- University of Cape Town
- 9705245720
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In wireless communication networks, the physical layer security is of prime concern which is mainly effected due to anomaly/malicious users. Cognitive radio network (CRN) is one of the next generation wireless networks, recently emerged... more
In wireless communication networks, the physical layer security is of prime concern which is mainly effected due to anomaly/malicious users. Cognitive radio network (CRN) is one of the next generation wireless networks, recently emerged as a technology that can use unoccupied licensed frequency bands temporarily without taking permission from spectrum regulatory bodies. Owing to inherent nature of unlicensed access, CRNs are more likely to anomaly user effects that degrades the Quality of service (QoS) of both licensed and cognitive user communications. These effects are generally taken place during spectrum sensing. To eliminate single anomaly effects and improve accuracy of sensing decision by applying spatial diversity concept, cooperative sensing techniques have been developed. However, the detection probability of cooperative sensing degrades owing to the presence of multiple anomaly user in the network. In particular, due to spectrum sensing data falsification (SSDF) attacks. ...
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Sensing the radio frequency bands is an open problem for dynamic spectrum access (DSA) in cognitive radio networks. To address this, numerous signal processing algorithms have been described based on entropy measurement. However, most of... more
Sensing the radio frequency bands is an open problem for dynamic spectrum access (DSA) in cognitive radio networks. To address this, numerous signal processing algorithms have been described based on entropy measurement. However, most of the methods lack connectivity between signal detection and appropriateness of the selected entropy. In this work, we propose an optimal detection method which estimates the degree of chaotic nature of Digital Television (DTV) signal based on Approximate entropy (ApEn). The optimal required input parameter of rt is estimated based on heuristic search algorithm to maximize the detection probability. The results reveal that the optimal ApEn with adaptive threshold can be used as an efficient method for spectrum sensing.
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Page 1. FPGA implementation of Cooperative Spectrum Sensing for Cognitive Radio Networks S.Srinu, Samrat L.Sabat School of Physics University of Hyderabad Hyderabad, 500046 Email: ph08pe02@uohyd.emet.in, slssp@uohyd.emetin ...
Research Interests: Computer Science, Field-Programmable Gate Arrays, Cognitive radio, Spectrum Sensing, FPGA implementation, and 9 moreField Programmable Gate Array, Cognitive wireless systems, Energy Detection, Hardware in Loop, Signal to Noise Ratio, Cognitive Radio Network, Probability of Detection, Simulation Technique, and Fusion center
Spectrum sensing is one of the most important and challenging task in cognitive radio network. Its main functionality is to detect the presence of primary user signal and to be cognizant about its surrounding radio spectrum. In this... more
Spectrum sensing is one of the most important and challenging task in cognitive radio network. Its main functionality is to detect the presence of primary user signal and to be cognizant about its surrounding radio spectrum. In this paper, perfomrance of single node spectrum sensing based on energy and cyclostationary feature detection algorithms are compared for different signal to noise
Research Interests: Computer Science, Field-Programmable Gate Arrays, Software Defined Radio, Cognitive radio, Spectrum Sensing, and 10 moreCase Study, Feature Detection, Primary User, Field Programmable Gate Array, Spectrum, Energy Detection, Hardware in Loop, Signal to Noise Ratio, Resource Utilization, and Cognitive Radio Network
This work presents a new spectrum sensing technique based on entropy estimation of autocorrelation estimates of received signal at different cyclic frequencies. The performance of the proposed entropy detection is compared with... more
This work presents a new spectrum sensing technique based on entropy estimation of autocorrelation estimates of received signal at different cyclic frequencies. The performance of the proposed entropy detection is compared with cyclostationary detection based on spectral coherence function (SCF) and energy detection methods. Both the algorithms are verified under single node and multinode/cooperative environment. Sensing performance of both the
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ABSTRACT In Cognitive Radio (CR), spectrum sensing is an essential concept. It exploits the inefficient utilization of Radio spectrum without deteriorating the Quality of Service (QOS) of licensed/primary user communication. This paper... more
ABSTRACT In Cognitive Radio (CR), spectrum sensing is an essential concept. It exploits the inefficient utilization of Radio spectrum without deteriorating the Quality of Service (QOS) of licensed/primary user communication. This paper presents the cooperative wideband spectrum sensing technique based on entropy estimation in each subband under probable channel impediments. We have proposed generalized extreme studentized deviate (GESD) test to exclude multiple suspicious cognitive users for reliable cooperative sensing. The simulation results show that the entropy detection method outperforms the energy detection method. It achieved 8dB average signal-to-noise ratio (SNR) improvement while maintaining a false alarm probability of 0.01 and a detection probability of 0.9. The proposed sensing algorithm by excluding suspicious CR is able to detect low average SNR signals of -18.5dB under path loss environment using ten cognitive users in cooperation.
