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

2025-01-21

AI Summary

This paper addresses the non-convex weighted sum-rate (WSR) maximization problem in wireless networks. We propose a learnable primal-dual algorithm (PDA) that integrates interference function theory with deep unfolding. Under the log-concave interference function assumption, the method provides theoretical convergence guarantees while maintaining end-to-end differentiability, enabling low-complexity, trainable resource allocation. Our key innovation lies in incorporating the log-concave interference model into the deep-unfolding framework—achieving, for the first time, a theoretically grounded, learnable PDA design. Experiments on multi-user interference channels demonstrate that the proposed approach improves WSR by up to 12.7% over state-of-the-art algorithms such as FPLinQ, while reducing computational overhead by more than 50%. The method thus bridges theoretical rigor and engineering practicality.

Technology Category

• Planning, Routing, and Scheduling: Planning/Scheduling and Learning
• Search and Optimization: Non-convex Optimization
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Wireless Network Optimization
• Weighted Total Rate Maximization
• Transmission Speed Enhancement

Innovation

• Fixed-Point Algorithm
• Log-Concave Interference Function
• Deep Unfolding