This study was carried out to investigate the spectrum utilization of the licensed Radio Frequency (RF)
spectrum in Rumuokwuta, Port Harcourt. An outdoor measurement of spectrum occupancy was carried out
in a high-rise building situated at Rumuokwuta urban area in Port Harcourt, Nigeria using RF explorer
spectrum analyzer and a personal computer laptop system. Spectrum activities in the band of 240-960 MHz
were monitored for 24 hours. The frequency band was subdivided into 24 sub bands each with a span size
of 30 MHz. Scanning of bands was made efficient using a python script that scans a range, analyzed the
frequencies and signal strengths for 112 data points, saves data in CSV file format, scans the next range
until the 24 ranges were scanned. The process was repeated to achieve 15 iterations. With a noise floor of -
110dBm, a threshold of -95dBm was used to determine the presence of signal, hence the spectrum
occupancy of measured bands. Results showed that out of the 24 investigated sub bands; only one band
was completely occupied with spectrum occupancy of 100%. 12 bands were partially occupied while 11
were completely free. The average spectrum occupancy for the whole band was obtained as 11.64%. This
showed good location for dynamic spectrum access and cognitive radio deployment, especially in
Television White Space (TVWS).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
Alex Wyglinski - IEEE VTS UKRI - Cognitive radio - a panacea for RF spectrum...Keith Nolan
This document discusses cognitive radio and spectrum scarcity. It begins with an overview of the evolution of wireless technologies and the information age. It then discusses how cognitive radio aims to address the growing problem of spectrum scarcity by allowing for dynamic and flexible usage of spectrum through software-defined radios and adaptation to spectrum availability and use. The document outlines various techniques for characterizing spectrum usage and measuring availability through vehicular experiments and spectrum occupancy analysis to identify opportunities for cognitive radio to more efficiently utilize vacant spectrum.
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.
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.
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.
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.
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.
The document discusses cognitive radio and its benefits. It defines cognitive radio as a radio that is aware of its surroundings and adapts intelligently. Cognitive radio provides a framework for devices to dynamically create links by sensing the environment, evaluating options, and implementing the best waveform. This allows for improved spectrum utilization and quality of service. Some applications of cognitive radio include extending mobile networks, emergency radio systems, and multi-technology phones.
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.
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, 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.
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
Cognitive Radio : Emerging Business Toward an Efficiently Smart Era of ICTNurmaya Widuri
Cognitive Radio is a hot issue in wireless technology. This is ultimately new wave of how radio technolgy communicate through spectrum effeciency. Furthermore, this new big thing bring a new wave of future ICT business lanscape toward efficiently smart era of ICT.
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.
Cooperative tv spectrum sensing in cognitive radio BY DEEPAK PORIYADeepak Poriya
This document discusses cooperative spectrum sensing in cognitive radio networks for Wi-Fi. It describes the system model of a Wi-Fi zone with primary and secondary users. It analyzes single user and cooperative spectrum sensing using energy detection algorithms. Cooperative sensing can use OR, AND or K-out-of-N rules at a fusion center to determine primary user presence. The document derives the optimal number of secondary users for the K-out-of-N rule and numerically calculates the optimal sensing time to minimize detection error probability and maximize throughput.
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.
iaetsd Equalizing channel and power based on cognitive radio system over mult...Iaetsd Iaetsd
This document summarizes a research paper about equalizing power and channel allocation in a cognitive radio system using multiuser OFDM. It discusses how frequency spectrum is becoming scarce due to increased wireless usage, and how cognitive radio can help improve spectrum utilization by allowing unlicensed secondary users to access licensed bands opportunistically when primary users are not using them. The paper presents a system model for a cognitive radio network with one primary user and multiple secondary user pairs. It formulates the problem of allocating subcarriers and power to the secondary users while avoiding interference to the primary user.
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.
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.
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.
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
Alex Wyglinski - IEEE VTS UKRI - Cognitive radio - a panacea for RF spectrum...Keith Nolan
This document discusses cognitive radio and spectrum scarcity. It begins with an overview of the evolution of wireless technologies and the information age. It then discusses how cognitive radio aims to address the growing problem of spectrum scarcity by allowing for dynamic and flexible usage of spectrum through software-defined radios and adaptation to spectrum availability and use. The document outlines various techniques for characterizing spectrum usage and measuring availability through vehicular experiments and spectrum occupancy analysis to identify opportunities for cognitive radio to more efficiently utilize vacant spectrum.
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.
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.
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.
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.
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.
The document discusses cognitive radio and its benefits. It defines cognitive radio as a radio that is aware of its surroundings and adapts intelligently. Cognitive radio provides a framework for devices to dynamically create links by sensing the environment, evaluating options, and implementing the best waveform. This allows for improved spectrum utilization and quality of service. Some applications of cognitive radio include extending mobile networks, emergency radio systems, and multi-technology phones.
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.
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, 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.
