide some marginal bandwidth etc.All analysis done over a WiMax network i.e. being designed and simulated using the Qualnet wireless simulator.
ENSURING QOS GUARANTEES IN A HYBRID OCS/OBS NETWORKijngnjournal
The bursting aggregation assembly in edge nodes is one of the key technologies in OBS (Optical Burst Switching) network, which has a direct impact on flow characteristics and packet loss rate. An optical burst assembly technique supporting QoS is presented through this paper, which can automatically adjust the threshold along with the increasing and decreasing volume of business, reduce the operational burst, and generate corresponding BDP (Burst Data Packet) and BCP (Burst Control Packet). In addition to the burst aggregation technique a packet recovery technique by restoration method is also described. The data packet loss due to the physical optical link failure is not currently included in the QoS descriptions. This link failure is also a severe problem which reduces the data throughput of the transmitter node. A mechanism for data recovery from this link failure is vital for guaranteeing the QoS demanded by each user. So this paper will also discusses a specific protocol for reducing the packet loss by utilizing the
features of both optical circuit switching (OCS) and Optical Burst switching (OBS) techniques
SECURITY ANALYSIS AND DELAY EVALUATION FOR SIP-BASED MOBILE MASS EXAMINATION ...ijngnjournal
IP Multimedia Subsystem (IMS) is considered to be one of the important features in Mobile Next Generation Networks (MNGN). It adds value to the mobile services and applications by integrating mobile network resources, such as location, billing and authentication. This is achieved by enabling a third party access to network resources. In previous work [1] we have presented a testbed to be used as platform for testing mobile application prior to actual deployment. We have chosen a novel IMS based MObile Mass EXamination (MOMEX) system to showcase the benefit of designing an IMS based mobile application. We identify two aspects essential to of the application namely security threats and delay analysis. In this paper we identify MOMEX security threats and suggest strategies to mitigate system vulnerabilities. We then
evaluate the performance of MOMEX system in terms of delay and security threats and vulnerabilities. The results presented show system performance limitation and tradeoffs.
OPTIMIZATION OF QOS PARAMETERS IN COGNITIVE RADIO USING ADAPTIVE GENETIC ALGO...ijngnjournal
Genetic algorithm based optimization rely on explicit relationships between parameters, observations and criteria. GA based optimization when done in cognitive radio can provide a criteria to accommodate the secondary users in best possible space in the spectrum by interacting with the dynamic radio environment at real time. In this paper we have proposed adaptive genetic algorithm with adapting crossover and mutation parameters for the reasoning engine in cognitive radio to obtain the optimum radio configurations. This method ensure better controlling of the algorithm parameters and hence the increasing the performance. The main advantage of genetic algorithm over other soft computing techniques is its multi – objective handling capability. We focus on spectrum management with a hypothesis that inputs are provided by either sensing information from the radio environment or the secondary user. Also the QoS requirements condition is also specified in the hypothesis. The cognitive radio will sense the radio frequency parameter from the environment and the reasoning engine in the cognitive radio will take the required decisions in order to provide new spectrum allocation as demanded by the user. The transmission parameters which can be taken into consideration are modulation method, bandwidth, data rate, symbol rate, power consumption etc. We simulated cognitive radio engine which is driven by genetic algorithm to determine the optimal set of radio transmission parameters. We have fitness objectives to guide one system to an optimal state. These objectives are combined to one multi – objective fitness function using weighted sum approach so that each objective can be represented by a rank which represents the importance of each objective. We have transmission parameters as decision variables and environmental parameters are used as inputs to the objective function. We have compared the proposed adaptive genetic algorithm (AGA) with conventional genetic algorithm (CGA) with same set of conditions. MATLAB simulations were used to analyze the scenarios
HIGH PERFORMANCE ETHERNET PACKET PROCESSOR CORE FOR NEXT GENERATION NETWORKSijngnjournal
As the demand for high speed Internet significantly increasing to meet the requirement of large data transfers, real-time communication and High Definition ( HD) multimedia transfer over IP, the IP based network products architecture must evolve and change. Application specific processors require high
performance, low power and high degree of programmability is the limitation in many general processor based applications. This paper describes the design of Ethernet packet processor for system-on-chip (SoC) which performs all core packet processing functions, including segmentation and reassembly, packetization classification, route and queue management which will speedup switching/routing performance making it
more suitable for Next Generation Networks (NGN). Ethernet packet processor design can be configured for use with multiple projects targeted to a FPGA device the system is designed to support 1/10/20/40/100 Gigabit links with a speed and performance advantage. VHDL has been used to implement and simulated the required functions in FPGA
ESTIMATION AND COMPENSATION OF INTER CARRIER INTERFERENCE IN WIMAX PHYSICAL L...ijngnjournal
WiMAX is Wireless Interoperability for Microwave Access has emerged as a promising solution for transmission of higher data rates for fixed and mobile applications. IEEE 802.16d and e are the standards proposed by WiMAX group for fixed and mobile. As the wireless channel have so many limitation Such as Multipath, Doppler spread, Delay spread and Line Of Sight (LOS)/Non Line Of Sight (NLOS) components. To attain higher data rates the Multi Carrier System with Multiple Input and Multiple Output (MIMO) is incorporated in the WiMAX. The Orthogonal Frequency Division Multiplexing (OFDM) is a multi carrier technique used with the WiMAX systems. In OFDM the available spectrum is split into numerous narrow band channels of dissimilar frequencies to achieve high data rate in a multi path fading environment. And all these sub carriers are considered to be orthogonal to each other. As the number of sub carriers is increased there is no guarantee of sustained orthogonality, i.e. at some point the carriers are not
independent to each other, and hence where the orthogonality can be loosed which leads to interference and also owing to the synchronization between transmitter and receiver local oscillator, it causes interference known as Inter Carrier Interference (ICI). The systems uses MIMO-OFDM will suffer with the effects of ICI and Carrier Frequency Offset (CFO) “ε”. However these affect the power leakage in the midst of sub carriers, consequently degrading the system performance. In this paper a new approach is proposed in order to reduce the ICI caused in WiMAX and improve the system performance. In this scheme at the transmitter side the modulated data and a few predefined pilot symbols are mapped onto the non
neighboring sub carriers with weighting coefficients of +1 and -1. With the aid of pilot symbols the frequency offset is exactly estimated by using Maximum Likelihood Estimation (MLE) and hence can be minimized. At demodulation stage the received signals are linearly combined along with their weighted
coefficients and pilot symbols, called as Pilot Aided Self Cancellation Method (PASCS). And also to realize the various wireless environments the simulations are carried out on Stanford University Interim (SUI) channels. The simulation results shows that by incorporating this method into WiMAX systems it performs better when the Line Of Sight (LOS) component is present in the transmission and also it improves the Bit Error Rate (BER) and Carrier to Interference Ratio (CIR). The CIR can be improved 20 dB. In this paper the effectiveness of PASCS scheme is compared with the Self Cancellation Method (SCM). It provides accurate estimation of frequency offset and when residual CFO is less significant the ICI can be diminished successfully.
