• Computer Science > Networking and Internet Architecture [Submitted on 24 Feb 2026] Title:Deep Reinforcement Learning Based Block Coordinate Descent for Downlink Weighted Sum-rate Maximization on AI-Native Wireless Networks View PDF HTML (experimental)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.

Article Summaries:

  • Computer Science > Networking and Internet Architecture [Submitted on 24 Feb 2026] Title:Deep Reinforcement Learning Based Block Coordinate Descent for Downlink Weighted Sum-rate Maximization on AI-Native Wireless Networks View PDF HTML (experimental)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

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