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
Cognitive Radio : Emerging Business Toward an Efficiently Smart Era of ICTNurmaya Widuri
Cognitive Radio is a hot issue in wireless technology. This is ultimately new wave of how radio technolgy communicate through spectrum effeciency. Furthermore, this new big thing bring a new wave of future ICT business lanscape toward efficiently smart era of ICT.
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.
Cooperative tv spectrum sensing in cognitive radio BY DEEPAK PORIYADeepak Poriya
This document discusses cooperative spectrum sensing in cognitive radio networks for Wi-Fi. It describes the system model of a Wi-Fi zone with primary and secondary users. It analyzes single user and cooperative spectrum sensing using energy detection algorithms. Cooperative sensing can use OR, AND or K-out-of-N rules at a fusion center to determine primary user presence. The document derives the optimal number of secondary users for the K-out-of-N rule and numerically calculates the optimal sensing time to minimize detection error probability and maximize throughput.
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.
iaetsd Equalizing channel and power based on cognitive radio system over mult...Iaetsd Iaetsd
This document summarizes a research paper about equalizing power and channel allocation in a cognitive radio system using multiuser OFDM. It discusses how frequency spectrum is becoming scarce due to increased wireless usage, and how cognitive radio can help improve spectrum utilization by allowing unlicensed secondary users to access licensed bands opportunistically when primary users are not using them. The paper presents a system model for a cognitive radio network with one primary user and multiple secondary user pairs. It formulates the problem of allocating subcarriers and power to the secondary users while avoiding interference to the primary user.
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.
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.
A Cognitive Radio And Dynamic Spectrum Access – A Studyjosephjonse
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.
A cognitive radio and dynamic spectrum access – a studyijngnjournal
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.
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.
IRJET- Simulating Spectrum Sensing in Cognitive Radio Network using Cyclostat...IRJET Journal
1) The document discusses simulating spectrum sensing in cognitive radio networks using cyclostationary techniques. It aims to detect spectrum holes and classify primary user signals of different modulation schemes.
2) It reviews different spectrum sensing techniques and models/simulates cyclostationary-based sensing. Cyclostationary detection exploits the periodicity in primary user signals to identify their presence and can differentiate modulated signals from noise.
3) The methodology assumes a cognitive radio network with primary and secondary users. It formulates spectrum sensing as a hypothesis test to detect the presence or absence of primary users. It then discusses representing signals using their cyclostationary properties like the cyclic autocorrelation function.
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.
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.
The document discusses cognitive radio, which aims to solve the problem of limited radio spectrum availability. Cognitive radio can detect available channels and move between them, optimizing spectrum usage. It describes three types of cognitive radio - interweave, underlay, and overlay - which differ in how secondary users transmit around primary users. Interweave detects and uses vacant spectrum holes, underlay limits interference to primary users, and overlay maintains primary communication while adding secondary usage through signal processing.
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.
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 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.
International Journal of Computational Engineering Research(IJCER) ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Investigation of TV White Space for Maximum Spectrum Utilization in a Cellula...Onyebuchi nosiri
Abstract— The shortage of spectrum resource availability in wireless communication network due to the rapid increase in the number of subscribers and multimedia applications has given rise to the need for effective spectrum utilization of the licensed spectrum. Cognitive Radio Technology (CRT) was adopted for the system analysis due to its dynamism in accommodating both licensed and unlicensed users within a particular spectrum band. The study framework comprised television station channels in Owerri and its environs and Mobile Telecommunication Networks (MTN) in Owerri- the capital of Imo State, Nigeria as the license and unlicensed users respectively. An outdoor twenty-four hour spectrum occupancy measurement was carried out in the frequency bands of the licensed networks using 240-960 MHz Radio Frequency Spectrum analyzer to determine the spectral usage of the licensed user. A threshold of -95 dB was used to determine the presence of the licensed users. From the results obtained, it was observed that 60.7% of the spectrum band covered was unoccupied, 31.5% was not fully occupied while 7.9% was fully occupied. Energy Detection spectrum approach was implemented by the unlicensed users for easy determination of the spectrum status and resource management. The research therefore determines the status of Radio Frequency (RF) Spectrum receivable in Owerri and its environs and proffer measures deployable in harnessing the unused RF resources using CRT.
Investigation of TV White Space for Maximum Spectrum Utilization in a Cellula...Onyebuchi nosiri
Abstract— The shortage of spectrum resource availability in wireless communication network due to the rapid increase in the number of subscribers and multimedia applications has given rise to the need for effective spectrum utilization of the licensed spectrum. Cognitive Radio Technology (CRT) was adopted for the system analysis due to its dynamism in accommodating both licensed and unlicensed users within a particular spectrum band. The study framework comprised television station channels in Owerri and its environs and Mobile Telecommunication Networks (MTN) in Owerri- the capital of Imo State, Nigeria as the license and unlicensed users respectively. An outdoor twenty-four hour spectrum occupancy measurement was carried out in the frequency bands of the licensed networks using 240-960 MHz Radio Frequency Spectrum analyzer to determine the spectral usage of the licensed user. A threshold of -95 dB was used to determine the presence of the licensed users. From the results obtained, it was observed that 60.7% of the spectrum band covered was unoccupied, 31.5% was not fully occupied while 7.9% was fully occupied. Energy Detection spectrum approach was implemented by the unlicensed users for easy determination of the spectrum status and resource management. The research therefore determines the status of Radio Frequency (RF) Spectrum receivable in Owerri and its environs and proffer measures deployable in harnessing the unused RF resources using CRT.