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ABSTRACT Spectrum sensing is a vital phase in Cognitive Radio (CR) to identify the unutilized spectrum for improving the spectrum utilization. Cooperative sensing is being used for spectrum sensing to mitigate the effect of shadowing and... more
ABSTRACT Spectrum sensing is a vital phase in Cognitive Radio (CR) to identify the unutilized spectrum for improving the spectrum utilization. Cooperative sensing is being used for spectrum sensing to mitigate the effect of shadowing and fading in the channel. In cooperative sensing, the channels to be sensed by cognitive users are assumed to be noisy. Moreover, channel noise is also presents in between CR users and fusion center which reduces the cooperative sensing detection accuracy. In this paper, we studied the effect of noise in the control channel on detection probability and used forward error correction technique with convolutional encoder to mitigate the effect of control channel noise. Energy detection based on Neyman-Pearson criteria is used in each CR and sensing performance is analyzed using Monte-Carlo methods. The simulations are carried out with different signal-to-noise ratio (SNR) in the control channel with and without convolutional coding. The results reveal that the detection probability of the algorithm improves significantly with convolutional coding.
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Page 1. ICCCCT-10 FPGA implementation of Spectrum Sensing based on Energy detection for Cognitive Radio S.Srinu, Samrat L. Sabat School of Physics University of Hyderabad Hyderabad, 500046 Email:... more
Page 1. ICCCCT-10 FPGA implementation of Spectrum Sensing based on Energy detection for Cognitive Radio S.Srinu, Samrat L. Sabat School of Physics University of Hyderabad Hyderabad, 500046 Email: ph08pe02@uohyd.ernet.in.slssp@uohyd.ernet.in ...
Research Interests: Computer Science, Field-Programmable Gate Arrays, Cognitive radio, Spectrum Sensing, FPGA implementation, and 10 moreRayleigh fading channel, Sample Size, Primary User, Field Programmable Gate Array, Energy Detection, Hardware in Loop, Signal to Noise Ratio, Probability of Detection, Energy Detector, and Additive White Gaussian Noise
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ABSTRACT This study presents a cooperative wideband spectrum-sensing technique based on the entropy of the received signal in the frequency domain. To enhance the efficiency of detection, multiple suspicious cognitive users are excluded... more
ABSTRACT This study presents a cooperative wideband spectrum-sensing technique based on the entropy of the received signal in the frequency domain. To enhance the efficiency of detection, multiple suspicious cognitive users are excluded from the network using generalised extreme studentised deviate test and sigma limit test. The performance of the proposed technique is compared with the energy detection technique under different channel environments. The simulation results demonstrate that the entropy detection method, with excluded suspicious users, has 9 dB improvement in performance compared to the energy detection technique in extremely low signal-to-noise ratio regime. This is a significant improvement for cognitive radio systems. The simulations were performed with false alarm probability and detection probability of 0.01 and 0.9, respectively. The hardware-in-the-loop simulation of the proposed algorithm is further carried out in the Xilinx Virtex-4 Field Programmable Gate Array.
