A Lightweight Machine Learning Assisted Power Optimization for Minimum Error in NOMA-CRS over Nakagami-$m$ channels

TL;DR

This paper introduces a machine learning-based power optimization method for NOMA-CRS that minimizes error rates over Nakagami-m channels, addressing the gap in error performance analysis and improving BER performance.

Contribution

It derives a closed-form BEP expression for NOMA-CRS over Nakagami-m channels and proposes a low-complexity ML-assisted power sharing and allocation scheme to minimize BER.

Findings

Theoretical BEP analysis matches simulation results.

ML-assisted PS-PA achieves lower BER than previous strategies.

Proposed method has low online implementation complexity.

Abstract

Non-orthogonal multiple access based cooperative relaying system (NOMA-CRS) has been proposed to alleviate the decay in spectral efficiency of the conventional CRS. However, existing NOMA-CRS studies assume perfect successive interference canceler at the relay and mostly investigate sum-rate whereas the error performance has not been taken into consideration. In this paper, we analyze error performance of the NOMA-CRS and the closed-form bit error probability (BEP) expression is derived over Nakagami-m fading channels. Then, thanks to the high performance of machine learning (ML) in challenging optimization problems, a joint power sharing-power allocation (PS-PA) scheme is proposed to minimize the bit error rate (BER) of the NOMA-CRS. The proposed ML-assisted optimization has a very low online implementation complexity. Based on provided extensive simulations, theoretical BEP analysis is validated. Besides, the proposed ML-aided PS-PA provides minimum BER (MBER) and outperforms previous PA strategies for the NOMA-CRS notably.