Downlink Beamforming Design for NOMA Using Convolutional Neural Networks

TL;DR

This paper introduces a CNN-based method for downlink NOMA beamforming that reduces computational complexity and latency, making it suitable for real-time wireless communication systems.

Contribution

The paper presents a novel CNN approach that efficiently designs beamforming vectors for NOMA, approximating optimal solutions with less computational effort.

Findings

CNN-based beamforming closely matches optimal performance

Significant reduction in computational time

Enhanced suitability for real-time applications

Abstract

Non-orthogonal multiple access (NOMA) and beamforming are well-established techniques for enabling massive connectivity in future wireless networks. However, many optimal beamforming solutions rely on highly complex iterative algorithms and optimization methods, resulting in an increase in computational burden and latency, making them less suitable for delay-sensitive applications and services. To address these challenges, we propose an effective convolutional neural network (CNN)-based approach for beamforming design in downlink NOMA systems to solve the transmit power minimization problem. The proposed method utilizes two representations of channel state information as input features to produce normalized beamforming vectors. Simulation results show that the CNN-based solution closely approximates the optimal label performance while significantly reducing computational time compared to conventional high-complexity algorithms, enhancing its practicality for real-time applications.