Base station power control strategy in ultra-dense networks via deep reinforcement learning

IF 2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication Pub Date : 2025-03-10 DOI:10.1016/j.phycom.2025.102655

Qi Chen , Xuehan Bao , Shan Chen , Junhui Zhao

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引用次数: 0

Abstract

Within the context of 5G, Ultra-Dense Networks (UDNs) are regarded as an important network deployment strategy, employing a large number of low-power small cells to achieve extended coverage and enhanced service quality. However, the deployment of numerous small cells results in a linear increase in energy consumption in wireless communication systems. To enhance system efficiency and establish green wireless communication systems, this paper investigates base station sleeping and power allocation strategy based on deep reinforcement learning in UDNs. Firstly, a system energy consumption model for UDNs is established, which is divided into two sub-problems based on the final optimization problem, namely base station sleep and power allocation. Two Deep Q-networks (DQNs) are employed simultaneously for optimization. In addition to considering traditional system energy efficiency (EE), this study also optimizes system spectral efficiency (SE) and user transmission rate as optimization objectives simultaneously. Simulation results show that the proposed method improves EE and SE by about 70% and 81%.

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基于深度强化学习的超密集网络基站功率控制策略

在5G背景下，超密集网络（Ultra-Dense Networks, udn）被视为一种重要的网络部署策略，采用大量低功耗小蜂窝来实现扩大覆盖范围和提高服务质量。然而，大量小蜂窝的部署导致无线通信系统的能量消耗呈线性增长。为了提高系统效率，建立绿色无线通信系统，本文研究了基于深度强化学习的udn中基站休眠和功率分配策略。首先，建立了udn的系统能耗模型，该模型在最终优化问题的基础上分为基站睡眠和功率分配两个子问题。同时使用两个Deep q -network （dqn）进行优化。本研究除了考虑传统的系统能效（EE）外，还同时优化了系统频谱效率（SE）和用户传输速率作为优化目标。仿真结果表明，该方法分别提高了70%和81%的EE和SE。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Physical Communication ENGINEERING, ELECTRICAL & ELECTRONICTELECO-TELECOMMUNICATIONS

CiteScore

5.00

自引率

9.10%

发文量

212

审稿时长

55 days

期刊介绍： PHYCOM: Physical Communication is an international and archival journal providing complete coverage of all topics of interest to those involved in all aspects of physical layer communications. Theoretical research contributions presenting new techniques, concepts or analyses, applied contributions reporting on experiences and experiments, and tutorials are published. Topics of interest include but are not limited to: Physical layer issues of Wireless Local Area Networks, WiMAX, Wireless Mesh Networks, Sensor and Ad Hoc Networks, PCS Systems; Radio access protocols and algorithms for the physical layer; Spread Spectrum Communications; Channel Modeling; Detection and Estimation; Modulation and Coding; Multiplexing and Carrier Techniques; Broadband Wireless Communications; Wireless Personal Communications; Multi-user Detection; Signal Separation and Interference rejection: Multimedia Communications over Wireless; DSP Applications to Wireless Systems; Experimental and Prototype Results; Multiple Access Techniques; Space-time Processing; Synchronization Techniques; Error Control Techniques; Cryptography; Software Radios; Tracking; Resource Allocation and Inference Management; Multi-rate and Multi-carrier Communications; Cross layer Design and Optimization; Propagation and Channel Characterization; OFDM Systems; MIMO Systems; Ultra-Wideband Communications; Cognitive Radio System Architectures; Platforms and Hardware Implementations for the Support of Cognitive, Radio Systems; Cognitive Radio Resource Management and Dynamic Spectrum Sharing.