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Spectrum sensing is an essential concept in Cognitive Radio (CR) systems. It exploits the inefficient utilization of radio frequency spectrum without causing destructive interference to the licensed/primary user communication. In recent... more
Spectrum sensing is an essential concept in Cognitive Radio (CR) systems. It exploits the inefficient utilization of radio frequency spectrum without causing destructive interference to the licensed/primary user communication. In recent past, most of the studies on spectrum sensing are focused on cooperative/multinode detection approaches. However, they are confined to the detection of signals in a single frequency band or
Research Interests: Computer Science, Field-Programmable Gate Arrays, Cognitive radio, Probability, Spectrum Sensing, and 11 moreFPGA implementation, Radio Frequency, Sample Size, Primary User, Field Programmable Gate Array, Spectrum, Signal Detection, Energy Detection, Signal to Noise Ratio, Cognitive Radio Network, and False alarm probability
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Distinguishing deterministic signal from noise in radio spectrum to detect white spaces for cognitive radio communication is vital task. To address this, quite a few sensing algorithms have been developed based on entropy measurement.... more
Distinguishing deterministic signal from noise in radio spectrum to detect white spaces for cognitive radio communication is vital task. To address this, quite a few sensing algorithms have been developed based on entropy measurement. However, most of them focused only on the information content in primary user transmitted signal and ignored the hidden complexity. Hence, in this work, the techniques that quantify hidden complexity in the signal rather than only information are studied using real-time Digital Television (DTV) signals. To quantify complexity, a test statistic is developed based on linear combination of sample entropy (SaEn(LC)) at different tolerance (rt) values. Furthermore, weighted collaborative detection method based on SaEn(LC) and fractal dimension measure is proposed to improve the detection accuracy by mitigating noise encountered by single user. The results reveal that the proposed method with five nodes can detect signals up to −23dB signal-to-noise ratio.
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Wireless communication network (WCN) performance is primarily depends on physical layer security which is critical among all other layers of OSI network model. It is typically prone to anomaly/malicious user’s attacks owing to openness of... more
Wireless communication network (WCN) performance is primarily depends on physical layer security which is critical among all other layers of OSI network model. It is typically prone to anomaly/malicious user’s attacks owing to openness of wireless channels. Cognitive radio networking (CRN) is a recently emerged wireless technology that is having numerous security challenges because of its unlicensed access of wireless channels. In CRNs, the security issues occur mainly during spectrum sensing and is more pronounced during distributed spectrum sensing. In recent past, various anomaly effects are modelled and developed detectors by applying advanced statistical techniques. Nevertheless, many of these detectors have been developed based on sensing data of one variable (energy measurement) and degrades their performance drastically when the data is contaminated with multiple anomaly nodes, that attack the network cooperatively. Hence, one has to develop an efficient multiple anomaly detection algorithm to eliminate all possible cooperative attacks. To achieve this, in this work, the impact of anomaly on detection probability is verified beforehand in developing an efficient algorithm using bivariate data to detect possible attacks with mahalanobis distance measure. Result discloses that detection error of cooperative attacks by anomaly has significant impact on eigenvalue-based sensing.
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This letter explores covariance matching based adaptive robust cubature Kalman filter (CMRACKF). In this method, the innovation sequence is used to determine the covariance matrix of measurement noise that can overcome the limitation of... more
This letter explores covariance matching based adaptive robust cubature Kalman filter (CMRACKF). In this method, the innovation sequence is used to determine the covariance matrix of measurement noise that can overcome the limitation of conventional CKF. In the proposed algorithm, weights are adaptively adjust and used for updating the measurement noise covariance matrices online. It can also enhance the adaptive capability of the ACKF. The simulation results are illustrated to evaluate the performance of the proposed algorithm.