OPTIMUM EFFICIENT MOBILITY MANAGEMENT SCHEME FOR IPv6 ijngnjournal
Mobile IPv6 (MIPv6) and Hierarchical Mobile IPv6 (HMIPv6) both are the mobility management solutions proposed by the Internet Engineering Task Force (IETF) to support IP Mobility. It’s been an important issue, that upon certain condition, out of MIPv6 and HMIPv6 which one is better. In this paper an Optimum Efficient Mobility Management (OEMM) scheme is described on the basis of analytical model which shows that OEMM Scheme is better in terms of performance and applicability of MIPv6 and HMIPv6. It shows that which one is better alternative between MIPv6 and HMIPv6 and if HMIPv6 is adopted it chooses the best Mobility Anchor Point (MAP). Finally it is illustrated that OEMM scheme is
better than that of MIPv6 and HMIPv6.
INVESTIGATION OF UTRA FDD DATA AND CONTROL CHANNELS IN THE PRESENCE OF NOISE ...ijngnjournal
In this paper, the main aim is to design and simulate UTRA FDD control channel in the presence of noise and wireless channel by using FDD library/Matlab box set that can be used to design and implement some
systems. Moreover, a test and verification of the library is achieved with different channel models such as Additive White Gaussian Noise (AWGN), fading and moving channel models. FDD library are employed to design whole transmitter and receiver. Then we had tested AWGN channel and some other channel models.
Also we illustrated what are control channels DCCH and the other one as understanding the whole system. Moreover, the standards have been covered as well as implemented the whole transmit and receive chain plus the generation of DPCH, DPCCH channel. we had tested the performance against the AWGN noise.
Then we have studied different channel models that are defined in the standard, used the few of them like the fading channel and moving channel. We have tried to compare the performance in terms of Monte Carlo simulation by producing the BER curves. We have also change some channel parameters like phase, number of multipaths and we have tried to see the performance of the model in the presence of actual channel model.
TOWARDS FUTURE 4G MOBILE NETWORKS: A REAL-WORLD IMS TESTBEDijngnjournal
In the near future, current mobile communication networks will converge towards an All-IP network in order to provide richer applications, stronger customer satisfaction, andfurther return on investment for the industry. However, such a convergence induces a strong level of complexity when handling interoperability between different operators and different handset vendors. In this context, the 3GPP consortium is working on the standardization of the convergence, and IMS is emerging as the internationally agreed upon standard that is multi-operator and multi-vendor. In this paper, we shed further light on the subtleties of IMS, and we delineate a blueprint for the implementation of a real-world
IMS testbed. An open source Presence Server is deployed as well. The operation of the IMS testbed and the Presence Server are checked to assess their conformance with 3GPP standards. A simple third party application is developed on top the IMS testbed to further assess its operation.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
In our second session, we shall learn all about the main features and fundamentals of UiPath Studio that enable us to use the building blocks for any automation project.
📕 Detailed agenda:
Variables and Datatypes
Workflow Layouts
Arguments
Control Flows and Loops
Conditional Statements
💻 Extra training through UiPath Academy:
Variables, Constants, and Arguments in Studio
Control Flow in Studio
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
Must Know Postgres Extension for DBA and Developer during MigrationMydbops
Mydbops Opensource Database Meetup 16
Topic: Must-Know PostgreSQL Extensions for Developers and DBAs During Migration
Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
Abstract: Discover how PostgreSQL extensions can be your secret weapon! This talk explores how key extensions enhance database capabilities and streamline the migration process for users moving from other relational databases like Oracle.
Key Takeaways:
* Learn about crucial extensions like oracle_fdw, pgtt, and pg_audit that ease migration complexities.
* Gain valuable strategies for implementing these extensions in PostgreSQL to achieve license freedom.
* Discover how these key extensions can empower both developers and DBAs during the migration process.
* Don't miss this chance to gain practical knowledge from an industry expert and stay updated on the latest open-source database trends.
Mydbops Managed Services specializes in taking the pain out of database management while optimizing performance. Since 2015, we have been providing top-notch support and assistance for the top three open-source databases: MySQL, MongoDB, and PostgreSQL.
Our team offers a wide range of services, including assistance, support, consulting, 24/7 operations, and expertise in all relevant technologies. We help organizations improve their database's performance, scalability, efficiency, and availability.
Contact us: info@mydbops.com
Visit: https://www.mydbops.com/
Follow us on LinkedIn: https://in.linkedin.com/company/mydbops
For more details and updates, please follow up the below links.
Meetup Page : https://www.meetup.com/mydbops-databa...
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Blogs: https://www.mydbops.com/blog/
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Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
📕 Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
"Choosing proper type of scaling", Olena SyrotaFwdays
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A cognitive radio and dynamic spectrum access – a study
1. International Journal of Next-Generation Networks (IJNGN) Vol.6, No.1, March 2014
DOI : 10.5121/ijngn.2014.6104 43
Cognitive Radio And Dynamic Spectrum Access –
A Study
Goutam Ghosh1
, Prasun Das2
and Subhajit Chatterjee3
1
Department of Electronics and Communication Engineering, College of Engineering and
Management,Kolaghat, West Bengal, India
2
Department of Electronics and Communication Engineering, Bengal Institute of
Technology and Management,Santiniketan, West Bengal, India
3
Department of Electronics and Communication Engineering, Swami Vivekananda
Institute of Science and Technology, Kolkata, West Bengal, India
ABSTRACT
A basic problem facing the future in wireless systems is where to find suitable spectrum bands to fulfill the
demand of future services. While all of the radio spectrum is allocated to different services, applications
and users, observation show that usage of the spectrum is actually quite low. To overcome this problem
and improve the spectrum utilization, cognitive radio concept has been evolved. Wireless communication,
in which a transmitter and receiver can detect intelligently communication channels that are in use and
those which are not in use are known as Cognitive Radio, and it can move to unused channels. This makes
possible the use of available radio frequency spectrum while minimizing interference with other users. CRs
must have the capability to learn and adapt their wireless transmission according to the surrounding radio
environment. The application of Artificial Intelligence approaches in the Cognitive Radio is very promising
since they have a great importance for the implementation of Cognitive Radio networks architecture.
