A Learning Aided Flexible Gradient Descent Approach to MISO Beamforming

TL;DR

This paper introduces a learning aided gradient descent algorithm for MISO beamforming that adaptively optimizes transmit precoders, outperforming traditional methods without prior training or labeled data.

Contribution

It presents a novel learning aided gradient descent method that dynamically optimizes beamforming in MISO systems, applicable across various SNRs and antenna configurations.

Findings

Outperforms WMMSE algorithm in simulations

Does not require pre-training or labeled data

Operates effectively across different SNRs and system sizes

Abstract

This paper proposes a learning aided gradient descent (LAGD) algorithm to solve the weighted sum rate (WSR) maximization problem for multiple-input single-output (MISO) beamforming. The proposed LAGD algorithm directly optimizes the transmit precoder through implicit gradient descent based iterations, at each of which the optimization strategy is determined by a neural network, and thus, is dynamic and adaptive. At each instance of the problem, this network is initialized randomly, and updated throughout the iterative solution process. Therefore, the LAGD algorithm can be implemented at any signal-to-noise ratio (SNR) and for arbitrary antenna/user numbers, does not require labelled data or training prior to deployment. Numerical results show that the LAGD algorithm can outperform of the well-known WMMSE algorithm as well as other learning-based solutions with a modest computational complexity. Our code is available at https://github.com/XiaGroup/LAGD.