Deep Unfolded Multicast Beamforming

TL;DR

This paper introduces a scalable deep unfolded neural network approach for multicast beamforming that improves convergence speed and efficiency, addressing limitations of previous deep learning methods in large systems.

Contribution

It proposes a novel deep unfolded beamforming algorithm with a recursive structure and trainable parameters, enhancing scalability and training stability.

Findings

Accelerates convergence speed using unsupervised learning.

Demonstrates high scalability and efficiency in large systems.

Outperforms conventional algorithms in numerical tests.

Abstract

Multicast beamforming is a promising technique for multicast communication. Providing an efficient and powerful beamforming design algorithm is a crucial issue because multicast beamforming problems such as a max-min-fair problem are NP-hard in general. Recently, deep learning-based approaches have been proposed for beamforming design. Although these approaches using deep neural networks exhibit reasonable performance gain compared with conventional optimization-based algorithms, their scalability is an emerging problem for large systems in which beamforming design becomes a more demanding task. In this paper, we propose a novel deep unfolded trainable beamforming design with high scalability and efficiency. The algorithm is designed by expanding the recursive structure of an existing algorithm based on projections onto convex sets and embedding a constant number of trainable parameters to the expanded network, which leads to a scalable and stable training process. Numerical results show that the proposed algorithm can accelerate its convergence speed by using unsupervised learning, which is a challenging training process for deep unfolding.