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.
SELECTION OF SPECTRUM SENSING METHOD TO ENHANCE QOS IN COGNITIVE RADIO NETWORKSijwmn
The massively increasing number of wireless communication devices has led to considerable growths in
radio traffic density, resulting in a predictable shortage of the available spectrum. To address this potential
shortage, the Cognitive Radio (CR) technology offers promising solutions that aim to improve the spectrum
utilization. The operation of CR relies on detecting the so-called spectrum holes, i.e., the frequency bands
when they are unoccupied by their licensed operators. The unlicensed users are then allowed to
communicate using these spectrum holes. Consequently, the performance of CR is highly dependent on the
employed spectrum sensing methods. Several sensing methods are already available or literarily proposed.
However, no individual method can accommodate all possible CR operation scenarios. Hence, it is fair to
ascertain that the performance of a CR device can be improved if it is capable of supporting several
sensing methods. Then it should be able to effectively select the most suitable method. In this paper, several
spectrum sensing methods are compared and analyzed, aiming to identify their advantages and
shortcomings in different CR operating conditions. Furthermore, it identifies the factors that need to be
considered while selecting a proper sensing method from the catalog of available methods.
SPECTRUM SENSING STRATEGY TO ENHANCE THE QOS IN WHITE-FI NETWORKSIJCNC Journal
The rapidly growing number of wireless devices running applications that require high bandwidths, has
resulted in increasing demands for the unlicensed frequency spectrum. Given the scarcity of allocated
unlicensed frequencies, meeting such demands can become a serious concern. Cognitive Radio (CR)
technology opens the door for the opportunistic use of the licensed spectrum to partially address the issues
relevant to the limited availability of unlicensed frequencies. Combining CR and Wi-Fi to form the socalled
White-Fi networks, has been proposed for achieving higher spectrum utilization. This article
discusses the spectrum sensing in White-Fi networks and the impacts that it has on the QoS of typical
applications. It also reports the analysis of such impacts through various simulation studies. Our results
demonstrate the advantages of an adaptive sensing strategy that is capable of changing the related
parameters based on QoS requirements. We also propose such a sensing strategy that can adapt to the
IEEE 802.11e requirements. The goal of the proposed strategy is the enhancement of the overall QoS of the
applications while maintaining efficient sensing of the spectrum. Simulation results of the scenarios that
implement the proposed mechanisms demonstrate noticeable QoS improvements compared to cases where
common sensing methods are utilized in IEEE802.11 networks.
Comparative Study of Different Non-Cooperative Techniques in Cognitive RadioRSIS International
Wireless technology is expanding its domain and with it
is growing the need for more frequencies for communication.
Cognitive radio offers a solution to this problem by using the
concept of Dynamic spectrum access instead of fixed spectrum
allocation. Such radios are capable of sensing the RF spectrum
for identifying idle frequency bands. It then transmits
opportunistically so as to avoid interference with primary user
over same band. In cognitive radio, intelligent spectrum sensing
forms the major and most important part. Out of the various
sensing techniques, we will give an overview of some of the
prominent non-cooperative techniques. The paper deals with
comparative study of these methods.
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.
SELECTION OF SPECTRUM SENSING METHOD TO ENHANCE QOS IN COGNITIVE RADIO NETWORKSijwmn
The massively increasing number of wireless communication devices has led to considerable growths in
radio traffic density, resulting in a predictable shortage of the available spectrum. To address this potential
shortage, the Cognitive Radio (CR) technology offers promising solutions that aim to improve the spectrum
utilization. The operation of CR relies on detecting the so-called spectrum holes, i.e., the frequency bands
when they are unoccupied by their licensed operators. The unlicensed users are then allowed to
communicate using these spectrum holes. Consequently, the performance of CR is highly dependent on the
employed spectrum sensing methods. Several sensing methods are already available or literarily proposed.
However, no individual method can accommodate all possible CR operation scenarios. Hence, it is fair to
ascertain that the performance of a CR device can be improved if it is capable of supporting several
sensing methods. Then it should be able to effectively select the most suitable method. In this paper, several
spectrum sensing methods are compared and analyzed, aiming to identify their advantages and
shortcomings in different CR operating conditions. Furthermore, it identifies the factors that need to be
considered while selecting a proper sensing method from the catalog of available methods.
SPECTRUM SENSING STRATEGY TO ENHANCE THE QOS IN WHITE-FI NETWORKSIJCNC Journal
The rapidly growing number of wireless devices running applications that require high bandwidths, has
resulted in increasing demands for the unlicensed frequency spectrum. Given the scarcity of allocated
unlicensed frequencies, meeting such demands can become a serious concern. Cognitive Radio (CR)
technology opens the door for the opportunistic use of the licensed spectrum to partially address the issues
relevant to the limited availability of unlicensed frequencies. Combining CR and Wi-Fi to form the socalled
White-Fi networks, has been proposed for achieving higher spectrum utilization. This article
discusses the spectrum sensing in White-Fi networks and the impacts that it has on the QoS of typical
applications. It also reports the analysis of such impacts through various simulation studies. Our results
demonstrate the advantages of an adaptive sensing strategy that is capable of changing the related
parameters based on QoS requirements. We also propose such a sensing strategy that can adapt to the
IEEE 802.11e requirements. The goal of the proposed strategy is the enhancement of the overall QoS of the
applications while maintaining efficient sensing of the spectrum. Simulation results of the scenarios that
implement the proposed mechanisms demonstrate noticeable QoS improvements compared to cases where
common sensing methods are utilized in IEEE802.11 networks.
Comparative Study of Different Non-Cooperative Techniques in Cognitive RadioRSIS International
Wireless technology is expanding its domain and with it
is growing the need for more frequencies for communication.
Cognitive radio offers a solution to this problem by using the
concept of Dynamic spectrum access instead of fixed spectrum
allocation. Such radios are capable of sensing the RF spectrum
for identifying idle frequency bands. It then transmits
opportunistically so as to avoid interference with primary user
over same band. In cognitive radio, intelligent spectrum sensing
forms the major and most important part. Out of the various
sensing techniques, we will give an overview of some of the
prominent non-cooperative techniques. The paper deals with
comparative study of these methods.
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.
