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

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 under latent interference. Namely, with the possibility of power policy shifting in the hidden network, computing an iterating direction based on the observed interference inherently implies that counterfactual is ignored in decision making. A synthetic control (SC) method 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. Power iteration is performed on the estimated rather than the observed interference. The proposed SC-WMMSE requires no more information than its origin. To our best knowledge, this is the first paper explores the potential of causal inference to assist 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.