Investigation of TV White Space for Maximum Spectrum Utilization in a Cellula...Onyebuchi nosiri
Abstract— The shortage of spectrum resource availability in wireless communication network due to the rapid increase in the number of subscribers and multimedia applications has given rise to the need for effective spectrum utilization of the licensed spectrum. Cognitive Radio Technology (CRT) was adopted for the system analysis due to its dynamism in accommodating both licensed and unlicensed users within a particular spectrum band. The study framework comprised television station channels in Owerri and its environs and Mobile Telecommunication Networks (MTN) in Owerri- the capital of Imo State, Nigeria as the license and unlicensed users respectively. An outdoor twenty-four hour spectrum occupancy measurement was carried out in the frequency bands of the licensed networks using 240-960 MHz Radio Frequency Spectrum analyzer to determine the spectral usage of the licensed user. A threshold of -95 dB was used to determine the presence of the licensed users. From the results obtained, it was observed that 60.7% of the spectrum band covered was unoccupied, 31.5% was not fully occupied while 7.9% was fully occupied. Energy Detection spectrum approach was implemented by the unlicensed users for easy determination of the spectrum status and resource management. The research therefore determines the status of Radio Frequency (RF) Spectrum receivable in Owerri and its environs and proffer measures deployable in harnessing the unused RF resources using CRT.
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.
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INVESTIGATION OF RADIO FREQUENCY LICENSED SPECTRUM UTILIZATION IN NIGERIA: A STUDY OF RUMUOKWUTA, PORT HARCOURT, NIGERIA
1. International Journal of Wireless & Mobile Networks (IJWMN), Vol.13, No.5, October 2021
DOI:10.5121/ijwmn.2021.13503 31
INVESTIGATION OF RADIO FREQUENCY LICENSED
SPECTRUM UTILIZATION IN NIGERIA: A STUDY OF
RUMUOKWUTA, PORT HARCOURT, NIGERIA
Nkwachukwu Chukwuchekwa, Enwume Joshua U,
Longinus S. Ezema and Cosmas K Agubor
Department of Electrical and Electronic Engineering, School of Engineering and
Engineering Technology, Federal University of Technology, Owerri, Nigeria
ABSTRACT
This study was carried out to investigate the spectrum utilization of the licensed Radio Frequency (RF)
spectrum in Rumuokwuta, Port Harcourt. An outdoor measurement of spectrum occupancy was carried out
in a high-rise building situated at Rumuokwuta urban area in Port Harcourt, Nigeria using RF explorer
spectrum analyzer and a personal computer laptop system. Spectrum activities in the band of 240-960 MHz
were monitored for 24 hours. The frequency band was subdivided into 24 sub bands each with a span size
of 30 MHz. Scanning of bands was made efficient using a python script that scans a range, analyzed the
frequencies and signal strengths for 112 data points, saves data in CSV file format, scans the next range
until the 24 ranges were scanned. The process was repeated to achieve 15 iterations. With a noise floor of -
110dBm, a threshold of -95dBm was used to determine the presence of signal, hence the spectrum
occupancy of measured bands. Results showed that out of the 24 investigated sub bands; only one band
was completely occupied with spectrum occupancy of 100%. 12 bands were partially occupied while 11
were completely free. The average spectrum occupancy for the whole band was obtained as 11.64%. This
showed good location for dynamic spectrum access and cognitive radio deployment, especially in
Television White Space (TVWS).
KEYWORDS
Cognitive radio, Dynamic spectrum access, RF spectrum, TV white space & Wireless communication.
1. INTRODUCTION
The Radio Frequency (RF) spectrum is a natural scarce resource that requires optimum
utilization. It is that portion of electromagnetic spectrum which ranges from 3 Hz to 300 GHz that
is used for communication purposes [1]. The enormous pressure on it has caused significant
scarcity on the availability of the RF resource [2][3][4]. Spectrum occupancy measurement
carried out in various parts of the world showed underutilization of the resource [5][6][7] in the
frequency range of 2.4-2.7 GHz The locations cut across rural, suburban and urban areas. From
their investigations, rural locations have the least duty cycle of approximately 0%. This is
apparently due to very small or no wireless connectivity. The result showed absolute unoccupied
bands. The occupancy levels for suburban regions were better compared to the rural locations.
The urban locations have the highest occupancy level.
As the very first occupancy measurement campaign in Nigeria, [8] considered an insight into
spectrum occupancy in Nigeria. The objective of the study was to carry out an indoor spectrum
occupancy measurement within the region of 0.7 GHz to 2.5 GHz in Gwarimpa, Abuja, Nigeria.