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
An Approach to Spectrum Sensing in Cognitive Radio IOSR Journals
Recent research shows that more than 70% of the available spectrum is not utilized
efficiently. The bandwidth becomes expensive due to a shortage of frequencies. Therefore for efficient
utilization of spectrum, we need to sniff the spectrum to determine whether it is being used by primary user or
not. The term cognitive radio refers to the adoption of radio parameters using the sensed information
of the spectrum. There are various spectrum sensing techniques proposed in the literature but still there is
room for researchers in this field to explore more sophisticated approaches. There are three major
categories of spectrum sensing techniques; transmitter detection, receiver detection and interference
temperature detection. This thesis presents a survey of techniques suggested in the literature for
spectrum sensing with a performance analysis of transmitter-based detection techniques.
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.
Heterogeneous Spectrum Sensing in Cognitive Radio Network using Traditional E...IJEACS
The accurate spectrum sensing is a predominant
aspect of any competent CR system. Efficient spectrum sensing
enables a CR terminal to detect the spectrum holes (underutilized
spectral bands) by providing high spectral resolution, thereby
accrediting opportunistic transmission in the licensed band to the
CR. In order to facilitate a good spectrum management and its
efficient use a hybrid method for the detection of the spectrum
with the purpose of detecting the presence of bands of
unoccupied frequencies is proposed. The method used are
traditional energy detection and matched filter with changing
number of secondary users using each technique and finally a
centralized cooperative spectrum sensing network which employs
hard combination at the fusion centre.
A SURVEY ON OPTIMIZATION BASED SPECTRUM SENSING TECHNIQUES TO REDUCE ISI AND ...IJNSA Journal
Cognitive radio is emerging technologies in OFDM based wireless systems which are very important for spectrum sensing. By using cognitive radio (CR) high data can be transferred with low bit error rate. The key idea of OFDM is to split the total transmission bandwidth into the subcarriers which further reduce the intersymbol interference (ISI) and peak to average power ratio(PAPR) in the signal. There are many optimization based spectrum sensing techniques are existing for efficient sensing purpose but each has its own advantages and disadvantages. This leads to start the comprehensive study for reducing PAPR and ISI(Intersymbol interference) in terms of FPGA based partial configuration. In the first part of review OFDM characteristics of the signal has compared with several optimizations based ISI reduction techniques. The second part is to compare the various spectrum sensing techniques in cognitive radio engine and its application in FPGA.
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.
MULTI-STAGES CO-OPERATIVE/NONCOOPERATIVE SCHEMES OF SPECTRUM SENSING FOR COGN...ijwmn
Searching for spectrum holes in practical wireless channels where primary users experience multipath
fading and shadowing, with noise uncertainty, limits the detection performance significantly. Moreover, the
detection challenge will be tougher when different band types have to be sensed, with different signal and
spectral characteristics, and probably overlapping spectra. Besides, primary user waveforms can be known
(completely or partially) or unknown to allow or forbid cognitive radios to use specific kinds of detection
schemes! Hidden primary user’s problem, and doubly selective channel oblige the use of cooperative
sensing to exploit the spatial diversity in the observations of spatially located cognitive radio users.
Incorporated all the aforementioned practical challenges as a whole, this paper developed a new multistage detection scheme that intelligently decides the detection algorithm based on power, noise, bandwidth
and knowledge of the signal of interest. The proposed scheme switches between individual and cooperative
sensing and among featured based sensing techniques (cyclo-stationary detection and matched filter) and
sub-band energy detection according to the characteristics of signal and band of interest.Compared to the
existing schemes, performance evaluations show reliable results in terms of probabilities of detection and
mean sensing times under the aforementioned conditions.
SPECTRUM SENSING STRATEGY TO ENHANCE THE QOS IN WHITE-FI NETWORKSIJCNCJournal
The rapidly growing number of wireless devices running applications that require high bandwidths, has resulted in increasing demands for the unlicensed frequency spectrum. Given the scarcity of allocated unlicensed frequencies, meeting such demands can become a serious concern. Cognitive Radio (CR) technology opens the door for the opportunistic use of the licensed spectrum to partially address the issues relevant to the limited availability of unlicensed frequencies. Combining CR and Wi-Fi to form the socalled White-Fi networks, has been proposed for achieving higher spectrum utilization. This article discusses the spectrum sensing in White-Fi networks and the impacts that it has on the QoS of typical applications. It also reports the analysis of such impacts through various simulation studies. Our results demonstrate the advantages of an adaptive sensing strategy that is capable of changing the related parameters based on QoS requirements. We also propose such a sensing strategy that can adapt to the IEEE 802.11e requirements. The goal of the proposed strategy is the enhancement of the overall QoS of the applications while maintaining efficient sensing of the spectrum. Simulation results of the scenarios that implement the proposed mechanisms demonstrate noticeable QoS improvements compared to cases where common sensing methods are utilized in IEEE802.11 networks.
