NOMANet: A Graph Neural Network Enabled Power Allocation Scheme for NOMA

TL;DR

This paper introduces NOMANet, a graph neural network-based power allocation scheme for NOMA networks that significantly improves inference speed and scalability while maintaining near-optimal energy efficiency.

Contribution

The paper presents a novel GNN-based deep learning approach for power allocation in NOMA, enhancing speed and scalability over traditional methods.

Findings

NOMANet achieves near-optimal performance compared to convex approximation methods.

NOMANet is approximately 700 times faster in inference.

The approach is scalable to varying numbers of users and subchannels.

Abstract

This paper proposes a graph neural network (GNN) enabled power allocation scheme for non-orthogonal multiple access (NOMA) networks. In particular, a downlink scenario with one base station serving multiple users over several subchannels is considered, where the number of subchannels is less than the number of users, and thus, some users have to share a subchannel via NOMA. Our goal is to maximize the system energy efficiency subject to the rate requirement of each user and the overall budget. We propose a deep learning based approach termed NOMA net (NOMANet) to address the considered problem. Particularly, NOMANet is GNN-based, which maps channel state information to the desired power allocation scheme for all subchannels. The multi-head attention and the residual/dense connection are adopted to enhance the feature extraction. The output of NOMANet is guaranteed to be feasible via the customized activation function and the penalty method. Numerical results show that NOMANet trained unsupervised achieves performance close to that of the successive convex approximation method but with a faster inference speed by about $700$ times. Besides, NOMANet is featured by its scalability to both users and subchannels.