Gerschgorin Radii Research Articles

Performance Analysis of Robust GRCR Based Spectrum Detector Using Compressed Sensing with Non-reconstruction Model

In cognitive radio applications, a robust detector is essentially required for the determination of spectrum sensing. Gerschgorin radii and centers ratio (GRCR) detector is a robust detector for cooperative spectrum sensing techniques. Covariance matrix generates the test statistics for signal established from one or supplementary sources. Though the method is robust against noise uncertainty, it is not suitable for wideband sensing due to the complexity associated with the computation of covariance matrix. To tackle this challenge of extensive communication cost and high processing time complexity, an efficient GRCR detector using compressive sensing with non-reconstruction is proposed here. This method introduces relevance for multiple received signals by using same measurement matrix to all received signals. Computational complexity is analysed and the proposed method is compared with the existing method through ROC simulations, and it shown that the proposed method performs better even in the low SNR range of -20 dB. Throughput analysis is validated through simulations.

Correlated Source Number Estimation with Gerschgorin Radii of Partitioned Matrices Products

In this paper, a partitioned matrices products approach based on Gerschgorin disk estimation (PM-GDE) method is proposed in order to solve the problem of correlated source number estimation under spatially correlated noise background. This method is based on a uniform linear array. The improved partitioned matrices products of the array receiving data are computed by weighted summation of the rows and adjustment of the element sequence from partitioned matrices products matrix. The auto correlation matrix of the partitioned matrices products and its Gerschgorin radii are computed. A source number estimation criterion based on Gerschgorin radii is deduced by considering the second-order statistical property of the partitioned matrices products auto correlation matrix. Simulations results validate the efficiency and robustness of PM-GDE with correlated signals and spatially correlated noise background. PM-GDE performs better than its counterpart especially for close incident azimuth correlated sources.

Performance of the Gerschgorin Radii and Centers Ratio Detector for Cooperative Spectrum Sensing under Burst Control Channel Errors

In centralized cooperative spectrum sensing (CSS), cognitive radios (CRs) monitor the spectrum and send the related data to a fusion center (FC) via a control channel in order to more efficiently detect possible idle bands for opportunistic occupation. In centralized cooperative spectrum sensing (CSS), cognitive radios (CRs) monitor the spectrum and send the related data to a fusion center (FC) via a control channel in order to more efficiently detect possible idle bands for opportunistic occupation. In practice, such data must be quantized prior to transmission, which can lead to loss of the global spectrum sensing performance due to quantization errors. Additionally, wireless control channel impairments, such as the multipath fading, further contributes to performance degradation. In many researches, the control channel is considered error-free. Even when errors are considered, they usually affect the transmitted symbols independently, that is, the control channel is assumed to be memoryless. Error bursts come from the memory effect of the channel, and are typically found in wireless communications. In this paper we consider the effects of burst errors in the control channel as well as the problem of signal distortion caused by three methods of quantization in centralized CSS with sample fusion. The Gerschgorin Radii and Centers Ratio (GRCR) detector is used as the test statistic for spectrum sensing. Our findings show that: i) the GRCR is robust in the scenarios taken into consideration, ii) the uniform quantization may be preferred in some cases when the control channel is considered perfect, iii) the nonuniform quantization attains better performance under errors in the control channel, and iv) the effect of memory in the control channel may produce, in some situations, performance gains with respect to the memoryless channel, when both have the same average bit error rate.

Research on the Gerschgorin Radii Estimator Based on Real-Valued Decomposition Technique in Picosatellites

In order to reduce the computational burden, real-valued decomposition technique is applied in picosat positioning. Through converting the correlation matrix to real-valued matrix, the real-valued decomposition technique not only reduces the computer burden of matrix decomposition, but also makes the correlation matrix more close to Toeplitz matrix through a forward-backward smoothing. Simulations confirm that the real-valued Gerschgorin Radii shows an improved performance.

Smart antenna‐assisted spectrum sensing for robust detection in cognitive radio networks

SummaryIn this paper, we consider the problem of multiband spectrum sensing by employing smart antenna arrays at the cognitive receiver. Although energy detection is widely used for spectrum sensing in cognitive radio networks because of its simplicity and accuracy, it is severely deteriorated by the noise uncertainty. This paper introduces robust spectrum sensing techniques to circumvent this difficulty, which operate simultaneously over the total frequency channels rather than a single channel each time. To enhance the detection performance, the proposed schemes jointly utilize the information of eigenvalues and eigenvectors, signal and noise subspace components in conjunction with the likelihood functions and Gerschgorin radii. Neither subjective decision threshold setting nor the estimation of noise power is required in our schemes, making them robust to noise uncertainty. Simulations are presented to validate the performance of the proposed schemes, and the results show that our schemes can outperform other existing spectrum sensing methods. Copyright © 2014 John Wiley & Sons, Ltd.

Blind multiband spectrum sensing for cognitive radio systems with smart antennas

Energy detection is a widely used method for spectrum sensing in cognitive radios because of its simplicity and accuracy. However, it is severely affected by noise uncertainty. To solve this problem, this study raises a blind multiband spectrum sensing method with smart antennas which is robust to noise uncertainty. The proposed method performs spectrum sensing simultaneously over the total frequency channels rather than a single channel each time. Owing to the fact that the noise eigenvectors of the sample covariance matrix are orthogonal to the direction matrix whereas the signal eigenvectors are not, the proposal utilises the Gerschgorin radii of the unitary transformed covariance matrix to distinguish the noise from the signal. Unlike the conventional sensing methods, the authors' approach does not need any prior knowledge of the noise power or the primary user signals, which makes it suitable for blind spectrum sensing. This study presents simulations in various conditions to validate the performance of the proposed scheme and shows that their proposal outperforms the other existing methods.