Meta-learning Based Beamforming Design for MISO Downlink

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.

arXiv commentconference

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Cited by in corpus (7)

• Robust Beamforming for RIS-aided Communications: Gradient-based Manifold Meta Learning
• Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo Simulation
• A Learning Aided Flexible Gradient Descent Approach to MISO Beamforming
• Energy-efficient Beamforming for RISs-aided Communications: Gradient Based Meta Learning
• A matrix-inverse-free implementation of the MU-MIMO WMMSE beamforming algorithm
• Learning-to-Learn the Wave Angle Estimation
• SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming