Performance analysis and deep learning assessment of full-duplex overlay cognitive radio NOMA networks under non-ideal system imperfections

Abstract

<div lang="en" class="abs"><p class="abs_header">Abstract</p><p>In this paper, we investigate the effectiveness of an overlay cognitive radio (OCR) coupled with non-orthogonal multiple access (NOMA) system using a full-duplex (FD) cooperative spectrum access with a maximal ratio combining (MRC) scheme under the various non-ideal system imperfections. In view of practical realization, we ponder the impact of loop self-interference, transceiver hardware impairments, imperfect successive interference cancellation, and channel estimation errors on the system performance. We investigate the performance of the proposed system by obtaining closed-form expressions for outage probability and ergodic rate for primary as well as secondary users using Nakagami- <em>m</em> fading channels. As a result, we reveal some notable ceiling effects and present efficacious power allocation strategy for cooperative spectrum access. We further evaluate the system throughput and ergodic sum-rate (ESR) to assess the system’s overall performance. Our findings manifest that the FD-based OCR-NOMA can comply with the non-ideal system imperfections and outperform the competing half-duplex (HD) and orthogonal multiple access (OMA) counterparts. Due to the massive complexity of the suggested system model, direct derivation of the closed-form formula for the ESR becomes cumbersome. To address this problem, we develop a deep neural network (DNN) framework for ESR prediction in real-time situations.</p></div>