Dynamic spectrum access is a promising approach to make less severe the spectrum scarcity that wireless
communications face now. It aims at reusing sparsely occupied frequency bands and does not interfere to
the actual licensees. This paper is a review and comparison of different DSA models and methods.
KEYWORDS
Wireless communication system, cognitive radio (CR), dynamic spectrum access (DSA), fuzzy logic,
markovian chain, threshold policies, spectrum sharing and spectrum management.
1. INTRODUCTION TO COGNITIVE RADIO
Cognitive radios are the radio systems that autonomously coordinate the usage of radio band.
They recognize radio spectrum when it is unused by the incumbent radio system and use this
spectrum in an intelligent way. Such unused radio spectrum is called ‘spectrum opportunity,’ also
known to as ‘white space.’[1].
The idea of cognitive radio (CR) was first presented officially in an article by Joseph Mitola III
and Gerald Q. Maguire, Jr in 1999. It was a new approach in wireless communications that Mitola
described as: “The point in which wireless personal digital assistants (PDAs) and the related
networks are sufficiently computationally intelligent about radio resources and related computer-
to-computer communications to detect user communications needs as a function of use context,
and to provide radio resources and wireless services most appropriate to those needs” [2]. This is
an intelligent wireless communication system that is cognizant of its surrounding environment
2. International Journal of Next-Generation Networks (IJNGN) Vol.6, No.1, March 2014
44
and uses a understanding methodology by building to learn from the environment, adapt its
internal states to statistical change in the incoming radio frequency stimuli by making
corresponding variation in certain operating parameters in real time and with two primary
objectives: i) highly reliable communications whenever and wherever needed ii) efficient
utilization of radio spectrum [3].
It was thought of an ideal goal towards which a software-defined radio (SDR) platform should
develop: a fully reconfigurable wireless black-box that automatically varies its communication
variables with network and user demands. [4]
Figure 1. Block diagram contrasting (a) SDR and (b) Cognitive Radio
Cognitive radio (CR) is a form of wireless communication in which a transceiver can
intelligently detect communication channels which are in use and which are not, and instantly
move into unused channels while avoiding occupied ones. This optimizes the use of available
radio-frequency spectrum while interference minimized to other users. CR technology is a
paradigm for wireless communication in which transmission or reception parameters of network
or wireless node are changed to communicate avoiding interference with licensed or unlicensed
users. A spectrum hole (Figure 2) is generally a concept which is the latent opportunities for safe
use of spectrum as non-interfering and considered as multidimensional areas within frequency,
time, and space. The main challenge for secondary radio systems is to be able to sense when they
are within such a spectrum hole [5].
.
Figure 2. Spectrum Hole (or spectrum opportunity).
RF
Hardware
RF Modulation Coding Framing Processing
Hardware Software
a) SDR
Modulation Coding Framing Processing
SoftwareIntelligence (Sense, Learn, Optimize)
b) CR
3. International Journal of Next-Generation Networks (IJNGN) Vol.6, No.1, March 2014
45
There are two types of cognitive radio, i) full cognitive radio and ii) spectrum-sensing cognitive
radio. Full CR considers all parameters, a wireless node or network can be aware of every
possible parameter observable by a wireless node or network is considered. Spectrum-sensing
cognitive radio detects the channels in the radio frequency spectrum and considers radio
frequency spectrum. The requirements of the performances for cognitive radio system are: i)
authentic spectrum hole and detection of primary user, ii) precise link estimation between nodes,
iii) fast and accurate frequency control and iv) method of power control that assures reliable
communication between cognitive radio terminals and non-interference to the primary users [3].
There are two main characteristics of the cognitive radio and can be defined
Cognitive capability: The ability of the radio technology is to capture or sense the
information from its radio environment. [6].
Reconfigurability: Spectrum awareness is provided by the cognitive capability whereas
the radio to be dynamically programmed according to the radio environment are enabled
by the reconfigurability. [6].
1.1. Major Functions of Cognitive Radio
1.1.1. Spectrum Sensing
The first step is spectrum sensing determines if a primary user is present on a band.
The spectrum, the cognitive radio can share the result of its detection with other
cognitive radios after sensing [7].
The goal of spectrum sensing is to determine spectrum status and the licensed user’s activity by
periodically sensing the target frequency band. In particular, a cognitive radio transceiver detects
a spectrum which is unused or spectrum hole (i.e. band, location, and time) and also determines
the accessing method of it (i.e. transmitting power and access duration) without interfering of a
licensed user’s transmission. Spectrum sensing may be either centralized or distributed. In the
centralized spectrum sensing, a sensing controller (e.g. access point or base station) senses the
target frequency band, and the information obtained is shared with other nodes in the system. For
example, the sensing controller may be unable to detect an unlicensed user at the edge of the cell.
In distributed spectrum sharing, unlicensed users sense the spectrum independently, and the
spectrum sensing is achieved either used by individual cognitive radios (non-cooperative sensing)
or shared with other users (cooperative sensing). Even though cooperative sensing deals with a
communication and processing overhead, the accuracy of spectrum sensing is greater than that of
non-cooperative sensing [8]. So spectrum sensing techniques may be classified into three
categories: Transmitter detection, Cooperative detection and Interference based detection [9].
1.1.2. Spectrum Management
In order to meet the communication requirements of the users spectrum management captures the
best available frequencies. The CRs should decide the best band of the spectrum in order to meet
the Quality of Service (QoS) desires all available frequency bands; therefore, the functions of the
spectrum management are important for the CRs. These management functions can be classified
as follows [10].
a. Spectrum analysis
The sensing of spectrum results is analyzed to estimate the spectrum standard. Here one issue is
how to measure the quality of spectrum accessed by a SU. This quality can be characterized by
4. International Journal of Next-Generation Networks (IJNGN) Vol.6, No.1, March 2014
46
the Signal to Noise Ratio (SNR), the average correlation and the white spaces availability.