Sensing Time Improvement Using Two Stage Detectors for Cognitive Radio SystemIJCNCJournal
Cognitive radio (CR) is a promising technology for both present and future telecommunications to satisfy the demand of the next generation due to inefficient use of the allocated spectrum. Due to its ability to utilize the available bandwidth of other wireless communication networks and so enhance its occupancy. Spectrum sensing (SS) is the key characteristic of the CR system that helps it identify the empty spectrum. SS has gained a lot of interest recently and it is an active research area since it offers additional opportunities to secondary users. A broad variety of analytical methods to identify the Primary User's (PUs) presence have emerged as a result of SS techniques for CRs. Although each approach has its own benefits, the drawbacks attached to them make an individual implementation of the technique impractical for usage. To mitigate the drawbacks and maximize the benefits offered by the individual methods, a two stage detector can be employed for SS. However, the stages method lengthens the time needed to sense the spectrum and produce a definitive result. In this paper, we propose a two-stage sensing approach, where the first stage is an Interval Dependent Denoising detector (IDD) and followed by a second stage is Energy Detection (ED) which provides a large decrease in the mean sensing time compared to different related two-stage SS approaches The ED method is simple and has a short sensing time, but it performs poorly when the Signal to Noise Ratio (SNR) is low, Hence it is thought that separating PU activity from noise is essential for accurate SS. Therefore, we use IDD as a noise reduction technique in the first stage before the ED stage. The simulation results have been utilized to demonstrate that this technique leads to a large savings in sensing time as compared to an existing two-stage detection approaches
Sensing Time Improvement using Two Stage Detectors for Cognitive Radio SystemIJCNCJournal
Cognitive radio (CR) is a promising technology for both present and future telecommunications to satisfy the demand of the next generation due to inefficient use of the allocated spectrum. Due to its ability to utilize the available bandwidth of other wireless communication networks and so enhance its occupancy. Spectrum sensing (SS) is the key characteristic of the CR system that helps it identify the empty spectrum. SS has gained a lot of interest recently and it is an active research area since it offers additional opportunities to secondary users. A broad variety of analytical methods to identify the Primary User's (PUs) presence have emerged as a result of SS techniques for CRs. Although each approach has its own benefits, the drawbacks attached to them make an individual implementation of the technique impractical for usage. To mitigate the drawbacks and maximize the benefits offered by the individual methods, a two stage detector can be employed for SS. However, the stages method lengthens the time needed to sense the spectrum and produce a definitive result. In this paper, we propose a two-stage sensing approach, where the first stage is an Interval Dependent Denoising detector (IDD) and followed by a second stage is Energy Detection (ED) which provides a large decrease in the mean sensing time compared to different related two-stage SS approaches The ED method is simple and has a short sensing time, but it performs poorly when the Signal to Noise Ratio (SNR) is low, Hence it is thought that separating PU activity from noise is essential for accurate SS. Therefore, we use IDD as a noise reduction technique in the first stage before the ED stage. The simulation results have been utilized to demonstrate that this technique leads to a large savings in sensing time as compared to an existing two-stage detection approaches.
Implementation of Vacate on Demand Algorithm in Various Spectrum Sensing Netw...IJERA Editor
In present days the wireless communications are widely increases because of this reason spectrum utilization can be rapidly increased.For efficient usage of spectrum we can implement the Vacate on demand algorithm in different networks. CR users also need to sense the spectrum and vacate the channel upon the detection of the PU‟s presence to protectPUs from harmful interference. To achieve these fundamental CR functions, CR users usually coordinate with each other by using a common medium for control message exchange ensuring a priority of PUs over CR users. This paper presents the Vacate on Demand (VD) algorithm which enables dynamic spectrum access and ensures to vacate the assigned channel in case of PU activity and move the CR user to some other vacant channel to make spectrum available to PUs as well as to CR users. The basic idea is to use a ranking table of the available channels based on the PU activity detected on each channel. To improve the spectrum efficiency we can implement the Vacate on demand algorithm in MANET Network.
Spectrum scarcity is an emerging issue in wireless communication systems due to the increasing
demand of broadband services like mobile communications, wireless internet access, IoT applications,
among others. The migration of analog TV to digital systems (a.k.a. digital TV switchover) has led to
the release of a significant spectrum share that can be used to support said additional services. Likewise,
TV white spaces emerge as spectral opportunities that can also be explored. Hence, cognitive radio (CR)
presents itself as a feasible approach to efficiently use resources and exploit gaps within the spectrum.
The goal of this paper is to unveil the state of the art revolving around the usage of TV white spaces,
including some of the most important methods developed to exploit such spaces, upcoming opportunities,
challenges for future research projects, and suggestions to improve current models.
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
An Approach to Spectrum Sensing in Cognitive Radio IOSR Journals
Recent research shows that more than 70% of the available spectrum is not utilized
efficiently. The bandwidth becomes expensive due to a shortage of frequencies. Therefore for efficient
utilization of spectrum, we need to sniff the spectrum to determine whether it is being used by primary user or
not. The term cognitive radio refers to the adoption of radio parameters using the sensed information
of the spectrum. There are various spectrum sensing techniques proposed in the literature but still there is
room for researchers in this field to explore more sophisticated approaches. There are three major
categories of spectrum sensing techniques; transmitter detection, receiver detection and interference
temperature detection. This thesis presents a survey of techniques suggested in the literature for
spectrum sensing with a performance analysis of transmitter-based detection techniques.
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.
Heterogeneous Spectrum Sensing in Cognitive Radio Network using Traditional E...IJEACS
The accurate spectrum sensing is a predominant
aspect of any competent CR system. Efficient spectrum sensing
enables a CR terminal to detect the spectrum holes (underutilized
spectral bands) by providing high spectral resolution, thereby
accrediting opportunistic transmission in the licensed band to the
CR. In order to facilitate a good spectrum management and its
efficient use a hybrid method for the detection of the spectrum
with the purpose of detecting the presence of bands of
unoccupied frequencies is proposed. The method used are
traditional energy detection and matched filter with changing
number of secondary users using each technique and finally a
centralized cooperative spectrum sensing network which employs
hard combination at the fusion centre.
A SURVEY ON OPTIMIZATION BASED SPECTRUM SENSING TECHNIQUES TO REDUCE ISI AND ...IJNSA Journal
Cognitive radio is emerging technologies in OFDM based wireless systems which are very important for spectrum sensing. By using cognitive radio (CR) high data can be transferred with low bit error rate. The key idea of OFDM is to split the total transmission bandwidth into the subcarriers which further reduce the intersymbol interference (ISI) and peak to average power ratio(PAPR) in the signal. There are many optimization based spectrum sensing techniques are existing for efficient sensing purpose but each has its own advantages and disadvantages. This leads to start the comprehensive study for reducing PAPR and ISI(Intersymbol interference) in terms of FPGA based partial configuration. In the first part of review OFDM characteristics of the signal has compared with several optimizations based ISI reduction techniques. The second part is to compare the various spectrum sensing techniques in cognitive radio engine and its application in FPGA.
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.
MULTI-STAGES CO-OPERATIVE/NONCOOPERATIVE SCHEMES OF SPECTRUM SENSING FOR COGN...ijwmn
Searching for spectrum holes in practical wireless channels where primary users experience multipath
fading and shadowing, with noise uncertainty, limits the detection performance significantly. Moreover, the
detection challenge will be tougher when different band types have to be sensed, with different signal and
spectral characteristics, and probably overlapping spectra. Besides, primary user waveforms can be known
(completely or partially) or unknown to allow or forbid cognitive radios to use specific kinds of detection
schemes! Hidden primary user’s problem, and doubly selective channel oblige the use of cooperative
sensing to exploit the spatial diversity in the observations of spatially located cognitive radio users.
