The Experts below are selected from a list of 5760 Experts worldwide ranked by ideXlab platform
Yonghong Zeng - One of the best experts on this subject based on the ideXlab platform.
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On the Eigenvalue-Based Spectrum Sensing and Secondary User Throughput
IEEE Transactions on Vehicular Technology, 2014Co-Authors: Ayse Kortun, Tharmalingam Ratnarajah, Mathini Sellathurai, Ying-chang Liang, Yonghong ZengAbstract:In this paper, we study the tradeoff between sensing time and achievable throughput of the secondary user that employs robust Eigenvalue-based spectrum sensing techniques in the presence of noise uncertainty. First, we study exact distributions of the test statistics for two types of robust Eigenvalue-based sensing techniques, namely, the blind generalized likelihood ratio test (B-GLRT) detection and energy with Minimum Eigenvalue (EME) detection. The developed threshold setting is more accurate than benchmark methods in achieving a target constant false alarm rate (CFAR). Second, prior to the throughput analysis, the necessary asymptotic detection and false alarm probabilities under noise uncertainty are formulated for Eigenvalue-based detectors such as maximum Eigenvalue detection (MED) and maximum-Minimum Eigenvalue (MME) detection. Finally, the throughput is maximized using Eigenvalue-based spectrum sensing techniques which are B-GLRT, EME, MME, and MED detectors. The results are compared with the commonly used energy detector (ED). An improved achievable throughput is obtained under low-signal-to-noise-ratio (SNR) regime by incorporating the robust Eigenvalue-based techniques, which are insusceptible to noise uncertainty.
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Eigenvalue based spectrum sensing algorithms for cognitive radio
arXiv: Information Theory, 2008Co-Authors: Yonghong Zeng, Ying-chang LiangAbstract:Spectrum sensing is a fundamental component is a cognitive radio. In this paper, we propose new sensing methods based on the Eigenvalues of the covariance matrix of signals received at the secondary users. In particular, two sensing algorithms are suggested, one is based on the ratio of the maximum Eigenvalue to Minimum Eigenvalue; the other is based on the ratio of the average Eigenvalue to Minimum Eigenvalue. Using some latest random matrix theories (RMT), we quantify the distributions of these ratios and derive the probabilities of false alarm and probabilities of detection for the proposed algorithms. We also find the thresholds of the methods for a given probability of false alarm. The proposed methods overcome the noise uncertainty problem, and can even perform better than the ideal energy detection when the signals to be detected are highly correlated. The methods can be used for various signal detection applications without requiring the knowledge of signal, channel and noise power. Simulations based on randomly generated signals, wireless microphone signals and captured ATSC DTV signals are presented to verify the effectiveness of the proposed methods.
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maximum Minimum Eigenvalue detection for cognitive radio
Personal Indoor and Mobile Radio Communications, 2007Co-Authors: Yonghong Zeng, Ying-chang LiangAbstract:Sensing (signal detection) is a fundamental problem in cognitive radio. In this paper, a new method is proposed based on the Eigenvalues of the covariance matrix of the received signal. It is shown that the ratio of the maximum Eigenvalue to the Minimum Eigenvalue can be used to detect the signal existence. Based on some latest random matrix theories (RMT), we can quantize the ratio and find the threshold. The probability of false alarm is also found by using the RMT. The proposed method overcomes the noise uncertainty difficulty while keeps the advantages of the energy detection. The method can be used for various sensing applications without knowledge of the signal, the channel and noise power. Simulations based on randomly generated signals and captured ATSC DTV signals are presented to verify the methods.
Ying-chang Liang - One of the best experts on this subject based on the ideXlab platform.
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On the Eigenvalue-Based Spectrum Sensing and Secondary User Throughput
IEEE Transactions on Vehicular Technology, 2014Co-Authors: Ayse Kortun, Tharmalingam Ratnarajah, Mathini Sellathurai, Ying-chang Liang, Yonghong ZengAbstract:In this paper, we study the tradeoff between sensing time and achievable throughput of the secondary user that employs robust Eigenvalue-based spectrum sensing techniques in the presence of noise uncertainty. First, we study exact distributions of the test statistics for two types of robust Eigenvalue-based sensing techniques, namely, the blind generalized likelihood ratio test (B-GLRT) detection and energy with Minimum Eigenvalue (EME) detection. The developed threshold setting is more accurate than benchmark methods in achieving a target constant false alarm rate (CFAR). Second, prior to the throughput analysis, the necessary asymptotic detection and false alarm probabilities under noise uncertainty are formulated for Eigenvalue-based detectors such as maximum Eigenvalue detection (MED) and maximum-Minimum Eigenvalue (MME) detection. Finally, the throughput is maximized using Eigenvalue-based spectrum sensing techniques which are B-GLRT, EME, MME, and MED detectors. The results are compared with the commonly used energy detector (ED). An improved achievable throughput is obtained under low-signal-to-noise-ratio (SNR) regime by incorporating the robust Eigenvalue-based techniques, which are insusceptible to noise uncertainty.
