Spectrum Sensing for Underwater Cognitive Radio With Limited Sensing Time

Underwater acoustic (UWA) communications suffer from the following factors: many multi-paths, slow propagation speed, rapid time-varying channels, and various noise such as the sound of marine animals and artificial acoustic systems. Moreover, since there are no strict standards or specifications for UWA communications, UWA communications generally employ cognitive radio (CR)-based ad-hoc networks, and recently, orthogonal frequency division multiple access (OFDMA) has been adopted to improve CR-based communication performance by maximizing multiplexing gain. However, due to the CR protocol, the performance of the UWA communication is significantly affected by sensing techniques. Therefore, this letter proposes a deep-learning-based spectrum sensing scheme in an OFDMA-based UWA-CR network. Compared to the existing schemes, the proposed scheme has a limited sensing time even shorter than one symbol duration, which is effective in a UWA environment where a long symbol duration is essential. In addition, by learning animal noise and interference caused by the broken orthogonality of OFDMA, the proposed scheme increases the detection accuracy of idle channels and recognizes animal sounds to prevent damage to animal. The simulation results confirm the superiority of the proposed scheme.