2. International Journal of Wireless & Mobile Networks (IJWMN), Vol.13, No.5, October 2021
32
Each band was studied for 12 hours daily; from 9am to 9pm. An Aarona spectrum analyzer and
Omnilog 90200 antenna with a frequency range of 0.7-2.5 GHz were used in the measurement. A
decision threshold of -76dBm was used to determine the active and idle states of a licensed user.
The measurement showed that the cellular bands have the highest degree of utilization at 26%
and 25.56% for the 2G and 3G cellular standards respectively. 2-2.2 GHz band has the least
utilization level. As expected, the result showed spectrum white space that could be exploited
through cognitive radio.
The Radio Frequency (RF) spectrum is a natural scarce resource that requires optimum utilization
[1]. However, to optimize the scarce resources, spectrum sharing through adaptive power
allotment has been proposed by many authors. This is a way forward in achieving spectrum
optimization. Cognitive radio network allows primary and secondary resource user to share the
same frequency band while ensuring minimal interference [9][10][11]. The TVWS network can
be made to share the spectrum as a secondary user to provide internet services.
2. REVIEW OF LITERATURES
In recent years, spectrum management has been a major concern for researchers. This is due to
the rising demand for wireless connectivity. The astronomical growth in number of mobile
subscribers, as well as the high demand for multimedia access spurs interest in querying the
efficiency of present radio frequency management. The command and control method of
spectrum management has shown to be ineffective. Researches on spectrum management showed
that high proportion of assigned radio frequency band is unused. The spectrum usage of licensed
radio spectrum varied from 15% to 85% in the United States of America [12]. This conventional
system of spectrum management would need to be replaced by a more intelligent system that
accesses the spectrum dynamically.
Dynamic Spectrum Access (DSA) is basically the process and system of accessing spectrum
white space of a licensed user referred to as Primary User (PU) by an opportunistic user known as
the Secondary User (SU). The idea of Cognitive Radio (CR) was first presented in [13]. Mitola
analyzed CR as a radio that is aware of its environment and can adjust its parameters to respond
to environmental changes. For safe deployment of CR without harmful interference and
infringement on the right of the licensed user, it is therefore necessary to have the knowledge
about the degree of utilization of a particular band to ascertain which band(s) would be suitable
for DSA.
2.1. Traditional Spectrum Management System
Spectrum is a finite resource and therefore its continuous use depends on how efficiently
spectrum bandwidth is used within a given portion of the radio spectrum and geographical
location. Traditionally, the spectrum management system is a command and control approach
where a user has an exclusive right to operate within a certain band. This is to ensure that the
activities of spectrum users are devoid of interference. The spectrum management system has
been in existence for several years since licensing became part of spectrum resource control[14].
It has been a wide practiced system by every spectrum administrator as well as management
authorities. In this management approach, a spectrum manager outlined certain rules and
constraints that determine how, where and when spectrum can be used, as well as who has access
to the resource. In this management model, different services are sometimes allocated to different
frequency bands, although for some frequency bands, more than a radio service is allocated. The
traditional spectrum management system is a hierarchical management system with International
Telecommunication Union (ITU) at top hierarchy. ITU oversees radio spectrum management
3. International Journal of Wireless & Mobile Networks (IJWMN), Vol.13, No.5, October 2021
33
internationally and ensures compliance at the national level. The next in hierarchy is the national
planning process and down to licensing and interference management. Basically, the traditional
management system uses three managerial tools which include spectrum allocation, spectrum
assignment and interference management.
2.2. Dynamic Spectrum Management System
The limitations of the traditional fixed management system are enough to embrace a system that
would access spectrum dynamically [2]. The old spectrum management system does not consider
spectrum white space and as such renders the system ineffective. The dynamic spectrum
management system is a proposed spectral management system that relies on dynamic spectrum
access which showed the dependent of spectrum on location, time, measurement conditions, and
equipment [15]. Dynamic spectrum management system births dynamic spectrum access which is
a recent spectrum sharing paradigm in which a licensed user, otherwise known as Primary User
(PU) shares the licensed band with an opportunistic user known as the Secondary User [16][17].
With DSA, SUs search for the spectrum bands dynamically, and temporarily access them for
wireless communications. To avoid interference to PUs, SUs continuously monitor the spectrum
bands and yield to PUs whenever the latter start using the band [18].
2.3. Spectrum Sensing
Spectrum is a limited wireless communication resource, and licensed spectrum can only be used
by the paid company owners. Cognitive radio (CR) is a new model of using a licensed spectrum
when not use in an unlicensed manner [19]. The motivation for cognitive radio is to carry out
analyses of various spectrum or channel utilization to detect vacant and occupied spectrum. It
immediately switches to the vacant spectrum while avoiding the licensed occupied spectrum
thereby avoiding interference with the licensed channel. To ensure interference free, the
secondary user must keep monitoring the primary user and be able to sense weak PU signals. The
Cognitive radio should be able to switch to another available channel on sensing PU signal in
occupied channel [20].