Information on the available spectrum quality for a Cognitive Radio user can be inaccurate and
noisy.
b. Spectrum Decision
Spectrum access requires a decision model. The complexity of this model is depended on the
parameters considered in the spectrum analysis.
The decision model becomes more complex when a Secondary User has multiple objectives. For
example, a SU may want to maximize performance while minimizing disturbance caused to the
PU. A stochastic optimization method is an interesting tool to model and solve the problem of
spectrum access in a Cognitive Radio. When users (both primary and secondary) are in the
system, preference will influence the decision of the spectrum access. These users can be
cooperative or non-cooperative in spectrum access. Each user has its own purpose in a non-
cooperative environment. In a cooperative one, all users can work together to achieve the goal.
In a cooperative environment, CRs cooperate with each other, make a decision for
accessing the spectrum and maximizing the objective function considering the common
constraints. In such a scenario, a central controller coordinates the spectrum management. [10].
1.1.3. Spectrum Mobility
Spectrum mobility is a function related to the variation of operating frequency band of CR users.
When a licensed user begins to access a radio channel which is currently being used by an
unlicensed user, the unlicensed user can change idle spectrum to a active spectrum band. This
change in operating frequency band is known as spectrum handoff. The protocol parameters at the
different layers in the protocol stacks have to be adjusted to match the new operating frequency
band during spectrum handoff. Spectrum handoff must try to ensure that the unlicensed user can
continue the data transmission in the new spectrum band [8].
1.1.4. Spectrum Sharing
Since there is number of secondary users want to use available spectrum holes, cognitive radio
has to maintain balance between its self-goal of information transferring efficiently and selfless
goal to share the available spectrum with other cognitive and non-cognitive users. This is done by
policy rules determining behaviour of cognitive radio in radio environment [3]. The fair spectrum
scheduling method, open spectrum usage in the spectrum sharing is one of the major challenges.
In existing systems, it regards to be similar to generic media access control MAC problems [11]
2. DYNAMIC SPECTRUM ACCESS
The concept of dynamic spectrum access is t h e identification of spectrum holes (a
frequency band which is free enough to be used) or white spaces and uses them to
communicate [7].
Dynamic spectrum access is the most vital application of cognitive radios. The PU bands are
opportunistically accessed by the SU networks such that the interference caused to the PUs is
negligible. Fig.3 shows the scenario for dynamic spectrum access (DSA) where multiple PUs and
SUs are coexisting [12].
5. International Journal of Next-Generation Networks (IJNGN) Vol.6, No.1, March 2014
47
Figure 3. Coexistence of multiple primary and secondary user networks
(homogeneous or heterogeneous)
This is a technique by which a radio system adapts to available spectrum holes with limited
spectrum use rights dynamically, in response to changing circumstances and objectives: the
created interference changes the radio’s state in environmental constraints [13]. The main task of
DSA is to overcome two types of interference: i) harmful interference caused by device
malfunctioning and ii) harmful interference caused by malicious user [9].
There are three main functions in Dynamic Spectrum Access [12]: i) spectrum awareness, ii)
cognitive processing, and iii) spectrum access.
Spectrum awareness creates awareness about the Radio Frequency environment when spectrum
access provides the ways to use the available spectrum opportunities for reuse efficiently.
Cognitive processing is the intelligence and decision making function that performs several
subtasks like learning of the radio environment, designing sensing efficient, and access policies
which manages interference for coexistence of the SU networks with the PU networks.
3. DIFFERENT MODELS AND SCHEMES OF DSA
Based on the fixed allocation of the radio resources and little sharing of radio spectrum which
causes in spectrum shortages, the current spectrum management policy is made. In comparison
to the static spectrum access, dynamic spectrum access (DSA) is widely used in cognitive
network and having various approaches and applications [14].
3.1. Different Approaches of DSA Models
As shown in Figure 4 dynamic spectrum access strategies can be classified as dynamic exclusive-
use, open sharing model, and hierarchical access model.
International Journal of Next-Generation Networks (IJNGN) Vol.6, No.1, March 2014
47
Figure 3. Coexistence of multiple primary and secondary user networks
(homogeneous or heterogeneous)
This is a technique by which a radio system adapts to available spectrum holes with limited
spectrum use rights dynamically, in response to changing circumstances and objectives: the
created interference changes the radio’s state in environmental constraints [13]. The main task of
DSA is to overcome two types of interference: i) harmful interference caused by device
malfunctioning and ii) harmful interference caused by malicious user [9].
There are three main functions in Dynamic Spectrum Access [12]: i) spectrum awareness, ii)
cognitive processing, and iii) spectrum access.
Spectrum awareness creates awareness about the Radio Frequency environment when spectrum
access provides the ways to use the available spectrum opportunities for reuse efficiently.
Cognitive processing is the intelligence and decision making function that performs several
subtasks like learning of the radio environment, designing sensing efficient, and access policies
which manages interference for coexistence of the SU networks with the PU networks.
3. DIFFERENT MODELS AND SCHEMES OF DSA
Based on the fixed allocation of the radio resources and little sharing of radio spectrum which
causes in spectrum shortages, the current spectrum management policy is made. In comparison
to the static spectrum access, dynamic spectrum access (DSA) is widely used in cognitive
network and having various approaches and applications [14].
3.1. Different Approaches of DSA Models
As shown in Figure 4 dynamic spectrum access strategies can be classified as dynamic exclusive-
use, open sharing model, and hierarchical access model.
International Journal of Next-Generation Networks (IJNGN) Vol.6, No.1, March 2014
47
Figure 3. Coexistence of multiple primary and secondary user networks
(homogeneous or heterogeneous)
This is a technique by which a radio system adapts to available spectrum holes with limited
spectrum use rights dynamically, in response to changing circumstances and objectives: the
created interference changes the radio’s state in environmental constraints [13]. The main task of
DSA is to overcome two types of interference: i) harmful interference caused by device
malfunctioning and ii) harmful interference caused by malicious user [9].
There are three main functions in Dynamic Spectrum Access [12]: i) spectrum awareness, ii)
cognitive processing, and iii) spectrum access.
Spectrum awareness creates awareness about the Radio Frequency environment when spectrum
access provides the ways to use the available spectrum opportunities for reuse efficiently.
