Malicious User Detection Based on Low-Rank Matrix Completion in Wideband Spectrum Sensing

Abstract

In cognitive radio networks, cooperative spectrum\ud sensing (CSS) has been a promising approach to improve sensing\ud performance by utilizing spatial diversity of participating secondary\ud users (SUs). In current CSS networks, all cooperative SUs\ud are assumed to be honest and genuine. However, the presence of\ud malicious users sending out dishonest data can severely degrade\ud the performance of CSS networks. In this paper, a framework\ud with high detection accuracy and low costs of data acquisition at\ud SUs is developed, with the purpose of mitigating the influences of\ud malicious users. More specifically, a low-rank matrix completion\ud based malicious user detection framework is proposed. In the\ud proposed framework, in order to avoid requiring any prior\ud information about the CSS network, a rank estimation algorithm\ud and an estimation strategy for the number of corrupted channels\ud are proposed. Numerical results show that the proposed malicious\ud user detection framework achieves high detection accuracy with\ud lower data acquisition costs in comparison with the conventional\ud approach. After being validated by simulations, the proposed\ud malicious user detection framework is tested on the real-world\ud signals over TV white space spectrum

20 Years of Evolution From Cognitive to Intelligent Communications

“…The first type exploits the sparsity of spectral signals in the frequency domain caused by the low spectrum utilization. The sparsity level needs to be estimated first to determine the minimal sampling rate at secondary users [35]- [37]. A twostep CS scheme has been proposed in [35] to minimize the sampling rates when the sparsity level is changing.…”

Distributed Compressive Sensing Augmented Wideband Spectrum Sharing for Cognitive IoT

“…To obtain the observation that e = ||x−x|| 2 2 could be bounded and estimated by S p = ||Φ vx − y v || 2 2 , we change the (15) to another form (16) and simplify it to (17):…”

“…In [14], [15], sequential sensing approaches were proposed to individually sense the channels by using the tunable narrowband bandpass filter with low-rate ADC. Due to the sequential nature of those schemes, the large sensing latency would be introduced, which may lead to missed opportunities or interferences [16]. Therefore, compressive sensing (CS) [17], [18] was applied to to realize wideband spectrum sensing without the high rate signal sampling and processing.…”

Robust and Low‐Complexity Cooperative Spectrum Sensing via Low‐Rank Matrix Recovery in Cognitive Vehicular Networks

“…In other words, the abnormal data are randomly and sparsely distributed. The matrix constructed by the received signals exhibits a low-rank property, as indicated in [35][36][37].…”

“…We set the decorrelation function to 20 m for urban environments as in [37]. We choose vehicle , which satisfies ( , ), as the maximum among the vehicles in set .…”