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

TL;DR

This paper proposes a novel deep reinforcement learning-based block coordinate descent algorithm to solve the nonconvex weighted sum-rate maximization problem in AI-native wireless networks, improving accuracy and robustness.

Contribution

It introduces a hybrid DRL-BCD method that enhances optimization performance and interpretability for wireless resource allocation tasks.

Findings

Significantly improves sum-rate maximization accuracy.

Reduces sensitivity to initial conditions and local optima.

Demonstrates robustness and efficiency in numerical experiments.

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. 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. 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.