Incorporated all the aforementioned practical challenges as a whole, this paper developed a new multistage detection scheme that intelligently decides the detection algorithm based on power, noise, bandwidth
and knowledge of the signal of interest. The proposed scheme switches between individual and cooperative
sensing and among featured based sensing techniques (cyclo-stationary detection and matched filter) and
sub-band energy detection according to the characteristics of signal and band of interest.Compared to the
existing schemes, performance evaluations show reliable results in terms of probabilities of detection and
mean sensing times under the aforementioned conditions.
SPECTRUM SENSING STRATEGY TO ENHANCE THE QOS IN WHITE-FI NETWORKSIJCNCJournal
The rapidly growing number of wireless devices running applications that require high bandwidths, has resulted in increasing demands for the unlicensed frequency spectrum. Given the scarcity of allocated unlicensed frequencies, meeting such demands can become a serious concern. Cognitive Radio (CR) technology opens the door for the opportunistic use of the licensed spectrum to partially address the issues relevant to the limited availability of unlicensed frequencies. Combining CR and Wi-Fi to form the socalled White-Fi networks, has been proposed for achieving higher spectrum utilization. This article discusses the spectrum sensing in White-Fi networks and the impacts that it has on the QoS of typical applications. It also reports the analysis of such impacts through various simulation studies. Our results demonstrate the advantages of an adaptive sensing strategy that is capable of changing the related parameters based on QoS requirements. We also propose such a sensing strategy that can adapt to the IEEE 802.11e requirements. The goal of the proposed strategy is the enhancement of the overall QoS of the applications while maintaining efficient sensing of the spectrum. Simulation results of the scenarios that implement the proposed mechanisms demonstrate noticeable QoS improvements compared to cases where common sensing methods are utilized in IEEE802.11 networks.
Sensing Time Improvement Using Two Stage Detectors for Cognitive Radio SystemIJCNCJournal
Cognitive radio (CR) is a promising technology for both present and future telecommunications to satisfy the demand of the next generation due to inefficient use of the allocated spectrum. Due to its ability to utilize the available bandwidth of other wireless communication networks and so enhance its occupancy. Spectrum sensing (SS) is the key characteristic of the CR system that helps it identify the empty spectrum. SS has gained a lot of interest recently and it is an active research area since it offers additional opportunities to secondary users. A broad variety of analytical methods to identify the Primary User's (PUs) presence have emerged as a result of SS techniques for CRs. Although each approach has its own benefits, the drawbacks attached to them make an individual implementation of the technique impractical for usage. To mitigate the drawbacks and maximize the benefits offered by the individual methods, a two stage detector can be employed for SS. However, the stages method lengthens the time needed to sense the spectrum and produce a definitive result. In this paper, we propose a two-stage sensing approach, where the first stage is an Interval Dependent Denoising detector (IDD) and followed by a second stage is Energy Detection (ED) which provides a large decrease in the mean sensing time compared to different related two-stage SS approaches The ED method is simple and has a short sensing time, but it performs poorly when the Signal to Noise Ratio (SNR) is low, Hence it is thought that separating PU activity from noise is essential for accurate SS. Therefore, we use IDD as a noise reduction technique in the first stage before the ED stage. The simulation results have been utilized to demonstrate that this technique leads to a large savings in sensing time as compared to an existing two-stage detection approaches
Sensing Time Improvement using Two Stage Detectors for Cognitive Radio SystemIJCNCJournal
Cognitive radio (CR) is a promising technology for both present and future telecommunications to satisfy the demand of the next generation due to inefficient use of the allocated spectrum. Due to its ability to utilize the available bandwidth of other wireless communication networks and so enhance its occupancy. Spectrum sensing (SS) is the key characteristic of the CR system that helps it identify the empty spectrum. SS has gained a lot of interest recently and it is an active research area since it offers additional opportunities to secondary users. A broad variety of analytical methods to identify the Primary User's (PUs) presence have emerged as a result of SS techniques for CRs. Although each approach has its own benefits, the drawbacks attached to them make an individual implementation of the technique impractical for usage. To mitigate the drawbacks and maximize the benefits offered by the individual methods, a two stage detector can be employed for SS. However, the stages method lengthens the time needed to sense the spectrum and produce a definitive result. In this paper, we propose a two-stage sensing approach, where the first stage is an Interval Dependent Denoising detector (IDD) and followed by a second stage is Energy Detection (ED) which provides a large decrease in the mean sensing time compared to different related two-stage SS approaches The ED method is simple and has a short sensing time, but it performs poorly when the Signal to Noise Ratio (SNR) is low, Hence it is thought that separating PU activity from noise is essential for accurate SS. Therefore, we use IDD as a noise reduction technique in the first stage before the ED stage. The simulation results have been utilized to demonstrate that this technique leads to a large savings in sensing time as compared to an existing two-stage detection approaches.
Implementation of Vacate on Demand Algorithm in Various Spectrum Sensing Netw...IJERA Editor
In present days the wireless communications are widely increases because of this reason spectrum utilization can be rapidly increased.For efficient usage of spectrum we can implement the Vacate on demand algorithm in different networks. CR users also need to sense the spectrum and vacate the channel upon the detection of the PU‟s presence to protectPUs from harmful interference. To achieve these fundamental CR functions, CR users usually coordinate with each other by using a common medium for control message exchange ensuring a priority of PUs over CR users. This paper presents the Vacate on Demand (VD) algorithm which enables dynamic spectrum access and ensures to vacate the assigned channel in case of PU activity and move the CR user to some other vacant channel to make spectrum available to PUs as well as to CR users. The basic idea is to use a ranking table of the available channels based on the PU activity detected on each channel. To improve the spectrum efficiency we can implement the Vacate on demand algorithm in MANET Network.
Spectrum scarcity is an emerging issue in wireless communication systems due to the increasing
demand of broadband services like mobile communications, wireless internet access, IoT applications,
among others. The migration of analog TV to digital systems (a.k.a. digital TV switchover) has led to
the release of a significant spectrum share that can be used to support said additional services. Likewise,
TV white spaces emerge as spectral opportunities that can also be explored. Hence, cognitive radio (CR)
presents itself as a feasible approach to efficiently use resources and exploit gaps within the spectrum.