Cognitive processing is the intelligence and decision making function that performs several
subtasks like learning of the radio environment, designing sensing efficient, and access policies
which manages interference for coexistence of the SU networks with the PU networks.
3. DIFFERENT MODELS AND SCHEMES OF DSA
Based on the fixed allocation of the radio resources and little sharing of radio spectrum which
causes in spectrum shortages, the current spectrum management policy is made. In comparison
to the static spectrum access, dynamic spectrum access (DSA) is widely used in cognitive
network and having various approaches and applications [14].
3.1. Different Approaches of DSA Models
As shown in Figure 4 dynamic spectrum access strategies can be classified as dynamic exclusive-
use, open sharing model, and hierarchical access model.
6. International Journal of Next-Generation Networks (IJNGN) Vol.6, No.1, March 2014
48
Figure 4. Dynamic Spectrum Access Models
3.1.1. Dynamic Exclusive Use model
The basic structure of the current spectrum regulation policy are maintained in this model:
Spectrum bands are licensed to services for exclusive use. The main concept is to improve
spectrum efficiency by introducing flexibility. Two approaches have been considered under this
model [5]: i) Spectrum property rights and ii) dynamic spectrum allocation. Spectrum property
rights approach allows licensees to sell and trade spectrum and to choose technology freely.
Therefore, economy and market will play a more important role with the most profitable use of
this limited resource.
Dynamic spectrum allocation approach aims to improve the efficiency of spectrum through
dynamic spectrum assignment by using the spatial and temporal traffic statistics of different
services i.e., spectrum is allocated to services for exclusive use in a given region and at a given
time.
3.1.2. Open Sharing Model
Open sharing model is also called spectrum commons model. In spectrum commons model, every
user has equal rights to use the spectrum. This is also known as open spectrum model, has been
successfully applied for wireless services which operates in the unlicensed industrial scientific
and medical (ISM) radio band (e.g., WLAN). Open sharing among users as the foundation for
managing a spectral region used by this model [12]. There are three types of spectrum commons
model [9]: i) Uncontrolled- commons, ii) Managed-commons and iii) Private-commons.
i) Uncontrolled-commons: When a spectrum band is managed and uses the
uncontrolled commons model, no entity has exclusive license to the spectrum band.
ii) Managed-commons: Managed-commons represent an effort to avoid the tragedy of
commons by imposing a limited form of structure of spectrum access. This is a
resource which is owned or controlled by a group of individuals or entities and it is
characterized by restrictions on when and how the resource is used.
Dynamic
Spectrum
Access
Open
Sharing
Model
Exclusive
Use
Model
Hierarchical
Access
Model
Spectrum
Overlay
(OSA)
Spectrum
Underlay
(UWB)
Inter-
Weave
Dynamic
Spectrum
Allocation
Spectrum
Property
Rights
COGNITIVE RADIO
7. International Journal of Next-Generation Networks (IJNGN) Vol.6, No.1, March 2014
49
iii) Private-commons: The concept of Private Commons was introduced by FCC in its
Second Report on the elimination of barriers to development of Secondary markets
for spectrum [14]. This concept grew on allowing use of advanced technologies
which enable multiple users to access the spectrum.
3.1.3. Hierarchical Access Model
In hierarchical access model, SUs use the primary resources such that the interference to the PU is
limited. There are three approaches under this model [15]: Inter-weave, Underlay and Overlay.
Inter-Weave: The inter-weave model is based on the idea of on opportunistic re-use the spectrum
in the spatial domain i.e., the primary spectrum is utilized by CRs in the geographical areas where
primary activity is absent. Exploitation of the so called “spatial spectrum holes” is attracting an
interest, since many current licensed systems like, e.g., TV broadcasting and cellular systems.
Figure 5 [15] shows where “CR 1” can ideally serve some of the SUs because no PU activity is
present in its proximity.
Figure 5. Exemplification of spectrum opportunities.
Underlay: Underlay technologies operate in the used spectrum at a very low power level for
other licensed or license exempt uses but does not impair the users. Underlay use is not licensed
[16]. Underlay access ideated CRs to operate below the noise floor of the PUs, involving an
undercurrent of Cognitive Radio communications without PUs being aware of.
Overlay: An overlay approach allows higher powers that could result interference to existing
users but overcomes this possibility by only permitting transmissions at times or areas where the
spectrum is currently unused [16].
8. International Journal of Next-Generation Networks (IJNGN) Vol.6, No.1, March 2014
50
Figure 6. Spectrum overlay and underlay approaches
Secondary CR user can transmit with a high transmitting power to increase their rates for
giving spectrum opportunities in spectrum overlay approach however they have to find the
idle frequency bands which are unused by PUs. Similarly, in spectrum underlay approach, the
SUs do not need to find the spectrum opportunities and can transmit at the same time
coexisting with primary users however they are not permitted to transmit with high transmitting
power even if the entire RF band is idle (entire RF is not used by primary users). Therefore
overlay is known as interference model where underlay is known as interference avoidance
model [9].
4. METHODS OF DYNAMIC SPECTRUM ACCESS
There are many methods of DSA which work based on these models and discussed below.
4.1. Game Theoretic Approach
Game Theory can be explained as a mathematical framework consisting models and techniques
used to analyze the iterative decisions behavior of individual’s interest about their own benefit.
This is a mathematical tool that is analyzed and planned the interaction among the multiple
decision makers. Three major components are there in the following form
G=<N, A, { ui } > [17].
i. Decision Makers (N): Each game is considered to have a finite number of decision
makers or players N.
ii. Action Space (A): Every player ‘i’ has its own action space (Ai) which is the set of
actions including all possible actions that player can choose. The total action space ‘A’ is
calculated by multiplying all action sets [17].
A = A1 × A2 × A3 × …. × AN (4.1.1)
iii. Utility Set (U): this is a set consisting utility or payoff functions for all players [17].
U = { U1, U2, U2,……UN } (4.1.2)
The games are generally divided into two types, cooperative games and competitive games [5],
International Journal of Next-Generation Networks (IJNGN) Vol.6, No.1, March 2014
50
Figure 6. Spectrum overlay and underlay approaches
Secondary CR user can transmit with a high transmitting power to increase their rates for
giving spectrum opportunities in spectrum overlay approach however they have to find the
idle frequency bands which are unused by PUs. Similarly, in spectrum underlay approach, the
SUs do not need to find the spectrum opportunities and can transmit at the same time
coexisting with primary users however they are not permitted to transmit with high transmitting
power even if the entire RF band is idle (entire RF is not used by primary users). Therefore
overlay is known as interference model where underlay is known as interference avoidance
model [9].
