Robust cooperative spectrum sensing in dense cognitive vehicular networks
Xia Liu, Zhimin Zeng, Caili Guo · 2017
This paper investigates the issue of efficient and robust cooperative spectrum sensing in dense cognitive vehicular networks. The sensing data from crowd cognitive vehicles may be vast or even have untrustworthy elements introduced by certain malicious secondary users (SUs). Many envisioned applications related to safety require highly reliable connectivity in the cognitive vehicular network. We propose a robust spectrum sensing algorithm to address the uncertainty of the quality of potentially corrupted sensing data. First, we propose a correlation-aware selection and weight assignment scheme to take advantage of SU diversity and reduce cooperation overhead. Under this scheme, we then formulate an optimization problem as a weighted low-rank and sparse recovery by utilizing the diagonal spectrum occupancy matrix and corrupted data matrix, which have a simultaneously low-rank and joint-sparse structure. We recover the principal component from noisy and corrupted data to improve the sensing data quality and cooperative sensing performance. Extensive simulation results demonstrate that the proposed robust cooperative spectrum sensing algorithm has significant performance to resist malicious SU behavior. Moreover, the simulations demonstrate that the proposed algorithm could be successfully applied to a dense traffic environment.