The goal of this paper is to unveil the state of the art revolving around the usage of TV white spaces,
including some of the most important methods developed to exploit such spaces, upcoming opportunities,
challenges for future research projects, and suggestions to improve current models.
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.
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.
Bio-inspired route estimation in cognitive radio networks IJECEIAES
Cognitive radio is a technique that was originally created for the proper use of the radio electric spectrum due its underuse. A few methods were used to predict the network traffic to determine the occupancy of the spectrum and then use the ‘holes’ between the transmissions of primary users. The goal is to guarantee a complete transmission for the second user while not interrupting the trans-mission of primary users. This study seeks the multifractal generation of traffic for a specific radio electric spectrum as well as a bio-inspired route estimation for secondary users. It uses the MFHW algorithm to generate multifractal traces and two bio-inspired algo-rithms: Ant Colony Optimization and Max Feeding to calculate the secondary user’s path. Multifractal characteristics offer a predic-tion, which is 10% lower in comparison with the original traffic values and a complete transmission for secondary users. In fact, a hybrid strategy combining both bio-inspired algorithms promise a reduction in handoff. The purpose of this research consists on deriving future investigation in the generation of multifractal traffic and a mobility spectrum using bio-inspired algorithms.
ENERGY EFFICIENT COOPERATIVE SPECTRUM SENSING IN COGNITIVE RADIOIJCNCJournal
Sensing in cognitive radio (CR) protects the primary user (PU) from bad interference. Therefore, it is
assumed to be a requirement. However, sensing has two main challenges; first the CR is required to sense
the PU under very low signal to noise ratios which will take longer sensing time, and second, some CR
nodes may suffer from deep fading and shadowing effects. Cooperative spectrum sensing (CSS) is supposed
to solve these challenges. However, CSS adds extra energy consumption due to CRs send the sensing result
to the fusion center and receive the final decision from the fusion center. This is in addition to the sensing
energy itself. Therefore, CSS may consume considerable energy out of the battery of the CR node.
Therefore in this paper, we try to find jointly the sensing time required from each CR node and the number
of CR nodes who should perform sensing such that the energy and energy efficiency (i.e., ratio of
throughput to energy consumed) are optimized. Simulation results show that the joint optimization achieves
better in terms of energy efficiency than other approaches that perform separate optimization.
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.
OPTIMIZATION OF THE RECURSIVE ONE-SIDED HYPOTHESIS TESTING TECHNIQUE FOR AUTO...ijwmn
In this paper, an optimized Recursive One-Sided Hypothesis Testing (ROHT) threshold estimation algorithm for energy detection based on Cognitive Radio (CR) application is presented. The ROHT algorithm is well known to compute and correct threshold values based on the choice of the parameter
values; namely the coefficient of standard deviation (z-value) and the stopping criteria (). A fixed computational process has been employed in most cases to estimate these parameter values, thus rendering them non-adaptive under different sensing conditions. Also, this fixed (manual tuning) process requires prior knowledge of some noise level to enable pre-configuration of a predefined target false alarm rate. This renders the parameter estimation process cumbrous and unworkable for real-time purposes, particularly for autonomous CR applications. Furthermore, using wrong parameter values may lead to either too high or too low false alarms or detection rates of the algorithm. Sequel to aforementioned mentioned constraints, we propose a new mechanism for instantaneous parameter optimization of the ROHT algorithm using Particle Swarm Optimization (PSO) algorithm. Our PSO-ROHT model design was extensively tested under different conditions and results were compared to the non-optimized ROHT. The
results obtained show that the proposed design effectively adapts the parameter values of the Recursive One-Sided Hypothesis Testing algorithm in accordance with the input dataset under consideration. Also, that the proposed optimized model outperforms its non-optimized counterpart following the estimated detection probability and false alarm probability of both schemes, particularly in detecting Orthogonal Frequency-Division Multiplexing signals. In detecting the Orthogonal Frequency-Division Multiplexingsignals at signal-to-noise ratio of 3dB and above, the proposed model achieved a higher detection rate of 96.23% while maintaining a low false alarm rate below 10%, which complies with the IEEE 802.22
standard for Cognitive Radio application. Our PSO-ROHT algorithm is shown to be highly effective for autonomous and full blind signal detection in CR, with strong potentials for application in other areas requiring automatic threshold estimation.
OPTIMIZATION OF THE RECURSIVE ONE-SIDED HYPOTHESIS TESTING TECHNIQUE FOR AUTO...ijwmn
In this paper, an optimized Recursive One-Sided Hypothesis Testing (ROHT) threshold estimation algorithm for energy detection based on Cognitive Radio (CR) application is presented. The ROHT algorithm is well known to compute and correct threshold values based on the choice of the parameter
values; namely the coefficient of standard deviation (z-value) and the stopping criteria (). A fixed computational process has been employed in most cases to estimate these parameter values, thus rendering them non-adaptive under different sensing conditions. Also, this fixed (manual tuning) process requires prior knowledge of some noise level to enable pre-configuration of a predefined target false alarm rate.
This renders the parameter estimation process cumbrous and unworkable for real-time purposes, particularly for autonomous CR applications. Furthermore, using wrong parameter values may lead to either too high or too low false alarms or detection rates of the algorithm. Sequel to aforementioned mentioned constraints, we propose a new mechanism for instantaneous parameter optimization of the ROHT algorithm using Particle Swarm Optimization (PSO) algorithm. Our PSO-ROHT model design was extensively tested under different conditions and results were compared to the non-optimized ROHT. The
results obtained show that the proposed design effectively adapts the parameter values of the Recursive One-Sided Hypothesis Testing algorithm in accordance with the input dataset under consideration. Also, that the proposed optimized model outperforms its non-optimized counterpart following the estimated detection probability and false alarm probability of both schemes, particularly in detecting Orthogonal Frequency-Division Multiplexing signals. In detecting the Orthogonal Frequency-Division Multiplexing signals at signal-to-noise ratio of 3dB and above, the proposed model achieved a higher detection rate of 96.23% while maintaining a low false alarm rate below 10%, which complies with the IEEE 802.22standard for Cognitive Radio application. Our PSO-ROHT algorithm is shown to be highly effective for autonomous and full blind signal detection in CR, with strong potentials for application in other areas requiring automatic threshold estimation.