4. METHODS OF DYNAMIC SPECTRUM ACCESS
There are many methods of DSA which work based on these models and discussed below.
4.1. Game Theoretic Approach
Game Theory can be explained as a mathematical framework consisting models and techniques
used to analyze the iterative decisions behavior of individual’s interest about their own benefit.
This is a mathematical tool that is analyzed and planned the interaction among the multiple
decision makers. Three major components are there in the following form
G=<N, A, { ui } > [17].
i. Decision Makers (N): Each game is considered to have a finite number of decision
makers or players N.
ii. Action Space (A): Every player ‘i’ has its own action space (Ai) which is the set of
actions including all possible actions that player can choose. The total action space ‘A’ is
calculated by multiplying all action sets [17].
A = A1 × A2 × A3 × …. × AN (4.1.1)
iii. Utility Set (U): this is a set consisting utility or payoff functions for all players [17].
U = { U1, U2, U2,……UN } (4.1.2)
The games are generally divided into two types, cooperative games and competitive games [5],
International Journal of Next-Generation Networks (IJNGN) Vol.6, No.1, March 2014
50
Figure 6. Spectrum overlay and underlay approaches
Secondary CR user can transmit with a high transmitting power to increase their rates for
giving spectrum opportunities in spectrum overlay approach however they have to find the
idle frequency bands which are unused by PUs. Similarly, in spectrum underlay approach, the
SUs do not need to find the spectrum opportunities and can transmit at the same time
coexisting with primary users however they are not permitted to transmit with high transmitting
power even if the entire RF band is idle (entire RF is not used by primary users). Therefore
overlay is known as interference model where underlay is known as interference avoidance
model [9].
4. METHODS OF DYNAMIC SPECTRUM ACCESS
There are many methods of DSA which work based on these models and discussed below.
4.1. Game Theoretic Approach
Game Theory can be explained as a mathematical framework consisting models and techniques
used to analyze the iterative decisions behavior of individual’s interest about their own benefit.
This is a mathematical tool that is analyzed and planned the interaction among the multiple
decision makers. Three major components are there in the following form
G=<N, A, { ui } > [17].
i. Decision Makers (N): Each game is considered to have a finite number of decision
makers or players N.
ii. Action Space (A): Every player ‘i’ has its own action space (Ai) which is the set of
actions including all possible actions that player can choose. The total action space ‘A’ is
calculated by multiplying all action sets [17].
A = A1 × A2 × A3 × …. × AN (4.1.1)
iii. Utility Set (U): this is a set consisting utility or payoff functions for all players [17].
U = { U1, U2, U2,……UN } (4.1.2)
The games are generally divided into two types, cooperative games and competitive games [5],
9. International Journal of Next-Generation Networks (IJNGN) Vol.6, No.1, March 2014
51
1) Cooperative Games: all players are interested about all the overall benefits, not very
worried about their own personal benefit. Few recent works in Cognitive Radio uses
cooperative game theory to reduce transmitting power of SUs to avoid generating
interference to PU transmissions. The well known property of game-theoretical
approaches is called Nash Equilibrium (NE). In NE, each player is considered to know
the equilibrium strategies of the other players, and none has anything to gain by changing
strategy [10]. Each rational network users only cares about own benefit and chooses
the optimal strategy which can maximize his/her pay off function and such
outcome is termed as Nash Equilibrium in non-cooperative spectrum sharing game [9].
2) Competitive Games: Each user is mainly concerned about his personal payoff, therefore
all decisions are made competitively and selfishly. These concepts have been
extensively used in spectrum allocations for Cognitive Radio networks where the
Primary User and Secondary User participating in a game. To choose strategies that
maximize their individual payoffs they behave rationally [10].
4.2. A Measurement-based Model for Dynamic Spectrum Access
In [18], A Measurement-based model is considered a Semi-Markov model which captures the
behavior of WLAN so good enough to be used for obtaining optimal control strategies within a
decision frame work and it is based on actual measurements in the 2.4GHZ ISM band using a
vector signal analyzer to get complex base band data. A setup is shown figure 7 [9].
Figure 7. Measurement Setup
Different from existing publication a commercial WLAN adapter card is used to obtain packet
trace but here a vector signal analyzer (VSA) is used to capture the raw complex baseband data.
These data have to identify busy and idle periods of the channel.
There is a wireless router (Netgear WGT624) and three computers with wireless adapter cards
(two Netgear WG311T and one WG511T) in the WLAN [9].
After capturing the transmission of WLAN, vector signal analyzer collects the complex data base
band samples and it internally down converts 2.462GHz to an internal Intermediate Frequency at
Access Point
Netgear WGT624
PC3
WG511T
PC2
WG311T
PC1
WG311T
802.11b WLAN
Down
Converter
I/Q Data
Agilent 89640A
VSA
TS= 1/44
MHz
2.462GHz
10. International Journal of Next-Generation Networks (IJNGN) Vol.6, No.1, March 2014
52
a sample rate of 44MHZ. Continuous-time Semi-Markov process allows an arbitrary specification
of temporary time distribution in each state. A Semi-Markov process is a stochastic process
whose behavior of transition is characterized in two steps.
i) the transition between states follow a markov chain and defined by transition matrix in
Equation 4.2.1, where Pij denoted the probability that transition occurs from state ‘i’ to state ‘j’.
=
⋯
⋮ ⋱ ⋮
⋯
( 4.2.1)
ii) given that the system is in state ‘i’ and will transition to state ‘j’, the sojourn time t in state ‘i’
is distributed according to cumulative distribution function Qij (t). The estimator in Equation 4.2.2
= (4.2.2)
In which the transition count nij is the number of transitions ‘i’ →‘j’ occurring in our
observation Sequence. Similarly
= (4.2.3)
Where ni the number of times that the system resides in state i.
The state sequences DATA→SIFS→ACK are deterministic (the corresponding transition
probabilities are very close to one) in high SNR transmission between the nodes and no hidden
terminals are there. Therefore, it is possible to make easy the model by putting these states
together. While this model prevents occurrence of collisions, retain good accuracy because
collisions are infrequent. The Markov model is as shown in Figure 8.
