Deep Learning Methods for Universal MISO Beamforming

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.

to appear in IEEE Wireless Communications Letters (5 pages, 3 figures, 2 tables)

References in corpus (4)

• Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management
• Optimal Multiuser Transmit Beamforming: A Difficult Problem with a Simple Solution Structure
• Learning to Detect
• A Deep Learning Framework for Optimization of MISO Downlink Beamforming

Cited by in corpus (6)

• Robust Beamforming for RIS-aided Communications: Gradient-based Manifold Meta Learning
• Embedding Model Based Fast Meta Learning for Downlink Beamforming Adaptation
• A Learning Aided Flexible Gradient Descent Approach to MISO Beamforming
• A matrix-inverse-free implementation of the MU-MIMO WMMSE beamforming algorithm
• Robust Millimeter Beamforming via Self-Supervised Hybrid Deep Learning
• Towards Unified AI Models for MU-MIMO Communications: A Tensor Equivariance Framework