Routing in Cognitive Radio Networks - A SurveyIJERA Editor
Cognitive Radio Networks (CRNs) have been emerged as a revolutionary solution to migrate the spectrum
scarcity problem in wireless networks. Due to increasing demand for additional spectrum resources, CRNs have
been receiving significant research to solve issues related with spectrum underutilization. This technology
brings efficient spectrum usage and effective interference avoidance, and also brings new challenges to routing
in multi-hop Cognitive Radio Networks. In CRN, unlicensed users or secondary users are able to use
underutilized licensed channels, but they have to leave the channel if any interference is caused to the primary or
licensed users. So CR technology allows sharing of licensed spectrum band in opportunistic and non-interfering
manner. Different routing protocols have been proposed recently based on different design goals under different
assumptions.
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.
Similar to Spectrum Sensing in Cognitive Radio Networks : QoS Considerations (20)
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
2. 10 Computer Science & Information Technology (CS & IT)
space and location. An SU can utilize these holes in addition to the unlicensed bands that it may
typically use.
To achieve their objectives, CR systems are dependent on the execution of a sequence of several
functions, the so-called CR cycle. A typical CR cycle was proposed by Mitola [3]. This is
illustrated in Figure 1.
Figure 1. Basic CR cycle
The main functions of this CR cycle are spectrum sensing, spectrum decision, spectrum sharing,
and spectrum mobility. More specifically, an SU should be able to perform the following [4]:
• Spectrum sensing: sense the surrounding RFS to determine spectrum holes and to detect
the presence of the relevant PU.
• Spectrum decision: analyze and decide which spectrum hole is the most suitable for
satisfying the application requirements.
• Spectrum sharing: share the available spectrum holes with other SUs as fairly as possible.
• Spectrum mobility: seamlessly switch to another suitable spectrum hole to avoid
interference with a detected PU that may wish to start using its licensed spectrum.
Detecting the presence of a PU, or more precisely finding out whether the PU is using its
allocated spectrum or not, is an essential task for a CR device. On one hand, this fundamental task
requires improving sensing accuracy by avoiding false positive results while detecting the
presence of a PU. On the other, the employed sensing technique should achieve a high detecting
probability of the available spectrum holes. The nature of the electromagnetic signals makes
accurate sensing a complicated process. More specifically, the Signal to Noise Ratio (SNR), the
multipath fading of the PU signals, and the changing levels of noise can significantly affect the
sensing accuracy [5, 6]. Moreover, imperfect spectrum sensing can result in increased
transmission error rates, for both the PU and the SUs [7]. Such errors may contribute to the
degradation of the quality of the services provided by a PU and SUs. Noticeably, any QoS
degradation that can be attributed to the CR technology can potentially harm the progress of the
CR-based solutions. In this paper, the main features and limitations of the prevalent spectrum
sensing methods are examined. Furthermore, the key aspects that should be involved in selecting
the appropriate sensing method are highlighted and discussed.
The remainder of this paper is organized as follows. Section II presents the background and
motivation for this work. The effects of the sensing operation on the QoS of the applications
running over CR networks are described in Section III. Several sensing approaches are discussed
and evaluated in Section IV. Factors that may help in selecting the proper sensing techniques are
outlined in Section V. The last section gives the conclusions and points to the potential future
expansion of the reported work.
3. Computer Science & Information Technology (CS & IT) 11
2. MOTIVATIONS
Most of the previous reviews of spectrum sensing techniques are mainly focused on the operation,
accuracies, complexities, and implementation issues [8-11]. For instance, the relation between the
sensing accuracy, and the speed, i.e., sensing time, and frequency, i.e., repeating the sensing, are
the primary focus of the authors in [8]. They aim to achieve an optimal spectrum sensing
performance with the capability of flexible tuning between the speed and frequency. However,
they find that the available state-of-art sensing technologies do not offer a possible trade-off
between complexity and accuracy. In contrast, other reviewers consider the characteristics of the
PU signal as the main factor for selecting a proper sensing method [9]. Nevertheless, other factors
should be considered for more adaptive sensing and improved performance.
In general, the dominant approach is about how to find an optimal sensing method for all possible
CR operation requirements. However, none of the proposed sensing methods is suitable for all
possible sensing situations, conditions and technologies of CR systems. This study shifts the
focus to another approach where a CR device supports a range of various sensing methods. Thus,
the proper sensing technique can be selected based on the real-time requirements. This approach
implies the need for a real-time mechanism to select the most suitable sensing method. In this
paper, various sensing methods are studied toward finding the relevant selection criteria that
should be considered when designing such as real-time selection mechanism.
3. SENSING OPERATION IMPACT ON APPLICATIONS’ QOS IN CR
NETWORKS
As shown in Figure 2, the operation of a CR can be divided into repeated cycles of the sensing
period. The sensing period T effectively represents the time interval where sensing is repeated. It
also represents the communication frame in CR. During the sensing time t, a CR device obtains
information from its environment. After the sensing time, the CR device can decide to transmit
data on the same channel or in a new vacant channel, i.e., a spectrum hole.
Figure 2. Simple structure of CR frames based on sensing operation
The decision taken by the CR device is based on the sensing outcome, i.e., the presence or
absence of the PU. The transmission starts after the sensing time until the next sensing period,
also called the CR communication frame. The transmission time (T-t) depends on the sensing
time t and the frame time T that is based on the design of how frequent the sensing will be
conducted. Thus, sensing frequency is 1/T. The sensing time t and the frame time T can be
designed to be fixed for all frames or could be designed to vary based on the design goals [12].
Typically, the sensing operation should be limited and less frequent as much as possible without
affecting the sensing accuracy [13].
Increasing the sensing time t and conducting the sensing more frequently, i.e. decreasing T, lead
to an increase in the probability of correct detection of the PU’s presence. In turn, this leads to
more protection to the PU from interference by CR users and more utilization of the spectrum. On
the other hand, this leads to less data transmission rate and hence to QoS degradation for SUs.
4. 12 Computer Science & Information Technology (CS & IT)
The degradation can be measured by several parameters such as throughput, delay and MAC
layer process overhead [14]. Therefore, designing the sensing time and frequency of sensing
operation should take into account the trade-off between protecting the PU’s QoS and improving
the QoS of SUs.
