Power Control Based on Multi-Agent Deep Q Network for D2D Communication

2025/11/02 by Shi Gengtian, Takashi Koshimizu, Gengtian, Shi +8 · 1 citation
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Advanced Wireless Network Optimization #FOS: Computer and information sciences #Networking and Internet Architecture (cs.NI) #Wireless Networks and Protocols

paper · pdf · doi:10.48550/arxiv.2511.00767

openalex publication_date 2025/11/02 · openalex created_date 2025/11/06 · openalex updated_date 2026/07/28

Abstract

In device-to-device (D2D) communication under a cell with resource sharing mode the spectrum resource utilization of the system will be improved. However, if the interference generated by the D2D user is not controlled, the performance of the entire system and the quality of service (QOS) of the cellular user may be degraded. Power control is important because it helps to reduce interference in the system. In this paper, we propose a reinforcement learning algorithm for adaptive power control that helps reduce interference to increase system throughput. Simulation results show the proposed algorithm has better performance than traditional algorithm in LTE (Long Term Evolution).

Citations

• TensorFlow: A system for large-scale machine learning
• Adam: A Method for Stochastic Optimization
• Playing Atari with Deep Reinforcement Learning
• An Overview on 3GPP Device-to-Device Proximity Services

Cited by

• Deep Q-Network for Optimizing NOMA-Aided Resource Allocation in Smart Factories with URLLC Constraints

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