ML-aided power allocation for Tactical MIMO

TL;DR

This paper introduces a learning-based power allocation method for MIMO wireless networks that is faster and scales better than traditional algorithms, maintaining robustness across various network conditions.

Contribution

It extends the UWMMSE framework to MIMO networks, enabling low-complexity, scalable, and robust power allocation with parameters depending only on antenna counts.

Findings

Outperforms WMMSE in speed and efficiency

Maintains robustness under changing channel conditions

Scales linearly with network size and antenna numbers

Abstract

We study the problem of optimal power allocation in single-hop multi-antenna ad-hoc wireless networks. A standard technique to solve this problem involves optimizing a tri-convex function under power constraints using a block-coordinate-descent based iterative algorithm. This approach, termed WMMSE, tends to be computationally complex and time consuming. Several learning-based approaches have been proposed to speed up the power allocation process. A recent work, UWMMSE, learns an affine transformation of a WMMSE parameter in an unfolded structure to accelerate convergence. In spite of achieving promising results, its application is limited to single-antenna wireless networks. In this work, we present a UWMMSE framework for power allocation in (multiple-input multiple-output) MIMO interference networks. A major advantage of this method lies in its use of low-complexity learnable systems in which the number of parameters scales linearly with respect to the hidden layer size of embedded neural architectures and the product of the number of transmitter and receiver antennas only, fully independent of the number of transceivers in the network. We illustrate the superiority of our method through an empirical study of our approach in comparison to WMMSE and also analyze its robustness to changes in channel conditions and network size.