Deep Reinforcement Learning-Based Block Coordinate Descent for Downlink Weighted Sum-Rate Maximization on AI-Native Wireless Networks

2026-02-24

IEEE Transactions on Wireless Communications

AI Summary

This work addresses the non-convex weighted sum-rate maximization problem under a total transmit power constraint in AI-native wireless networks. To tackle this challenge, the authors propose a hybrid optimization framework that integrates deep reinforcement learning with block coordinate descent (BCD). By synergistically combining data-driven learning and structured optimization, the method strictly enforces the power constraint while significantly improving solution accuracy, reducing sensitivity to initialization, and effectively avoiding poor local optima. Experimental results demonstrate that the proposed approach outperforms existing methods in terms of effectiveness, computational efficiency, robustness, and interpretability, offering a novel and reliable optimization paradigm for resource-constrained AI-native wireless networks.

Technology Category

• Search and Optimization: Non-convex Optimization
• Machine Learning: Optimization
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• 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

This paper introduces a deep reinforcement learning-based block coordinate descent (DRL-based BCD) algorithm to address the nonconvex weighted sum-rate maximization (WSRM) problem with a total power constraint. Firstly, we present an efficient block coordinate descent (BCD) method to solve the problem. While this method may not always achieve globally optimal solutions, it provides a pathway for integrating machine learning and domain-specific techniques with theoretical analysis of the underlying convexity of the subproblems. We then integrate deep reinforcement learning (DRL) techniques into the BCD method and propose the DRL-based BCD algorithm. This approach combines the data-driven learning capability of machine learning techniques with the navigational and decision-making characteristics of the optimization-theoretic-based BCD method. This combination significantly improves the algorithm’s performance by reducing its sensitivity to initial points and mitigating the risk of entrapment in local optima. The primary advantages of the proposed DRL-based BCD algorithm lie in its ability to adhere to the constraints of the WSRM problem and significantly enhance accuracy, potentially achieving the exact optimal solution. Moreover, unlike many pure machine-learning approaches, the DRL-based BCD algorithm capitalizes on the underlying theoretical analysis of the WSRM problem’s structure. This enables it to be easily trained and computationally efficient while maintaining a level of interpretability. Moreover, the DRL-based BCD framework demonstrates strong extensibility and can effectively be applied to other scenarios, such as joint beamforming for sum rate maximization, as demonstrated in this paper. Through numerical experiments, the DRL-based BCD algorithm demonstrates substantial advantages in effectiveness, efficiency, robustness, and interpretability for maximizing sum rates, which also provides valuable potential for designing resource-constrained AI-native wireless optimization strategies in next-generation wireless networks.

Problem

• weighted sum-rate maximization
• nonconvex optimization
• power constraint
• AI-native wireless networks
• downlink

Innovation

• Deep Reinforcement Learning
• Block Coordinate Descent
• Weighted Sum-Rate Maximization
• AI-Native Wireless Networks
• Nonconvex Optimization