Figure 8. Proposed Markov model. The lumped model (with deterministic DATA→SIFS→ACK transitions
is shown on the right).
In Figure 8 ‘transmit’ state (a lumped version of DATA, SIFS, and ACK), and an idle state is
shown. The transition probabilities of this simplified semi Markov model is not now worth
considering, because an idle period Continuous time Semi-Markov model follows every
transmit state and captures the idle periods remaining between the burst transmissions of the
wireless LAN. This model exhibits a good compromise between accuracy and computation
complexity.
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4.3. Dynamic Spectrum Access Using a Network Coded Cognitive Control Channel
Network coding is introduced for data dissemination in wireless networks to increase throughput,
robustness and decrease delay. Store, code and forward technique are provided in network coding
where each node stores all the incoming packets in an internal buffer and successively sends their
linear combinations, where combining is performed over some finite Galois Field. For example, n
packets, a node must collect at least n independent combinations of the original packets. A high
throughput gains in multicast or broadcast networks are provided in this way [19].
Dynamic Spectrum Access scheme allows the users opportunistically access the channels
available for communications efficiently. There are four important aspects of opportunistic
spectrum access: 1) implementation of the control channel, 2) multi-channel medium access
control, 3) primary user detection, and 4) secondary reuse of spectrum unused by primary users
[20]. All secondary users visit all channels in a pseudo random fashion and exchange control
information whenever they come across in any channel.
A resource allocation algorithm run independently by each user, transmission opportunities only
on free channels are assigned. This method is completely distributed and it does not need
allocated spectrum resources for control purposes but rather leverages of the virtual control
channel which is carrying out network coding techniques and exploit a cooperative detection
strategy to identify unused spectrum. This results a degradation of the spreading performance of
network coded cognitive control channel-DSA with respect to NC4-MAC.
The Most important aspects of the extended version NC4-DSA are the following [20]:
i. In each allocation period, primary user detects and tracks the varying pattern of primary
user’s activity over all channels.
ii. Each user gathers the detection information during an allocation period is to be sent to all
users using the control channel.
iii. Cooperative detection is performed independently by each user using the same
deterministic algorithm; so, all users will be able to infer the same set of free channels
which are correctly decoded the control information.
iv. The resource allocation algorithm assigns transmission opportunities only on free
channels which is run independently by each user and some users will still be instructed
to switch to busy channels for primary user detection purposes.
The performance evaluation of NC4-DSA with respect to primary user detection, interference
performance, the spectrum reuse efficiency of the secondary users and good put in dynamic
spectrum access are shown in Figure 9, Figure 10, Figure 11, and Figure12.
In figure 9 the value of the signal to noise ratio of the primary user is γ Є {0,5} dB. The
normal probability scale is used for both axes; cooperative detection strategy allows to
achieve significant improvements in the achievable tradeoff between primary user detection
and false alarm probability. The detection improves with increasing Ns and S.
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Figure 9. Primary user detection (λ - energy threshold parameter, Q- detection count threshold)
In [19] the performance for secondary spectrum access is shown by the ”CD” curves in Figure 10
and Figure 11, where we plot respectively Pinterf and Preuse as a function of the mean activity
duration ℓ1 = 1/β and inactivity duration ℓ0 = 1/α of the PU respectively, for a example with C =
10, π1 = 0.7 and λCD = 5 dB. For each combination of N and γ a value of the threshold count
parameter Q was chosen to yield a good tradeoff between Pcd and Pcf ; the chosen value is seen in
the figure
Figure 10. Probability of interference to the PU caused by CRs.
Figure 10 shows that Pinterf decreases when ℓ1 increases, since the longer activation period of the
PU allows CRs to detect it and avoid interfering with it and for most values of N and γ, the
performance obtained by the cooperative secondary spectrum access scheme is very close to the
limit performance of Pinterf and Preuse.
Figure 11. Efficiency of secondary reuse of spectrum unused by the PUs.
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Figure 12 shows that also the probability of successfully reusing unused spectrum depends on the
mean duration of the inactive period of the PU. Secondary spectrum access scheme is more
successful when the PU stays idle for longer periods. Again, the performance is best possible
when γ = 5 dB, and is in general rather close to the performance limit, with the exception of the
case N = 10 and γ = 0 dB which suffers from weak performance of both single detection and CD.
Therefore, both Pinterf and Preuse have a very weak dependence on the steady state activation
probability π1 of the Primary User.
Figure 12. Goodput for secondary spectrum access
The Good put performance of NC4-DSA for both the original “cooperative detection only”
version and the “cooperative + LBT” variant. The performance is shown in Figure 11, function
of the primary user activity π1 for C = 10, γ= 5 dB, λCD= 5 dB, ℓ1= 35, Tall/Tctrl= 600, and λLBT =3
dB (when LBT is used). The effect on the detection of the PUs and the reuse efficiency of
unused spectrum on the overall good put is limited, since detection works almost perfectly
with the value of S (slot) that is practical for the dissemination of control information in the
primary / secondary description; this effect does not increase in π1[9].
4.4. Fuzzy Logic Based System
Fuzzy logic provides a way to get the solution to a problem based on inaccurate, noisy,
and incomplete information. Fuzzy logic uses a set of fuzzy membership functions and indirect
rules to obtain the solution that meets objectives desirable. There are three important parts in a
system of control of fuzzy logic: 1) fuzzifier, 2) fuzzy logic processor and 3) defuzzifier. The
fuzzifier is used to plot the actual inputs by making them fuzzy, the fuzzy logic processor
provides an inference engine to get a solution based on sets of predefined rules, and the
defuzzifier is applied to convert the solution to real output [10].
Fuzzy logic is a multi-valued logic. Many input parameters are used to take the decision. Here
distance, signal strength, velocity and spectrum efficiency are known as input parameters. The
chance of taking decision is increased if the channel (offered by PU) signal is high and
distance between PU and SU is low. If the distance is small, the velocity increases the chance
of the spectrum accessing is more [9].
4.5. Spatio-Temporal Spectrum Management Model
Spectrum scarcity problem is due to the inflexibility of spectrum allocation regime, a more
suitable spectrum allocation is adapted to the spatio-temporal bandwidth demand which will
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increase the spectrum availability, solving the (inexistent) spectrum scarcity problem. Figure 13
shows Spatio-temporal dynamic spectrum access model. In this model [8], the service area is
divided into multiple regions. In the region ‘k’, network service provider ‘i’ provides wireless
services to users, and spectrum demand for this service provider is denoted by Dik. The spectrum
of a given region is owned by RSB (Regional Spectrum Broker) which grants short time licenses
for the requesters [21].
