DeepFP: Deep-Unfolded Fractional Programming for MIMO Beamforming

TL;DR

This paper introduces DeepFP, a deep-unfolded fractional programming approach that enhances MIMO beamforming efficiency by optimizing stepsize in FastFP, reducing computational complexity compared to traditional methods.

Contribution

It integrates deep unfolding into FastFP to optimize stepsize, improving efficiency for multicell MIMO beamforming over existing fractional programming techniques.

Findings

DeepFP outperforms WMMSE-based learning methods in efficiency.

The proposed method reduces computational complexity.

DeepFP achieves comparable or better beamforming performance.

Abstract

This work proposes a mixed learning-based and optimization-based approach to the weighted-sum-rates beamforming problem in a multiple-input multiple-output (MIMO) wireless network. The conventional methods, i.e., the fractional programming (FP) method and the weighted minimum mean square error (WMMSE) algorithm, can be computationally demanding for two reasons: (i) they require inverting a sequence of matrices whose sizes are proportional to the number of antennas; (ii) they require tuning a set of Lagrange multipliers to account for the power constraints. The recently proposed method called the reduced WMMSE addresses the above two issues for a single cell. In contrast, for the multicell case, another recent method called the FastFP eliminates the large matrix inversion and the Lagrange multipliers by using an improved FP technique, but the update stepsize in the FastFP can be difficult to decide. As such, we propose integrating the deep unfolding network into the FastFP for the stepsize optimization. Numerical experiments show that the proposed method is much more efficient than the learning method based on the WMMSE algorithm.