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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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.