4. SENSING METHODS
The main challenge facing the sensing methods is how to improve the spectrum sensing
performance by mainly increasing the positive detection probability and decreasing the false
detection probability. A sensing technique with a higher positive detection probability provides
more protection to PU. A CR user with a lower probability of false detection of the presence of
the PU has more chance to use the available spectrum holes. Therefore, the user has more chance
of achieving a higher throughput on the CR network. The design of a sensing technique is
constrained by an acceptable level of false detection [15]. Additionally, improving sensing
performance is challenged by a range of trade-offs and various constraints such as application
requirements, hardware capability, complexity and required infrastructure [16].
In general, a sensing method that uses surrounding RFS information collected by the CR device
only is called a local sensing. If the SUs do not exchange their surrounding RFS information
gathered by local sensing, then this sensing is referred to as non-cooperative sensing. In this
paper, the sensing methods are classified mainly into three categories: methods with no prior
information required, based on prior information and based on SUs cooperation.
4.1. No Prior Information Required (Blind Sensing)
No prior information about the PUs’ signal is necessary for the sensing methods under this
category. However, prior information about the noise power of the targeted spectrum may be
required for better performance. Otherwise, a reasonable estimation of the noise power is used
instead. Two well-known blind sensing methods are energy detection and covariance-based
detection.
4.1.1. Energy Detection
Also known as radiometry or periodogram, energy detection is the most common method for
spectrum sensing because of its low implementation complexity and computational overhead [5].
In this method, the energy detector is used to detect a narrowband spectrum and then the observed
signal energy level is compared with a predefined threshold. Thus, the channel is occupied by the
PU if the detected signal energy is over the threshold. Otherwise, it is considered unoccupied, i.e.,
a spectrum hole. Because of this simplicity, this technique requires the shortest sensing duration t
per frame compared to other common sensing technologies [17].
Generalizing the use of this method faces several challenges as a consequence of its simplicity.
Firstly, selection of the threshold used for detection is an issue when the channel noise level is
unknown or uncertain over time [18]. Secondly, under low SNR, it is hard to differentiate
between modulated signals, including signals of other SUs, noise, and interference, resulting in
poor detection performance [5]. Lastly, an energy detector is ineffective in detecting spread
spectrum signals [19].
4.1.2. Covariance-based Detection
This method is based on comparing the covariance of the detected signal and the covariance of
the noise where statistical covariance matrices of signal and noise are usually different [20]. The
main improvement of this method is to overcome the energy detection shortcoming. In particular,
it can distinguish between signal and noise in a low SNR, and without any prior information
about the PU’s signal and channel noise. This detection improvement is achieved at the expense
5. Computer Science & Information Technology (CS & IT) 13
of adding a computational overhead in computing the covariance matrix of the received signal
samples [11]. In addition to increasing complexity, other drawbacks of the energy detection are
still present in the covariance-based detection.
These sensing methods work with no prior information about the PU signals. They have a limited
performance particularly for spread spectrum and in situations where other SUs are sharing the
spectrum. Research is ongoing to improve the blind sensing approach in terms of performance
and required sensing time, such as in [21, 22].
4.2. Prior Information Required
Methods belonging to this category rely on partial or full information about the PU’s transmission
signal to be able to differentiate it from other signals and noise.
4.2.1. Cyclostationarity Feature Detection
This method is based on distinguishing the PU signal from noise, interference, and other signals
by identifying its cyclostationarity features [23, 24]. These cyclostationarity features are
associated with the signal modulation type, carrier frequency, and data rate. Hence, the CR device
needs sufficient prior information about these unique characteristics of the PU signal. Based on
this information, it can perform a cyclostationarity analysis on the detected signal to identify
matched features [9]. For this method to perform better than the energy detection method, an
adequate number of real-time sample sets in the frequency domain need to be collected. As a
consequence, better performance accrues more complexity and sensing time at the expense of the
available throughput [9].
4.2.2. Correlation Detection
Sensing based on correlation is also known as waveform-based sensing or coherent sensing. In
this method, the expected correlation or coherence between signal samples is identified to detect
the PU signal based on previous knowledge about its waveform patterns [9]. The accuracy of the
sensing increases when the length of the known signal pattern of the PU is increased [25]. The
main drawback of this method is related to the large amount of information required for signal
patterns of the PUs to achieve a high performance that is not practical for all CR systems.
4.2.3. Radio Identification Based Sensing
This method is based on having apriori information about the transmission technologies used by
the PU. In the radio identification stage of the method, several features of the received signal are
exploited and then classified to determine if the signal demonstrates the PU signal technology
[26]. Fundamentally, the feature extraction and classification techniques are used in the context of
European Transparent Ubiquitous Terminal (TRUST) project [27]. For collecting the signal
features, the radio identification method may use one of the known sensing techniques, such as
the energy detection method [9]. The radio identification improves the accuracy of the energy
detection to some extent with complexity implication. The achieved precision is dependent on the
signal features and classification techniques used to identify the presence of the PU.
4.2.4. Matched Filtering
The matched filtering method achieves a higher detection probability in a short detection time,
compared to other methods that are similarly based on prior information [28, 29]. Hence, under
this classification, this method is considered as the best sensing method. The collected signal is
passed through a filter that will amplify the possible PU signal and attenuate any noise signal. The
filter makes the detection of the presence of the PU signal more accurate [29]. The filter, which
is known as a matched filter, has to be tuned based on some features of the PU signal. These
6. 14 Computer Science & Information Technology (CS & IT)
characteristics include the required bandwidth, operating frequency, the modulation used and
frame format [9]. One of the disadvantages of this method is in implementation where different
PUs signal types require different dedicated hardware receivers. This requirement makes the
method impractical to implement and also leads to higher power consumptions if the method is
implemented based on current hardware technologies.
Figure 3 shows a comparison between non-cooperative sensing methods, based on accuracy and
complexity metrics. Table 1 shows more comparison factors between local sensing methods.
