GLRT-Based Spectrum Sensing with Blindly Learned Feature under Rank-1 Assumption

Prior knowledge can improve the performance of spectrum sensing. Instead of using universal features as prior knowledge, we propose to blindly learn the localized feature at the secondary user. Motivated by pattern recognition in machine learning, we define signal feature as the leading eigenvector of the signal's sample covariance matrix. Feature learning algorithm (FLA) for blind feature learning and feature template matching algorithm (FTM) for spectrum sensing are proposed. Furthermore, we implement the FLA and FTM in hardware. Simulations and hardware experiments show that signal feature can be learned blindly. In addition, by using signal feature as prior knowledge, the detection performance can be improved by about 2 dB. Motivated by experimental results, we derive several GLRT based spectrum sensing algorithms under rank-1 assumption, considering signal feature, signal power and noise power as the available parameters. The performance of our proposed algorithms is tested on both synthesized rank-1 signal and captured DTV data, and compared to other state-of-the-art covariance matrix based spectrum sensing algorithms. In general, our GLRT based algorithms have better detection performance. In addition, algorithms with signal feature as prior knowledge are about 2 dB better than algorithms without prior knowledge

Generalized FMD Detection for Spectrum Sensing Under Low Signal-to-Noise Ratio

Spectrum sensing is a fundamental problem in cognitive radio. We propose a function of covariance matrix based detection algorithm for spectrum sensing in cognitive radio network. Monotonically increasing property of function of matrix involving trace operation is utilized as the cornerstone for this algorithm. The advantage of proposed algorithm is it works under extremely low signal-to-noise ratio, like lower than -30 dB with limited sample data. Theoretical analysis of threshold setting for the algorithm is discussed. A performance comparison between the proposed algorithm and other state-of-the-art methods is provided, by the simulation on captured digital television (DTV) signal.Comment: 4 pages, 1 figure, 1 tabl

An Improved and More Accurate Expression for a PDF Related to Eigenvalue-Based Spectrum Sensing

Cooperative spectrum sensing based on the limiting eigenvalue ratio of the covariance matrix offers superior detection performance and overcomes the noise uncertainty problem. While an exact expression exists, it is complex and multiple useful approximate expressions have been published in the literature. An improved, more accurate, integral solution for the probability density function of the ratio is derived using order statistical analysis to remove the simplifying, but incorrect, independence assumption. Thereby, the letter makes an advance in the rigorous theory of eigenvalue-based spectrum sensing.Comment: It has been accepted by IEEE Systems Journa

Spectrum Sensing with Small-Sized Datasets in Cognitive Radio: Algorithms and Analysis

Spectrum sensing is a fundamental component of cognitive radio. How to promptly sense the presence of primary users is a key issue to a cognitive radio network. The time requirement is critical in that violating it will cause harmful interference to the primary user, leading to a system-wide failure. The motivation of our work is to provide an effective spectrum sensing method to detect primary users as soon as possible. In the language of streaming based real-time data processing, short-time means small-sized data. In this paper, we propose a cumulative spectrum sensing method dealing with limited sized data. A novel method of covariance matrix estimation is utilized to approximate the true covariance matrix. The theoretical analysis is derived based on concentration inequalities and random matrix theory to support the claims of detection performance. Comparisons between the proposed method and other traditional approaches, judged by the simulation using a captured digital TV signal, show that this proposed method can operate either using smaller-sized data or working under lower SNR environment.Comment: 11 pages, 12 figure

1 GLRT-Based Spectrum Sensing with Blindly Learned Feature under Rank-1 Assumption

Abstract—Using signal feature as the prior knowledge can improve spectrum sensing performance. In this paper, we consider signal feature as the leading eigenvector (rank-1 information) extracted from received signal’s sample covariance matrix. Via real-world data and hardware experiments, we are able to demonstrate that such a feature can be learned blindly and it can be used to improve spectrum sensing performance. We derive several generalized likelihood ratio test (GLRT) based algorithms considering signal feature as the prior knowledge under rank-1 assumption. The performances of the new algorithms are compared with other state-of-the-art covariance matrix based spectrum sensing algorithms via Monte Carlo simulations. Both synthesized rank-1 signal and real-world digital TV (DTV) data are used in the simulations. In general, our GLRT-based algorithms have better detection performances, and the algorithms using signal feature as the prior knowledge have better performances than the algorithms without any prior knowledge. Index Terms—Spectrum sensing, cognitive radio (CR), generalized likelihood ratio test (GLRT), hardware. I