Signal Enhancement Aided End-to-End Deep Learning Approach for Joint Denoising and Spectrum Sensing
Zhengyang Su, Kah Chan Teh, Yihang Xie, Sirajudeen Gulam Razul, Alex C. Kot
Abstract
Deep learning (DL) has emerged as a promising solution for addressing spectrum scarcity and improving spectrum utilization in cognitive radio networks. Prior to the spectrum sensing phase, additional pre-processing methods could be employed to augment signal quality, while leading to increased computational complexity. In this paper, we introduce a novel joint denoising and spectrum sensing (JDSS) network that leverages an effective loss function to improve the probability of accurate signal detection. The JDSS encompasses a denoising network that regresses noisy received signals and a detection network designed for accurate hypothesis prediction. A comprehensive set of simulations is presented, demonstrating the enhanced performance of our proposed algorithm in a non-cooperative spectrum sensing scenario.