Minimum Eigenvalue Detection for Spectrum Sensing in Cognitive Radio

Spectrum sensing is a key task for cognitive radio. Our motivation is to increase the probability of detection for spectrum sensing in cognitive radio. In this paper, we proposed a new semi blind method which is based on minimum Eigenvalue of a covariance matrix. The ratio of the minimum eigenvalue to noise power is used as the test statistic. The method does not need channel and signal information as prior knowledge. Eigenvalue based algorithm perform better than energy detection for correlated signal. Our proposed method is better than the maximum eigenvalue and energy detection for correlated signal. We perform Simulation which is based on digital TV signal. In all tests, our method performs better than maximum eigenvalue detection and energy detection.DOI:http://dx.doi.org/10.11591/ijece.v4i4.622

Spectrum Sensing Testbed Design for Cognitive Radio Applications

Abstract-In this paper, we proposed a cognitive radio (CR) implementation by using standard wireless communication laboratory equipments such as signal generator and spectrum analyzer. Equipments are controlled through MATLAB instrument control toolbox to carry out CR capabilities specified by IEEE 802.22 WRAN standard. Energy detection and maximum minimum eigenvalue detection algorithms are employed to sense spectrum for opportunistic access. The aim of this work is to provide a CR environment for spectrum sensing algorithms to perform a comparative study considering wireless microphone (WM) signals for research and educational purposes

Max-Min SNR Signal Energy based Spectrum Sensing Algorithms for Cognitive Radio Networks with Noise Variance Uncertainty

This paper proposes novel spectrum sensing algorithms for cognitive radio networks. By assuming known transmitter pulse shaping filter, synchronous and asynchronous receiver scenarios have been considered. For each of these scenarios, the proposed algorithm is explained as follows: First, by introducing a combiner vector, an over-sampled signal of total duration equal to the symbol period is combined linearly. Second, for this combined signal, the Signal-to-Noise ratio (SNR) maximization and minimization problems are formulated as Rayleigh quotient optimization problems. Third, by using the solutions of these problems, the ratio of the signal energy corresponding to the maximum and minimum SNRs are proposed as a test statistics. For this test statistics, analytical probability of false alarm ($P_f$) and detection ($P_d$) expressions are derived for additive white Gaussian noise (AWGN) channel. The proposed algorithms are robust against noise variance uncertainty. The generalization of the proposed algorithms for unknown transmitter pulse shaping filter has also been discussed. Simulation results demonstrate that the proposed algorithms achieve better $P_d$ than that of the Eigenvalue decomposition and energy detection algorithms in AWGN and Rayleigh fading channels with noise variance uncertainty. The proposed algorithms also guarantee the desired $P_f(P_d)$ in the presence of adjacent channel interference signals

On the Performance of Spectrum Sensing Algorithms using Multiple Antennas

In recent years, some spectrum sensing algorithms using multiple antennas, such as the eigenvalue based detection (EBD), have attracted a lot of attention. In this paper, we are interested in deriving the asymptotic distributions of the test statistics of the EBD algorithms. Two EBD algorithms using sample covariance matrices are considered: maximum eigenvalue detection (MED) and condition number detection (CND). The earlier studies usually assume that the number of antennas (K) and the number of samples (N) are both large, thus random matrix theory (RMT) can be used to derive the asymptotic distributions of the maximum and minimum eigenvalues of the sample covariance matrices. While assuming the number of antennas being large simplifies the derivations, in practice, the number of antennas equipped at a single secondary user is usually small, say 2 or 3, and once designed, this antenna number is fixed. Thus in this paper, our objective is to derive the asymptotic distributions of the eigenvalues and condition numbers of the sample covariance matrices for any fixed K but large N, from which the probability of detection and probability of false alarm can be obtained. The proposed methodology can also be used to analyze the performance of other EBD algorithms. Finally, computer simulations are presented to validate the accuracy of the derived results.Comment: IEEE GlobeCom 201

Generalized detector as a spectrum sensor in cognitive radio networks

The implementation of the generalized detector (GD) in cognitive radio (CR) systems allows us to improve the spectrum sensing performance in comparison with employment of the conventional detectors. We analyze the spectrum sensing performance for the uncorrelated and spatially correlated receive antenna array elements. Addi¬tionally, we consider a practical case when the noise power at the output of GD linear systems (the preliminary and additional filters) is differed by value. The choice of the optimal GD threshold based on the minimum total error rate criterion is also discussed. Simulation results demonstrate superiority of GD implementation in CR sys¬tem as spectrum sensor in comparison with the energy detector (ED), weighted ED (WED), maximum-minimum eigenvalue (MME) detector, and generalized likelihood ratio test (GLRT) detecto