Deep Learning Based Joint Multi-User MISO Power Allocation and Beamforming Design

TL;DR

This paper introduces a deep learning-based joint power allocation and beamforming method for MU-MISO systems, significantly improving spectral efficiency and computational speed over traditional approaches in 5G networks.

Contribution

It presents a novel unsupervised deep learning framework, NNBF-P, for joint power allocation and beamforming, addressing practical limitations of existing methods.

Findings

NNBF-P outperforms ZFBF and MMSE in spectral efficiency.

Joint design improves performance over separate beamforming.

Deep learning offers computational advantages for real-time resource management.

Abstract

The evolution of fifth generation (5G) wireless communication networks has led to an increased need for wireless resource management solutions that provide higher data rates, wide coverage, low latency, and power efficiency. Yet, many of existing traditional approaches remain non-practical due to computational limitations, and unrealistic presumptions of static network conditions and algorithm initialization dependencies. This creates an important gap between theoretical analysis and real-time processing of algorithms. To bridge this gap, deep learning based techniques offer promising solutions with their representational capabilities for universal function approximation. We propose a novel unsupervised deep learning based joint power allocation and beamforming design for multi-user multiple-input single-output (MU-MISO) system. The objective is to enhance the spectral efficiency by maximizing the sum-rate with the proposed joint design framework, NNBF-P while also offering computationally efficient solution in contrast to conventional approaches. We conduct experiments for diverse settings to compare the performance of NNBF-P with zero-forcing beamforming (ZFBF), minimum mean square error (MMSE) beamforming, and NNBF, which is also our deep learning based beamforming design without joint power allocation scheme. Experiment results demonstrate the superiority of NNBF-P compared to ZFBF, and MMSE while NNBF can have lower performances than MMSE and ZFBF in some experiment settings. It can also demonstrate the effectiveness of joint design framework with respect to NNBF.