Spectrum sensing is a key component of DSA. Sensing is so important that a slight neglect in the
process of DSA could cause huge effect to the PU. For a SU to transmit there would be need to
sense the spectral environment of the PU. Sensing ensures adequate white space before the SU
can share the spectrum band with the PU.Spectrum sensing could be implemented locally or
could be carried out via cooperative sensing. Sensing is done locally if and only if a SU senses
the spectrum environment independently and selects an unused band.
In order to improve sensing by providing accurate detection technique, as well as combating
fading, shadowing and other channel impairments, cooperative sensing has been proposed in this
study. Cooperative sensing improves accuracy in detection through cooperation among SUs,
hence the name [21].
2.4. Reviewed Work
In [22], the authors focused on evaluation of spectrum usage for GSM band in indoor and outdoor
scenarios for dynamic spectrum access. They presented the current spectrum usage of GSM band
in Pune, India. A fibre optic cable was used as an interface between a Rohde and Schwarz
spectrum analyzer and a Dell laptop computer. A 0.7-3 GHz band AOR DA 500 antenna was
used in the campaign. Four different scenarios were considered. The first scenario was an outdoor
measurement taken in suburban India. The second case scenario was an indoor measurement
carried out in outskirts of the city of Pune, India. The third and fourth scenarios were conducted
4. International Journal of Wireless & Mobile Networks (IJWMN), Vol.13, No.5, October 2021
34
in the busy places of Pune. Results and analysis showed that though there is an underutilization
for both uplink and downlink frequencies for GSM900 and GSM1800, the downlink band
occupancy is higher than uplink band occupancy.
The authors in [23] conducted spectrum investigation for TV white space In the band of 240-960
MHz. Part of the investigation were carried out in suburban Hall of Mercy, FUTO, Owerri;
Obinze; while the rest were carried out in two different locations of Oweri metropolis. With a
noise floor of -110dBm, they chose -95dBm as a suitable threshold. The measurement was done
outdoor in four different locations. The result of their investigation showed that 7.8% of the
spectrum was fully occupied, 31.5% was moderately occupied while 60.7% was unoccupied.
Obviously the result showed high percentage of unused spectral portion which will be a
promising band for cognitive radio deployment. However, their investigation lacks automation of
frequency scan, as a result; it requires manual changing of frequency range which could have
caused measurement error.
Spectrum occupancy measurement in the frequency range of 2.4-2.7 GHz in nine various
locations in Kwara State of Nigeria for 12 hours were carried out in [24]. The locations cut across
rural, suburban and urban areas. From their investigations, rural locations have the least duty
cycle of approximately 0%. This is apparently due to very small or no wireless connectivity. The
result showed absolute unoccupied bands. The occupancy levels for suburban regions were better
compared to the rural locations. The urban locations have the highest occupancy level. For duty
cycles of 0.08% and 0.95% for suburban locations and 18.56% and 22.56% for urban locations, it
is crystal clear that the radio frequency spectrum bands used in those locations are highly
underutilized. The result showed good environment for cognitive radio technology. The
measurements were done in so many locations, nevertheless, it were not done for a long period of
time.
In [25], the authors carried out a survey on the availability of TV white spaces in Eastern Nigeria.
The measurement was done in the analog television band of the UHF using a resolution
bandwidth of 20 KHz. The ambient noise of their spectrum analysis was measured as -100dBm
with a flat average noise level of - 114dBm and frequency range of 100 MHz. They investigated
the TV white space in two different locations of Eastern Nigeria. Results showed that the spectral
usage of the UHF frequency band within the study region is 32%. Again, the result, which is not
different from other campaigns, is a good location for dynamic spectrum access. While the
graphical representation of the TV white space was clearly presented, the research lacks clear
methodology as evident in the Resolution Bandwidth (RBW) of 20 KHz which does not conform
to the 100 MHz frequency range.
A new paradigm of converting radio spectrum wastage to wealth was proposed in [26]. Analysis
of spectrum occupancy verification in three different urban cities of Nigeria was carried out. The
investigation was done in the spectral range of 80-2200 MHz. The locations considered are Ado-
Ekiti, Akure and Ikeja, which are the capitals of Ekiti, Ondo and Lagos state respectively. The
three western Nigerian states were chosen due to population density, industrial activities and
socio-economic developments. Their verification showed variation in the usage of each
frequency band with location. It also showed that though a specific frequency is much more
utilized in a particular location, it is underutilized in a different location. The results generally
showed that the total spectrum occupancy for the three locations ranges from 0.08%-64.4%. The
limitation of this work is the use of very high average threshold of 70.64 in Ado-Ekiti
measurement location.