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Eigenvalue based spectrum sensing algorithms for cognitive radio
arXiv: Information Theory, 2008Co-Authors: Yonghong Zeng, Ying-chang LiangAbstract:Spectrum sensing is a fundamental component is a cognitive radio. In this paper, we propose new sensing methods based on the Eigenvalues of the covariance matrix of signals received at the secondary users. In particular, two sensing algorithms are suggested, one is based on the ratio of the maximum Eigenvalue to Minimum Eigenvalue; the other is based on the ratio of the average Eigenvalue to Minimum Eigenvalue. Using some latest random matrix theories (RMT), we quantify the distributions of these ratios and derive the probabilities of false alarm and probabilities of detection for the proposed algorithms. We also find the thresholds of the methods for a given probability of false alarm. The proposed methods overcome the noise uncertainty problem, and can even perform better than the ideal energy detection when the signals to be detected are highly correlated. The methods can be used for various signal detection applications without requiring the knowledge of signal, channel and noise power. Simulations based on randomly generated signals, wireless microphone signals and captured ATSC DTV signals are presented to verify the effectiveness of the proposed methods.
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maximum Minimum Eigenvalue detection for cognitive radio
Personal Indoor and Mobile Radio Communications, 2007Co-Authors: Yonghong Zeng, Ying-chang LiangAbstract:Sensing (signal detection) is a fundamental problem in cognitive radio. In this paper, a new method is proposed based on the Eigenvalues of the covariance matrix of the received signal. It is shown that the ratio of the maximum Eigenvalue to the Minimum Eigenvalue can be used to detect the signal existence. Based on some latest random matrix theories (RMT), we can quantize the ratio and find the threshold. The probability of false alarm is also found by using the RMT. The proposed method overcomes the noise uncertainty difficulty while keeps the advantages of the energy detection. The method can be used for various sensing applications without knowledge of the signal, the channel and noise power. Simulations based on randomly generated signals and captured ATSC DTV signals are presented to verify the methods.
Mathini Sellathurai - One of the best experts on this subject based on the ideXlab platform.
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optimal decision threshold for Eigenvalue based spectrum sensing techniques
International Conference on Acoustics Speech and Signal Processing, 2014Co-Authors: Tharmalingam Ratnarajah, Jiang Xue, Ebtihal H G Yousif, Mathini SellathuraiAbstract:This paper investigates optimization of the sensing threshold that minimizes the total error rate (i.e., the sum of the probabilities of false alarm and missed detection) of Eigenvalue-based spectrum sensing techniques for multiple-antenna cognitive radio networks. Four techniques are investigated, which are maximum Eigenvalue detection (MED), maximum Minimum Eigenvalue (MME) detection, energy with Minimum Eigenvalue (EME) detection, and the generalized likelihood ratio test (GLRT) detection. The contribution of this paper is of four parts. Firstly, we present the derivative of the matrix-variate confluent hypergeometric function, which is required for the MED case. Secondly, we derive the probabilities of false alarm for both cases MME and EME detection. Thirdly, we derive the probability of missed detection for the GLRT detector. Finally, we provide the exact expressions required to obtain the optimal sensing thresholds for all cases. The simulation results reveal that for all the investigated cases the chosen optimal sensing thresholds achieve the Minimum total error rate.
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On the Eigenvalue-Based Spectrum Sensing and Secondary User Throughput
IEEE Transactions on Vehicular Technology, 2014Co-Authors: Ayse Kortun, Tharmalingam Ratnarajah, Mathini Sellathurai, Ying-chang Liang, Yonghong ZengAbstract:In this paper, we study the tradeoff between sensing time and achievable throughput of the secondary user that employs robust Eigenvalue-based spectrum sensing techniques in the presence of noise uncertainty. First, we study exact distributions of the test statistics for two types of robust Eigenvalue-based sensing techniques, namely, the blind generalized likelihood ratio test (B-GLRT) detection and energy with Minimum Eigenvalue (EME) detection. The developed threshold setting is more accurate than benchmark methods in achieving a target constant false alarm rate (CFAR). Second, prior to the throughput analysis, the necessary asymptotic detection and false alarm probabilities under noise uncertainty are formulated for Eigenvalue-based detectors such as maximum Eigenvalue detection (MED) and maximum-Minimum Eigenvalue (MME) detection. Finally, the throughput is maximized using Eigenvalue-based spectrum sensing techniques which are B-GLRT, EME, MME, and MED detectors. The results are compared with the commonly used energy detector (ED). An improved achievable throughput is obtained under low-signal-to-noise-ratio (SNR) regime by incorporating the robust Eigenvalue-based techniques, which are insusceptible to noise uncertainty.
