Deep Reinforcement Learning for Multi-Agent Power Control in Heterogeneous Networks

2020/04/25 by Lin Zhang, Ying‐Chang Liang, Zhang, Lin +2 · 2 citations
Computer Science · Engineering · Mathematics · #Cognitive Radio Networks and Spectrum Sensing #Energy Harvesting in Wireless Networks #FOS: Computer and information sciences #FOS: Electrical engineering #Full-Duplex Wireless Communications #Information Theory (cs.IT) #Signal Processing (eess.SP) #cs.IT #eess.SP #electronic engineering #information engineering #math.IT

paper · pdf · doi:10.48550/arxiv.2004.12095

openalex publication_date 2020/04/25 · arxiv created 2020/08/09 · arxiv updated 2020/08/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We consider a typical heterogeneous network (HetNet), in which multiple access points (APs) are deployed to serve users by reusing the same spectrum band. Since different APs and users may cause severe interference to each other, advanced power control techniques are needed to manage the interference and enhance the sum-rate of the whole network. Conventional power control techniques first collect instantaneous global channel state information (CSI) and then calculate sub-optimal solutions. Nevertheless, it is challenging to collect instantaneous global CSI in the HetNet, in which global CSI typically changes fast. In this paper, we exploit deep reinforcement learning (DRL) to design a multi-agent power control algorithm in the HetNet. To be specific, by treating each AP as an agent with a local deep neural network (DNN), we propose a multiple-actor-shared-critic (MASC) method to train the local DNNs separately in an online trial-and-error manner. With the proposed algorithm, each AP can independently use the local DNN to control the transmit power with only local observations. Simulations results show that the proposed algorithm outperforms the conventional power control algorithms in terms of both the converged average sum-rate and the computational complexity.

Citations

• An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel
• Fractional Programming for Communication Systems--Part I: Power Control and Beamforming
• Deep Reinforcement Learning Based Resource Allocation for V2V Communications

Cited by

• Novel Multi-Agent Action Masked Deep Reinforcement Learning for General Industrial Assembly Lines Balancing Problems
• Prompting Wireless Networks: Reinforced In-Context Learning for Power Control

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