Two-timescale weighted sum-rate maximization for large cellular and cell-free massive MIMO

TL;DR

This paper introduces a novel iterative algorithm for large-scale MIMO networks that optimizes power control and beamforming using channel statistics, reducing the need for frequent updates and outperforming existing long-term methods.

Contribution

The paper presents a new sporadically run iterative algorithm for joint power control and beamforming in massive MIMO, leveraging channel statistics for improved efficiency and performance.

Findings

Algorithm reduces frequency of updates with minor performance loss

Outperforms existing long-term methods in massive MIMO scenarios

Applicable to cooperative beamforming with limited channel information sharing

Abstract

We reconsider the problem of joint power control and beamforming design to maximize the weighted sum rate in large and potentially cell-free massive MIMO networks. In contrast to the available short-term methods, where an iterative algorithm is run for every instantaneous channel realization, we derive an iterative algorithm that can be run only sporadically leveraging known channel statistics, with minor performance loss. In addition, our algorithm also applies to the design of non-trivial cooperative beamforming schemes subject to limited sharing of instantaneous channel state information. Furthermore, our algorithm generalizes and outperforms the competing long-term methods from the massive MIMO literature, which are restricted to long-term power control only or to long-term joint power control and large-scale fading decoding design.