Rahul Shrestha - One of the best experts on this subject based on the ideXlab platform.
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hardware efficient and fast sensing time maximum Minimum Eigenvalue based spectrum sensor for cognitive radio network
IEEE Transactions on Circuits and Systems I-regular Papers, 2019Co-Authors: Rohit B Chaurasiya, Rahul ShresthaAbstract:This paper proposes an implementation-friendly maximum-Minimum-Eigenvalue (MME)-based spectrum sensing algorithm for cognitive radio network. An iterative power method has been applied for the first time to compute maximum and Minimum Eigenvalues that reduces the computational complexity of this MME algorithm. We suggest new digital architecture of MME-based spectrum sensor with shorter critical-path delay that lowers its sensing time. This spectrum-sensor architecture has been resource shared to further enhance the hardware efficiency. Performance analysis of the suggested MME algorithm with 1024 input-signal samples delivers adequate performance at −10 dB of SNR with the detection probability of 0.8. Suggested MME algorithm performs better than the energy detection-based spectrum sensing under noise uncertainty. It has detection gain of 5.3 dB compared to the cyclostationary feature detection-based spectrum sensing algorithm at 0.7 detection probability. Hardware prototyping of our MME spectrum sensor has been carried out in FPGA platform and its real-time testing is performed using the communication environment of DVB-T standard. We synthesized and post-layout simulated proposed digital sensor in 90 nm-CMOS process that resulted in 0.42 mm2 of area, operating at a maximum clock frequency of 404 MHz which results in the sensing time of $53.5~\mu \text{s}$ . Comparison of our work with literature shows that the suggested MME spectrum-sensor has $2.5\times $ shorter sensing time and lowest area-time-product of 0.023, indicating better hardware-efficiency, than the state-of-the-art implementations.
Tharmalingam Ratnarajah - One of the best experts on this subject based on the ideXlab platform.
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optimal decision threshold for Eigenvalue based spectrum sensing techniques
International Conference on Acoustics Speech and Signal Processing, 2014Co-Authors: Tharmalingam Ratnarajah, Jiang Xue, Ebtihal H G Yousif, Mathini SellathuraiAbstract:This paper investigates optimization of the sensing threshold that minimizes the total error rate (i.e., the sum of the probabilities of false alarm and missed detection) of Eigenvalue-based spectrum sensing techniques for multiple-antenna cognitive radio networks. Four techniques are investigated, which are maximum Eigenvalue detection (MED), maximum Minimum Eigenvalue (MME) detection, energy with Minimum Eigenvalue (EME) detection, and the generalized likelihood ratio test (GLRT) detection. The contribution of this paper is of four parts. Firstly, we present the derivative of the matrix-variate confluent hypergeometric function, which is required for the MED case. Secondly, we derive the probabilities of false alarm for both cases MME and EME detection. Thirdly, we derive the probability of missed detection for the GLRT detector. Finally, we provide the exact expressions required to obtain the optimal sensing thresholds for all cases. The simulation results reveal that for all the investigated cases the chosen optimal sensing thresholds achieve the Minimum total error rate.
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On the Eigenvalue-Based Spectrum Sensing and Secondary User Throughput
IEEE Transactions on Vehicular Technology, 2014Co-Authors: Ayse Kortun, Tharmalingam Ratnarajah, Mathini Sellathurai, Ying-chang Liang, Yonghong ZengAbstract:In this paper, we study the tradeoff between sensing time and achievable throughput of the secondary user that employs robust Eigenvalue-based spectrum sensing techniques in the presence of noise uncertainty. First, we study exact distributions of the test statistics for two types of robust Eigenvalue-based sensing techniques, namely, the blind generalized likelihood ratio test (B-GLRT) detection and energy with Minimum Eigenvalue (EME) detection. The developed threshold setting is more accurate than benchmark methods in achieving a target constant false alarm rate (CFAR). Second, prior to the throughput analysis, the necessary asymptotic detection and false alarm probabilities under noise uncertainty are formulated for Eigenvalue-based detectors such as maximum Eigenvalue detection (MED) and maximum-Minimum Eigenvalue (MME) detection. Finally, the throughput is maximized using Eigenvalue-based spectrum sensing techniques which are B-GLRT, EME, MME, and MED detectors. The results are compared with the commonly used energy detector (ED). An improved achievable throughput is obtained under low-signal-to-noise-ratio (SNR) regime by incorporating the robust Eigenvalue-based techniques, which are insusceptible to noise uncertainty.