Deep Learning Methods for Universal MISO Beamforming

TL;DR

This paper introduces a deep learning approach that efficiently optimizes beamforming in multi-user MISO systems under arbitrary power constraints, using a single trained model for all power levels.

Contribution

The proposed universal deep learning method can adapt to any power constraint with one training, unlike traditional methods requiring multiple models for different power levels.

Findings

Outperforms existing beamforming schemes

Single DNN handles all power constraints

Effective in multi-user MISO systems

Abstract

This letter studies deep learning (DL) approaches to optimize beamforming vectors in downlink multi-user multi-antenna systems that can be universally applied to arbitrarily given transmit power limitation at a base station. We exploit the sum power budget as side information so that deep neural networks (DNNs) can effectively learn the impact of the power constraint in the beamforming optimization. Consequently, a single training process is sufficient for the proposed universal DL approach, whereas conventional methods need to train multiple DNNs for all possible power budget levels. Numerical results demonstrate the effectiveness of the proposed DL methods over existing schemes.