In TDSA (Temporal Dynamic Spectrum Allocation) method, the demands for spectrum to RSB
are sent by the service providers of the region. The RSB allocates continuous spectrum blocks to
the requesters and the blocks are separated by guard bands.
Figure 13. Spatio-temporal dynamic spectrum access model.
The Spectrum Dynamic Spectrum Allocation (SDSA) deals with spectrum demands coming at
the same time in different regions. The main objective of the SDSA is adjusting the different
demands within different regions on the way, where the least interference occurs in the
overlapping regions. Figure 14 and Figure 15 [9] shows the simulation results. In region 1 the
demand of NSP1 is larger than that of the region NSP2 and in region 2, it is just opposite way. The
numbers of carriers which are used by the providers in both regions as a function of time. The
excess spectrum is required to satisfy the demands in the case of overhearing, which is denoted by
a darker tone shown in the Figures.
Figure 14. Allocated spectrum sizes for both providers in region 1.
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Figure 15. Allocated spectrum sizes for both providers in region 2
4.6. Markovian Queuing Model for Dynamic Spectrum Allocation
Centralized architecture is used in Markovian Queuing model for dynamic spectrum allocation. In
centralized network the central controller of an ad-hoc network allocates bandwidth to intended
users. This CR ad-hoc network assumes to coexist with the network of licensed users where the
controller of licensed user is updated with the CR coordinating engine. A centralized network
eliminates hidden terminal problem, obtains complete database of unoccupied frequencies and
provide better coverage and efficiency spectrum handover technique. Each SU consists of
two transceivers, one is dedicated to control and another is software defined radio based.
The SDR based transceivers scan the availability of spectra in its area and forward the
information of these spectrum holes to the central controller in case Secondary User
form an infrastructure less network or to the Base Station in case of infrastructure based network.
Figure16 [22] shows the equivalent model of network queues.
Figure 16. Queuing Model for DSA in Cognitive Radio
These queues are in the special case of stochastic processes, characterized by the arrival process
of service requests, waiting list of requests which are to be processed. The queue stacking all the
entries of SUs is referred to SUQ and the entire request entering this queue are served on the first
come first serve basis. At any time when bandwidth needs to be allocated to the Secondary User,
the head considers both the request from the SU and the PU, who needs it licensed channel.
Therefore while distributing a number of frequencies for Primary User and Secondary User; the
arrival rates of both the users are added to access the frequencies with the head. This queue so
formed is known as bandwidth allocation queue (BAQ). Markov process is used to study the
Queuing model. The blocking probability PB (the probability that an SU request denied) for the
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bandwidth request made by Cognitive Radio that finds all the channels with head as occupied is
given by Erlang-B formula
≡ =
! ∑
!
(4.6.1)
Figure17 [9] shows the Blocking Probability. Variation in PB with change in number of available
channels in the system as 2, 5, 7, 10, 13, and 15. Blocking probability increases with the increase
in SU traffic in the network.
Figure 17. Blocking probabilities (PB) Against SU utilization in the system (ρ1). The Variation in PB is
depicted with different numbers of channels (S), available with the system.
5. CONCLUSIONS
Cognitive radio is a paradigm for wireless communication where transmission or reception
parameters are changed by a network or a wireless node to communicate efficiently and avoid
interference with licensed or unlicensed users. Here different dynamic spectrum access models
are discussed. Game theoretic approach is an important and one of the most authentic approaches
in studying, modelling and analyzing the Cognitive interaction process. It has been used to a large
degree for spectrum sharing and become an interesting field of research for spectrum
management in the context of Cognitive Radio. Game theory is the most acceptable technique to
obtain the equilibrium solution to the problem of the spectrum sharing, as obvious from the
discussions earlier in the paper.
A Measurement based model uses the continuous time semi Markov model which captures
the idle periods remaining between the bursty transmissions of a wireless LAN. This
model provides a good compromise between accuracy and computation complexity. In the
Markovian queuing model a centralized architecture coexisting with licensed users is proposed
for bandwidth allocation and to find blocking probability. The sensing is taken into account to be
decentralized to overcome hidden terminal problem and to get a complete database of unoccupied
frequencies. The delay performance of threshold policies is to minimize the delay of Secondary
User subject to a Primary User collision probability constraint. Spatio-Temporal spectrum
management model simplifies the problem of spectrum allocation and described architecture that
splits up the complex problems into temporal and spatial parts (TDSA and SDSA). Problem of
interference in the overlapping regions can be handled based on the information of Regional
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[22] Markovian Queuing Model for Dynamic Spectrum Allocation in Centralized Architecture for
Cognitive Radios-Prabhjot Kaur, Member, IEEE, Arun Khosla, Moin UddinSenior Member, IEEE,
IACSIT International Journal of Engineering and Technology, Vol.3, No.1, February 2011, ISSN:
1793-8236
Authors
Goutam Ghosh was born in India. He has done B.Tech in Electronics and
Communication Engineering and currently pursuing M.Tech in Electronics and
Communication Engineering from West Bengal University of Technology. He has 5
years experience in industries and 11 years in academics.
Prasun Das was born in India. He has more than 6 years experience in teaching.
Presently he is working as Assistant Professor, Department of Electronics &
Communication Engineering, Bengal Institute of Technology & Management,
Santineketan, West Bengal, India. He has some publications and his area of
interests are Image Cryptosystems, Speech Signal Modeling, Modern
Communication System Design. He has some professional memberships.
Subhajit Chatterjee was born in India. He has more than 12 years experience in
teaching starting and 3 years in industry. He has served some reputed
educational institutes in different positions from Lecturer to Teacher In Charge
of department. Presently he is working as Assistant Professor, Department of
Electronics & Communication Engineering, Swami Vivekananda Institute of Science
and Technology, Kolkata, India. He has number of publications and his area of
interests are Analog & Digital Communications, Solid State Devices,
Telecommunication, Microprocessors & Microcontrollers. He has co authored two books on
Microprocessors & Microcontrollers and Telecommunication & Switching respectively. He has
reviewed chapters of books published by reputed publishers. Presently he is doing his research on Spectrum
Access in Cognitive Radio.