Deep Multi-Task Learning for Cooperative NOMA: System Design and Principles

TL;DR

This paper introduces a deep learning-based cooperative NOMA system that optimizes communication reliability and BER performance through a novel neural network architecture and training method, outperforming traditional schemes in simulations.

Contribution

It proposes a hybrid-cascaded deep neural network architecture and a multi-task training approach for end-to-end optimization of cooperative NOMA systems, addressing BER and power mismatch issues.

Findings

Outperforms traditional NOMA and OMA schemes in simulations

Effectively handles power mismatch and channel coding integration

Provides insights into DNN training via information theory

Abstract

Envisioned as a promising component of the future wireless Internet-of-Things (IoT) networks, the non-orthogonal multiple access (NOMA) technique can support massive connectivity with a significantly increased spectral efficiency. Cooperative NOMA is able to further improve the communication reliability of users under poor channel conditions. However, the conventional system design suffers from several inherent limitations and is not optimized from the bit error rate (BER) perspective. In this paper, we develop a novel deep cooperative NOMA scheme, drawing upon the recent advances in deep learning (DL). We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner. On this basis, we construct multiple loss functions to quantify the BER performance and propose a novel multi-task oriented two-stage training method to solve the end-to-end training problem in a self-supervised manner. The learning mechanism of each DNN module is then analyzed based on information theory, offering insights into the proposed DNN architecture and its corresponding training method. We also adapt the proposed scheme to handle the power allocation (PA) mismatch between training and inference and incorporate it with channel coding to combat signal deterioration. Simulation results verify its advantages over orthogonal multiple access (OMA) and the conventional cooperative NOMA scheme in various scenarios.