Meta-learning Based Beamforming Design for MISO Downlink

TL;DR

This paper introduces a meta-learning based iterative algorithm using LSTM networks to optimize beamforming in MISO downlink channels, outperforming traditional methods especially at high SNR levels.

Contribution

It presents a novel meta-learning approach for beamforming design that learns dynamic optimization strategies, improving over conventional iterative algorithms like WMMSE.

Findings

Outperforms WMMSE significantly at high SNR

Achieves comparable performance to WMMSE at low SNR

Demonstrates the effectiveness of meta-learning in non-convex optimization

Abstract

Downlink beamforming is an essential technology for wireless cellular networks; however, the design of beamforming vectors that maximize the weighted sum rate (WSR) is an NP-hard problem and iterative algorithms are typically applied to solve it. The weighted minimum mean square error (WMMSE) algorithm is the most widely used one, which iteratively minimizes the WSR and converges to a local optimal. Motivated by the recent developments in meta-learning techniques to solve non-convex optimization problems, we propose a meta-learning based iterative algorithm for WSR maximization in a MISO downlink channel. A long-short-term-memory (LSTM) network-based meta-learning model is built to learn a dynamic optimization strategy to update the variables iteratively. The learned strategy aims to optimize each variable in a less greedy manner compared to WMMSE, which updates variables by computing their first-order stationary points at each iteration step. The proposed algorithm outperforms WMMSE significantly in the high signal to noise ratio(SNR) regime and shows the comparable performance when the SNR is low.