Abstract
In cognitive vehicular networks (CVNs), many envisioned applications related to safety require highly reliable connectivity. This paper investigates the issue of robust and efficient cooperative spectrum sensing in CVNs. We propose robust cooperative spectrum sensing via low-rank matrix recovery (LRMR-RCSS) in cognitive vehicular networks to address the uncertainty of the quality of potentially corrupted sensing data by utilizing the real spectrum occupancy matrix and corrupted data matrix, which have a simultaneously low-rank and joint-sparse structure. Considering that the sensing data from crowd cognitive vehicles would be vast, we extend our robust cooperative spectrum sensing algorithm to dense cognitive vehicular networks via weighted low-rank matrix recovery (WLRMR-RCSS) to reduce the complexity of cooperative spectrum sensing. In the WLRMR-RCSS algorithm, we propose a correlation-aware selection and weight assignment scheme to take advantage of secondary user (SU) diversity and reduce the cooperation overhead. Extensive simulation results demonstrate that the proposed LRMR-RCSS and WLRMR-RCSS algorithms have good performance in resisting malicious SU behavior. Moreover, the simulations demonstrate that the proposed WLRMR-RCSS algorithm could be successfully applied to a dense traffic environment.
Highlights
Social problems of road accidents, traffic congestion, and air pollution are becoming increasingly severe with the increasing number of vehicles worldwide
We demonstrate that our proposed LRMR-RCSS and WLRMR-RCSS algorithms are secure and more efficient in cognitive vehicular networks (CVNs), and the WLRMR-RCSS algorithm is robust against traffic density changes
We evaluate our proposed schemes by comparing the alternating direction method of multipliers (ADMM)-Cooperative spectrum sensing (CSS) algorithm [20], the Belief Propagation (BP)-CSS algorithms in [15], and the Blind-CSS algorithm [42], as these schemes are all designed for robust cooperative spectrum sensing
Summary
Social problems of road accidents, traffic congestion, and air pollution are becoming increasingly severe with the increasing number of vehicles worldwide. LRMR-RCSS algorithm in this paper is established to recover the real data from noisy and corrupted data for improving the spectrum sensing data quality and CSS performance, which is applicable to low traffic density environments. The aforementioned low-rank matrix recovery-based CSS method [20] focuses on improving sensing data quality without considering high data transmission cost in CSS networks. These above methods are complex to implement in practical CVNs due to their complexity or hardware facility. (iii) The simulations demonstrate that the LRMR-RCSS and WLRMR-RCSS algorithms can effectively mitigate the adverse effects of corrupted data introduced by the malicious behaviors of SUs. the proposed WLRMR-RCSS algorithm can be successfully applied in a dense traffic environment.
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