Deep Unfolding of Fixed-Point Based Algorithm for Weighted Sum Rate Maximization

TL;DR

This paper introduces a deep unfolding approach for a fixed-point based algorithm to efficiently solve the non-convex weighted sum rate maximization problem in wireless networks, with theoretical guarantees and competitive performance.

Contribution

It develops a novel deep unfolding method for a primal-dual algorithm addressing non-convex WSR maximization, with theoretical guarantees under log-concavity.

Findings

The proposed method achieves comparable or better performance than FPLinQ.

Deep unfolding significantly reduces computational complexity.

Theoretical guarantees hold under log-concavity assumptions.

Abstract

In this paper, we propose a novel approach that harnesses the standard interference function, specifically tailored to address the unique challenges of non-convex optimization in wireless networks. We begin by establishing theoretical guarantees for our method under the assumption that the interference function exhibits log-concavity. Building on this foundation, we develop a Primal-Dual Algorithm (PDA) to approximate the solution to the Weighted Sum Rate (WSR) maximization problem. To further enhance computational efficiency, we leverage the deep unfolding technique, significantly reducing the complexity of the proposed algorithm. Through numerical experiments, we demonstrate the competitiveness of our method compared to the state-of-the-art fractional programming benchmark, commonly referred to as FPLinQ.