Spectrum sensing in the low signal-to-noise ratio (SNR) environment is vital task for the evolution of cognitive radio technology. The numerous signal processing algorithms have since been proposed to improve the spectrum sensing... more
Spectrum sensing in the low signal-to-noise ratio (SNR) environment is vital task for the evolution of cognitive radio technology. The numerous signal processing algorithms have since been proposed to improve the spectrum sensing performance. In the recent past, entropy based sensing methods are shown to be robust in a low SNR environment with small data sets. However, these methods only focus on information content and ignore temporal order of the signal. Hence, selection of appropriate entropy technique that considers both information content and temporal order is important. In addition, many works consider that the distribution of noise follows Gaussian under assumption that the sample size is infinity. The detection threshold designed using this assumption yield unreliable decisions. On the contrary, the captured data is limited in real-time and it should be minimum to reduce the computational complexity. To address these two issues, empirical permutation entropy with adaptive thresholding detection technique is proposed. Then, the work is extended to weighted gain cooperative sensing that uses Higuchi fractal dimension method to generate weight for each node. Simulation results reveal that the proposed method is robust, less sensitive to sample size, and improves the single node as well as multinode sensing performance.
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Over the past decade, distinguishing deterministic signal from noise in the radio frequency bands has become an active issue in the evolution of cognitive radio technology. To address this problem, numerous signal processing algorithms... more
Over the past decade, distinguishing deterministic signal from noise in the radio frequency bands has become an active issue in the evolution of cognitive radio technology. To address this problem, numerous signal processing algorithms have been described based on entropy/complexity measurement. However, the methods that depend on finite empirical data lack connectivity between signal detection and appropriateness of the selected entropy technique. In this work, we investigate and characterize the chaotic nature of received signal based on Lyapunov exponents. After observing a seemingly chaotic nature, appropriate entropy measures are investigated to estimate the degree of chaosness in the signal. Based on comparative study, an optimal approximate entropy (OApEn) based detection technique is proposed. It estimates the optimal adaptive tolerance ðr t Þ using a heuristic search algorithm. The work is extended to cooperative sensing by introducing weighted gain combining based on fractal dimension (WGCFD) technique. The results reveal that the WGCFD with the OApEn algorithm can detect received signals up to À22 dB with five nodes in cooperation. It outperforms the other detection methods frequently used for signal detection.
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Cooperative spectrum sensing is a process of achieving spatial diversity gain to make global decision for cognitive radio networks. However, accuracy of global decision effects owing to the presence of malicious users/nodes during... more
Cooperative spectrum sensing is a process of achieving spatial diversity gain to make global decision for cognitive radio networks. However, accuracy of global decision effects owing
to the presence of malicious users/nodes during cooperative sensing. In this work, an
extended generalized extreme studentized deviate (EGESD) method is proposed to eliminate malicious nodes such as random nodes and selfish nodes in the network. The random
nodes are carried off based on sample covariance of each node decisions on different
frames. Then, the algorithm checks the normality of updated soft data using Shapiro–Wilk test and estimates the expected number of malicious users in cooperative sensing. These are the two essential input parameters required for classical GESD test to eliminate
significant selfish nodes accurately. Simulation results reveal that the proposed algorithm can eliminate both random and frequent spectrum sensing data falsification (SSDF) attacks
in cooperative sensing and outperforms the existing algorithms.
to the presence of malicious users/nodes during cooperative sensing. In this work, an
extended generalized extreme studentized deviate (EGESD) method is proposed to eliminate malicious nodes such as random nodes and selfish nodes in the network. The random
nodes are carried off based on sample covariance of each node decisions on different
frames. Then, the algorithm checks the normality of updated soft data using Shapiro–Wilk test and estimates the expected number of malicious users in cooperative sensing. These are the two essential input parameters required for classical GESD test to eliminate
significant selfish nodes accurately. Simulation results reveal that the proposed algorithm can eliminate both random and frequent spectrum sensing data falsification (SSDF) attacks
in cooperative sensing and outperforms the existing algorithms.