Downlink Beamforming Design for NOMA Using Convolutional Neural Networks

TL;DR

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.

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.

Figures (4)

• Figure 1: The architecture of the proposed CNN
• Figure 2: Transmit power performance versus different SINR thresholds
• Figure 3: Learning curve of the TCNN and FCNN methods with Adam optimizer
• Figure 4: Computation time of label, TCNN, and FCNN methods