NOMANet: A Graph Neural Network Enabled Power Allocation Scheme for NOMA
Yipu Hou, Yang Lu, Wei Chen, Bo Ai, Dusit Niyato, Zhiguo Ding

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…
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Taxonomy
TopicsAdvanced Wireless Communication Technologies · Molecular Communication and Nanonetworks · Wireless Body Area Networks
MethodsAttention Is All You Need · Softmax · Linear Layer · Multi-Head Attention · Balanced Selection · Graph Neural Network · SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