It is obvious from the aforementioned section that spectrum occupancy measurement is rich in
literature. However, there are noticeable gaps in the measurement of spectrum occupancy. None
5. International Journal of Wireless & Mobile Networks (IJWMN), Vol.13, No.5, October 2021
35
of these works considered several measurement iterations to obtain the mean (actual) value of
spectrum occupancy. Again, some of the work done has high resolution bandwidth as adjacent
frequencies could hardly be resolved. While this research work seeks to provide spectral
activities in Port Harcourt, Rivers State, Nigeria, it also aims to breach other research gaps in
spectrum occupancy measurement
This research aims to investigate for the first time the level of spectrum usage within the band of
240 MHz to 960 MHz in Rumuokwuta, Port Harcourt, Rivers State, Nigeria, using RF Explorer
and efficient python script. Unlike previous work, the spectrum activities were monitored for
24hours instead of 12hours. This is full day coverage. The Rumuokwuta area of the state was
considered because the population and the high level of commercial activities. In this area, there
are no commensurate available and low cost quality internet services to match the demand.
Therefore, result of this research will aid in the deployment of TVWS for internet provision in the
rural and suburban areas of the state.
3. METHODOLOGY
The methods adopted in this work are discussed in the following sections.
3.1. Conducting Spectrum Occupancy Measurements for Outdoor Scenario
The spectrum occupancy measurements were done outdoor in a high rise building in a residential
apartment in Rumuokwuta, Port Harcourt, Rivers State, Nigeria using RF Explorer spectrum
analyzer, python script and personal laptop computer. This site is positioned at latitude
N4o48’36” and longitude 6o55’12”. Port Harcourt, with an estimated population of 1,865,000 is
a densely populated city with various commercial activities in southern Nigeria. The location site
has direct line of sight with several telecommunication systems, sub systems and base stations.
The site was strategically chosen, being an urban setting with obvious high level of wireless
connectivity. This location being in direct line of sight with various transmitters ensures
measurements of signals that have suffered little or no form of channel impairments. The ambient
noise of the spectrum analyser was determined and a threshold value of -95dBm was gotten based
on noise floor.
For additional functionality of the RF Explorer spectrum analyzer, as well as efficient, broader,
automatic and quality display of the spectrum analysis, a Dell laptop computer was connected to
the spectrum analyzer with a USB cord as an interface. The system enables wider view of
spectrum environment, data capturing/saving, automation of tasks and additional features and
programmability. The experimental setup for data measurement and aggregation is as shown in
Fig. 1.
Fig. 1. Measurement setup
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Installed in the PC is python programming, version 3.5.0. The software is responsible for
automation. The RF explorer spectrum analyzer is designed to be configured manually, with a
maximum scanning range of 100 MHz. This limitation means a script is needed to repeatedly
scan the spectrum from 240 to 960 MHz for 24 hours.The 24 frequency bands analyzed in this
work are 240-270MHz, 270-300MHz, 300-330MHz, 330-360MHz, 360-390MHz, 390-420MHz,
420-450MHz, 450-480MHz, 480-510MHz, 510-540MHz, 540-570MHz, 570-600MHz, 600-
630MHz, 630-660MHz, 660-690MHz, 690-720MHz, 720-750MHz, 750-780MHz, 780-810MHz,
810-840MHz, 840-870MHz, 870-900MHz, 900-930MHz and 930-960MHz.
The step by step procedure or the algorithm designed to perform scanning, analysis and data
aggregation is outlined below.
1. Import modules from python 3.5.0 library.
2. Initialize global and local variables.
3. While WholeDay < (Iteration×24); continue; else; Go To 26.
4. Define RF Explorer object and sets its properties.
5. Connect system to module using RFEObj.ConnectPort(COMPort,DataRate).
6. Reset RF Explorer using RFEObj.SendCommand(“r”).
7. Wait for reset notification.
8. Wait for unit to stabilize using time.Sleep().
9. Send a request command for RF Explorer configuration.
10. Wait for the arrival of configuration and model details.
11. Process received configuration using RFEObj.ProcessReceivedString(True).
12. Update device configuration with new start, stop and span frequencies
13. While (StopFreq <= MaxStopFreq and StartFreq < StopFreq); continue;
14. Process all received from RF Explorer spectrum analyzer.
15. Save data to appropriate CSV file for a new sweep and start frequencies.
16. Set new range of frequencies.
17. If (StopFreq > MaxStopFreq); StopFreq = MaxStopFreq;
18. If (StartFreq < StopFreq); continue; else; Go To 21.
19. Update device with new settings.
20. Wait for fresh configuration to arrive.
21. Increment RoundOf Data.
22. Print round of saved data on console.
23. If WholeDay < (Iteration×24); time statement.
24. RFEObj.Close(); ##Close port.
25. Wait for the processing of the next round of data.
26. Compute total elapsed time.
Scanning started with the first frequency range and data saved in CSV file format; after which the
next range was scanned until the 24 iterations were completed.Investigation started from 9.00am
on 27-09-2017 to 9.00am on 28-09-2017.For each band, 112 data points were resolved with a
Resolution Bandwidth (RBW) of 268 KHz. Each frequency range with 112 data points was
further subdivided into 8 frequency ranges for efficient computation.Spectrum Resource
Occupancy (SRO) for each range is calculated as in equation 1.
