Weighted Sum-Rate Maximization With Causal Inference for Latent Interference Estimation

TL;DR

This paper introduces a novel approach combining causal inference and synthetic control to improve weighted sum-rate maximization in wireless networks with hidden interference sources, enhancing convergence and performance.

Contribution

It extends the WMMSE algorithm by integrating synthetic control for counterfactual interference estimation, addressing latent interference in wireless optimization.

Findings

SC-WMMSE outperforms original WMMSE in convergence.

The method effectively estimates counterfactual interference.

Numerical results demonstrate improved sum-rate maximization.

Abstract

The paper investigates the weighted sum-rate maximization (WSRM) problem with latent interfering sources outside the known network, whose power allocation policy is hidden from and uncontrollable to optimization. The paper extends the famous alternate optimization algorithm weighted minimum mean square error (WMMSE) [1] under a causal inference framework to tackle with WSRM. Specifically, with the possibility of power policy shifting in the hidden network, computing an iterating direction based only on the observed interference inherently implies that counterfactual is ignored in decision making. A method called synthetic control (SC) is used to estimate the counterfactual. For any link in the known network, SC constructs a convex combination of the interference on other links and uses it as an estimate for the counterfactual. Power iteration in the proposed SC-WMMSE is performed taking into account both the observed interference and its counterfactual. SC-WMMSE requires no more information than the original WMMSE in the optimization stage. To our best knowledge, this is the first paper explores the potential of SC in assisting mathematical optimization in addressing classic wireless optimization problems. Numerical results suggest the superiority of the SC-WMMSE over the original in both convergence and objective.