Figure 3. Sensing method complexity versus accuracy
Table 1. Comparison between local sensing methods
4.3. Based on SU Cooperation
The main principle of this approach is that SUs share their local sensed information of the
spectrum. The use of sensed information from all SUs can produce a more accurate sensing
outcome than relying solely on local sensing. The hidden transmitter problem is an example of
the issues that may prohibit a CR from detecting the presence of a PU. The cause of this problem
is the fading and shadowing of the signals from a PU, although it is within the transmission range
of the CR [9]. However, when cooperated SUs are spatially distributed, it helps to overcome the
hidden PU problem and other limitations of local sensing [30]. Sensing cooperation can also
reduce the local sensing cost, e.g., sensing time duration and energy consumption while
maintaining sensing quality by scheduling the sensing operation among cooperative SUs [31].
7. Computer Science & Information Technology (CS & IT) 15
The sensing method used by an individual SU can be based on one of the sensing methods for
local sensing, such as energy detection and cyclostationarity feature detection [10].
In some environments, cooperative sensing may lose its advantages as far as an individual SU is
concerned. For instance, increasing the local sensing frequency in individual high mobility SUs
is more efficient, in terms of sensing accuracy and overhead, than to cooperate with other SUs
[19]. In cooperative sensing, the improvement of sensing is more noticeable when the number of
cooperative SUs is increased. However, involvement of more SUs will increase the cooperation
overhead in terms of the amount of data exchange and the time required for the exchange [32].
The cooperative approaches can only be used when SUs are able and willing to collaborate. Also,
a SU may not always find other cooperative SUs within its transmission range. Therefore, the CR
devices should not solely rely on cooperative sensing approaches. They should be able to use a
fitting local sensing method and resort to cooperative sensing, only when an enhanced
performance is possible.
5. FACTORS FOR SELECTING THE FITTING SENSING METHOD
Selecting the best sensing method for a particular cognitive radio operation condition depends on
several factors. Based on the discussions in previous sections, notable factors are summarized
below:
5.1. CR Device Capability
A CR device designed with limited hardware resources and power capacities will not be able to
support a wider range of sensing methods. Some methods require sophisticated hardware
components and higher power consumption, e.g. the matched filter method, compared to simple
ones such as the energy detection method. An ideal CR device should be able to be reconfigured
on-the-fly to support a broad range of sensing methods. In practice, a CR device’s actual
capability will limit the range of sensing methods that can be supported.
5.2. Qos Required for Applications Running on the CR Device
The QoS requirements differ based on the applications running on a CR device. The sensing
delay and transmission throughput vary from one sensing method to another within the same
conditions. As a result, the sensing operation used on a CR device has a direct impact on the QoS
of an application running on the device, mainly in terms of the throughput and delay. As sensing
is a repetitive operation, a CR device should be able to select a proper sensing method with the
least impact on the QoS of the running application. Other operational requirements must also be
taken into account. For example, the PU protection should have a higher priority than the QoS
requirements of a CR user.
5.3. Apriori Information
The extent of information available about the characteristics of the PUs and the communications
media is a major factor influencing the selection of a proper sensing method. For instance,
insufficient information about the PU signals, excludes the use of matched filter method.
The CR device should be able to change the sensing method based on the information that
becomes available about the PU signal or the SNR of the targeted spectrum by sensing.
8. 16 Computer Science & Information Technology (CS & IT)
5.4. Level of Protection Required for PU
The selection of the sensing method must be considered with regard to the degree of protection
necessary for the PU. They may vary depending on available frequency bands and types of
services. For instance, analog TV service is more robust against interference than digital TV
service [16]. Hence, a sensing method that provides less protection, i.e., lower PU detection
probability, should only be used when the PU is more tolerant of interference such as in analog
TV services.
5.5. The CR Network Mode and Capability
The network mode and capability are important factors to CR systems to make a decision
between cooperative and non-cooperative sensing approaches. In CR networks with infrastructure
and centralized topology, a method based on cooperative sensing is more suitable than that based
on local sensing only. Hence, the capability of such a CR network depends on how much
management ability can provide for white space determination to its CR devices. Furthermore, the
capacity of a CR network relies on how much information the network can gather and provide to
its CR users about the PU signals and the ambient spectrum.
6. CONCLUSIONS
The work reported in this paper asserts that none of the available spectrum sensing techniques can
achieve perfect solutions for all potential CR operating conditions. Therefore, to improve the
performance of CR systems, the relevant devices must be capable of utilizing a catalog of sensing
methods. The selection of the most suitable method from the catalog, which is an obvious
necessity, is based on a number of factors that have also been identified and discussed in this
paper. Our future works will focus on more exhaustive evaluations of these factors and how their
fine-tunings can contribute to an improved CR performance.
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AUTHORS
Nabil Giweli received the B.Sc. degree in Communication Engineering from
Tripoli University, Libya, in 1997, the Master degree in Information and
Communication Technology (with the dean medal award) from the Western
Sydney University in 2011, and another M.Sc. form the same university in
Cloud Security in 2013. Currently, he is a Ph.D. candidate and a casual teacher
at the School of Computing, Engineering and Mathematics, Western Sydney
University, Australia. His current research area is in Cognitive Radio
Technologies.
Seyed Shahrestani completed his PhD degree in Electrical and Information
Engineering at the University of Sydney. He joined Western Sydney
University (Western) in 1999, where he is currently a Senior Lecturer. He is
also the head of the Networking, Security and Cloud Research (NSCR) group
at Western. His main teaching and research interests include: computer
networking, management and security of networked systems, analysis, control
and management of complex systems, artificial intelligence applications, and
health ICT. He is also highly active in higher degree research training
supervision, with successful results.
11. Computer Science & Information Technology (CS & IT) 19
Dr Hon Cheung graduated from The University of Western Australia in 1984
with First Class Honours in Electrical Engineering. He received his PhD degree
from the same university in 1988. He was a lecturer in the Department of
Electronic Engineering, Hong Kong Polytechnic from 1988 to 1990. From 1990
to 1999, he was a lecturer in Computer Engineering at Edith Cowan University,
Western Australia. He has been a senior lecturer in Computing at Western
Sydney University since 2000. Dr Cheung has research experience in a number
of areas, including conventional methods in artificial intelligence, fuzzy sets,
artificial neural networks, digital signal processing, image processing, network security and forensics, and
communications and networking. In the area of teaching, Dr Cheung has experience in development and
delivery of a relative large number of subjects in computer science, electrical and electronic engineering,
computer engineering and networking.