(1)
Where: NOCAT is the Number of Channel Above Threshold and TNMC is the Total Number of
measured Channels
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3.2. Estimation of Spectrum Utilization Level of the Measured Spectrum
Python script, as discussed in the previous section was employed to manipulate and automatically
change the settings of the digital device. The software changes the range of frequency to be
scanned at uniform time interval. A 112 data points comprising frequencies and corresponding
amplitudes of 24 frequency ranges were dumped in CSV file. The process was repeated until 15
sets of 112 data points were saved in each of the 24 files for 24 hours. The comma separated
value files were latter converted to Microsoft Excel files with .xlsx as the file extension. For a
range of frequencies with 268 KHz as resolution bandwidths, 15 columns, each with 112
amplitudes with corresponding frequencies were entered in excel worksheet. The tool allows the
computation of average amplitude in dBm which gives the mean amplitude for each sweep step.
The average values of the signal strength were further segmented for efficient computation. Each
segment consists of 14 sweep steps, which give rise to a total of 8 segments. The tool compares
these values with the threshold value to determine the extent of spectrum usage. Plots of
Spectrum Resource Occupancy versus frequency band were obtained. For more visualization of
spectrum usage level, various degrees of spectrum usage were shown with the use of a pie chart.
3.3. Identification of Potential Bands Suitable for CR
The tool used for identifying potential frequency bands suitable for cognitive radio deployment is
the Microsoft Excel 2016. By combining excel efficient functions, amplitude returned values
were compared with threshold value to estimate the percentage of occupancy for each frequency
range. To identify bands that could be accessed using cognitive radio, a threshold of 45% was
used. Percentage values less than or equal to 45% would be ideal bands for dynamic spectrum
access.
4. RESULTS AND DISCUSSION
Results of various analysis carried out in this work include the results of spectrum occupancy for
24 frequency bands for the range of 240-960MHz. For each band, with a band size of 30MHz, the
results of the mean amplitudes with corresponding Spectrum Resource Occupancy were
tabulated. Plots of Spectrum Resource Occupancy versus frequency range were presented.
Results of spectrum utilization level as well as identified bands suitable for cognitive radio were
shown.
The 24 possible bands of 240-960MHz were analyzed and occupancy level presented. From
analysis, 240-270MHz has the highest spectrum occupancy of 100%. For a threshold of -95dBm,
average signal strength of -88.18dBm shows maximum spectral activity within the region 240-
270MHz. The band has decibel maximum value of -85.89dBm and a minimum decibel value of -
91.42dBm.270-300MHz band is second most occupied with 51% occupancy. This band has
average strength of -95.93dBm, with maximum and minimum values of -93.24dBm and -
99.61dBm respectively.480-510MHz band with 47.63% occupancy is another measured band
whose spectrum resource occupancy is close to half of the total spectrum occupancy. The average
signal strength for this band is -101.21dBm with upper limit of -91.08dBm and lower limit of -
113.82dBm.
Aside aforementioned bands, as well as 450-480MHz and 510-540MHz with Average Spectrum
Occupancy (ASO) of 27.50% and 23.25% respectively, every other bands have average
spectrum occupancy of less than 7%. Bands 300-330MHz, 330-360MHz, 360-390MHz, 630-
660MHz, 660-690MHz, 690-720MHz, 720-750MHz, 810-840MHz, 840-870MHz, 870-
900MHz and 900-930MHz have the least spectrum occupancy with ASO of 0% each. The
comparative analysis of the various spectrum occupancies is as shown in Fig. 2. The degree of
8. International Journal of Wireless & Mobile Networks (IJWMN), Vol.13, No.5, October 2021
38
spectrum usage for the measurement location is shown in table 1.
Figure 2. Comparative Analysis on Spectrum Resource Occupancy for Various Bands
Table 1. Spectrum Utilization Table for 240-960 MHz
S/N Bands (MHz) Mean Amplitude(dBm) Level of Utilization
1 240-270 100.00% Completely utilized
2 270-300 51.00% Partially utilized
3 300-330 0.00% Unutilized
4 330-360 0.00% Unutilized
5 360-390 0.00% Unutilized
6 390-420 7.00% Partially utilized
7 420-450 1.75% Partially utilized
8 450-480 27.50% Partially utilized
9 480-510 47.63% Partially utilized
10 510-540 23.25% Partially utilized
11 540-570 0.88% Partially utilized
12 570-600 6.63% Partially utilized
13 600-630 2.50% Partially utilized
14 630-660 0.00% Unutilized
15 660-690 0.00% Unutilized
16 690-720 0.00% Unutilized
17 720-750 0.00% Unutilized
18 750-780 2.50% Partially utilized
19 780-810 4.25% Partially utilized
20 810-840 0.00% Unutilized
21 840-870 0.00% Unutilized
22 870-900 0.00% Unutilized
23 900-930 0.00% Unutilized
24 930-960 4.38% Partially utilized
From table 1, it is clear that unutilized bands are by far greater than completely utilized band. A
band is completely utilized, twelve bands are partially utilized while eleven bands are devoid of
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
120.00%
Spectrum
Resource
Occupancy
Frequency Band(MHz)
240-960 MHz
9. International Journal of Wireless & Mobile Networks (IJWMN), Vol.13, No.5, October 2021
39
spectral activities. The detailed analysis shows that 4.17% spectrum is completely utilized, 50%
is partially occupied while 45.83% is free. Table 2 and figure 3 show the level of utilization.
