Deep learning for Improved Spectrum Occupancy Prediction with Fading Estimation in 5G Radio

In this paper, we present a novel approach to spectrum sensing (SS) and spectrum future occupancy prediction (SP) in a multipath, frequency-selective fading radio environment. In conventional methods, fading effects in the link between a primary user (PU) and the sensor may impede the detection of the PU transmission. Additionally, in the presence of multiple PUs and links characterized by different signal-to-noise (SNR) ratios, reliable SS and SP become even more difficult. In this paper, we look at the methods to resolve these problems. Our proposed algorithm employs machine learning (ML) methods to uncover time and frequency patterns in the occupied spectrum and occurring frequency-selective fading effects. It exploits two convolutional neural networks (CNNs). One is for Spectrum Sensing (SS) and Sensing Prediction (SP), and the other estimates fading level of a signal in a given moment. Both CNN's decisions are combined into one joint decision with improved reliability. The fading estimation provides information on the level of channel distortion that impacts the quality of SS and SP performance. The obtained results are promising and prove the usefulness of the ML-based fading-level estimation in SS and SP.