Distributed Sparse Spectrum Detection in Multihop Cognitive Underwater Acoustict Communication Networks

Since the underwater acoustic channel suffers often severe frequency-dependent attenuation, low speed of wave propagation and excessive multipath delay spread, the implementation of spectrum detection in Cognitive Underwater Acoustic Communication (CUAC) becomes very difficult. Beside, there is no fusion center in Ad hoc underwater acoustic communication networks. Therefore, the centralized spectrum detection methods in CUAC are not available. Similar to Cognitive Radio (CR), since the spectrum utility in CUAC is also low, the spectrum is sparse. Based on compressed sensing and considering the specificity of underwater acoustic, compressed spectrum detection algorithm for cognitive radio is improved, and then two distributed cooperative spectrum detection methods, which are suitable for CUAC, are proposed for different scenarios (with and without channel state information). By strengthening among secondary users, the proposed algorithms obtain spatial diversity gains and exploit joint sparse structure to improve the performance of spectrum detection. Via distributed computation and localized optimization, the new schemes entail low computation and power overhead per cognitive users. Simulation results corroborate the effectiveness of the proposed methods in detecting the spectrum holes in underwater acoustic environment.