Table 2. Percentage Utilization
Utilization Level Percentage Utilization
Complete utilization 4.17%
Partial utilization 50%
Un utilization 45.83%
Figure 3. Percentage Utilization for 240-960 MHz
From table 1, one completely active band and two other fairly occupied bands are out of context
for cognitive radio technology, hence those bands should not be accessed dynamically. Twenty-
one bands have been identified for exploitation via cognitive radio technology. These are 300-
330 MHz, 330-360 MHz, 360-390 MHz, 390-420 MHz, 420-450 MHz, 450-480 MHz, 510-540
MHz, 540-570 MHz, 570-600 MHz, 600-630 MHz, 630-660 MHz, 660-690 MHz, 690-720 MHz,
720-750 MHz, 750-780 MHz, 780-810 MHz, 810-840 MHz, 840-870 MHz, 870-900 MHz and
900-930 MHz.
5. CONCLUSION
Radio frequency spectrum is a useful and finite resource and therefore need efficient
management system for complete utilization. As part of spectrum occupancy measurement
campaign, investigation of spectrum utilization in the frequency band of 240-290 MHz was
carried out in Rumuokwuta axis of Port Harcourt. The spectrum bands utilization was
investigated for 24hours using RF Explorer and efficient python script. Measurements and data
analysis show that the location is a good environment for dynamic spectrum access and
cognitive radio deployment. Spectrum occupancy measurements should indeed be carried out in
every parts of the whole world with sensitive and efficient spectrum detectors.
Spectrum Utilization Level
Complete
utilization
Partial utilization
Un utilization
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40
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AUTHOR BIOGRAPHY
Engr. Dr. Nkwachukwu Chukwuchekwa is an Associate Professor in the department of Electrical and
Electronic Engineering (EEE), Federal University of Technology, Owerri (FUTO), Nigeria. He obtained
his Bachelor of Engineering (B.Eng) degree in Electronic and Computer
Engineering from FUTO in 2000. His Master of Engineering degree was obtained in
Communication Engineering from the same University in 2006. Dr. Chukwuchekwa
proceeded to the United Kingdom precisely at Cardiff University where he obtained
his PhD in Electrical Engineering in 2012 using the United Kingdom’s Engineering
and Physical Sciences Research Council (EPSRC) scholarship. He spent the Spring
Semester of 2014 as a Postdoctoral Research Fellow at Massachusetts Institute of
Technology (MIT) USA sponsored by Total E&P, Nigeria National Petrolem Corporation (NNPC) and
Google. He has published many research articles and presented papers at reputable International
conferences in United Kingdom, Czech Republic, Italy, Greece, Poland etc. Dr. Chukwucchekwa is a
registered member of Council for the Regulations of Engineering in Nigeria (COREN), a corporate
member of the Nigerian Society of Engineers (NSE) and a member of Institute of Electrical and Electronic
Engineering (IEEE), USA. He was EEE departmental examination officer for several years and is at
present the Departmental Postgraduate Cordinator. Nkwachukwu enjoys playing football, reading, Internet
browsing, prayer and evangelism.
Longinus Sunday Ezema (Ph.D) is a senior lecturer and passionate student mentor
with about 10 years of postgraduate lecturing and research experience at the
Department of Electrical and Electronic Engineering, Federal University of
Technology, Owerri, Imo State, Nigeria. He received his B. Eng, in Electrical and
Electronic Engineering from the Nnamdi Azikiwe University (NAU), Awka. He
obtained M. Eng in Telecommunication System from Blekinge Institute of
Technology (BTH), Sweden and Ph.D in Electronic Engineering at University of
Nigeria Nsukka (UNN). He is a registered member of Nigerian Society of
Engineers (NSE), and Council for the Regulation of Engineering in Nigeria (COREN).
Cosmas Kemdirim Agubor has a B.Eng in Electrical and Electronic Engineering,
M.Eng in Electronic and Telecommunication Engineering and PhD in
Communication Engineering Technology. He is a registered member of the Nigerian
Society of Engineers (NSE) and the Council for the Regulation of Engineering in
Nigeria (COREN). He was an Engineer with the Nigerian Telecommunications
Limited (NITEL) and rose to the rank of an Assistant Manager. He is now the Head
of the Department of Electronic Engineering, Federal University of Technology,
Owerri, Imo State, Nigeria. He has several publications in both national and
international journals. His research area is in wireless communication with special interest in Antenna
systems.