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Energy-efficient access point clustering and power allocation in cell-free massive MIMO networks: a hierarchical deep reinforcement learning approach

Tan, Fangqing ; Deng, Quanxuan ; Liu, Qiang

EURASIP journal on advances in signal processing, 2024-12, Vol.2024 (1), p.18-25, Article 18 [Periódico revisado por pares]

Cham: Springer International Publishing

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Título:
Energy-efficient access point clustering and power allocation in cell-free massive MIMO networks: a hierarchical deep reinforcement learning approach
Autor: Tan, Fangqing ; Deng, Quanxuan ; Liu, Qiang
Assuntos: Access points clustering ; Advanced Distributed Signal Processing and Optimization for Multi-Agent IoT Networks ; Algorithms ; Cell-free massive MIMO ; Clustering ; Downlinking ; Energy efficiency ; Energy efficiency, hierarchical deep deterministic policy gradient ; Energy management systems ; Engineering ; MIMO communications ; Optimization ; Power allocation ; Quantum Information Technology ; Resource allocation ; Signal,Image and Speech Processing ; Spintronics ; Telecommunication systems ; Time ; Wireless networks
É parte de: EURASIP journal on advances in signal processing, 2024-12, Vol.2024 (1), p.18-25, Article 18
Descrição: Cell-free massive multiple-input multiple-output (CF-mMIMO) has attracted considerable attention due to its potential for delivering high data rates and energy efficiency (EE). In this paper, we investigate the resource allocation of downlink in CF-mMIMO systems. A hierarchical depth deterministic strategy gradient (H-DDPG) framework is proposed to jointly optimize the access point (AP) clustering and power allocation. The framework uses two-layer control networks operating on different timescales to enhance EE of downlinks in CF-mMIMO systems by cooperatively optimizing AP clustering and power allocation. In this framework, the high-level processing of system-level problems, namely AP clustering, enhances the wireless network configuration by utilizing DDPG on the large timescale while meeting the minimum spectral efficiency (SE) constraints for each user. The low layer solves the link-level sub-problem, that is, power allocation, and reduces interference between APs and improves transmission performance by utilizing DDPG on a small timescale while meeting the maximum transmit power constraint of each AP. Two corresponding DDPG agents are trained separately, allowing them to learn from the environment and gradually improve their policies to maximize the system EE. Numerical results validate the effectiveness of the proposed algorithm in term of its convergence speed, SE, and EE.
Editor: Cham: Springer International Publishing
Idioma: Inglês

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Energy-efficient access point clustering and power allocation in cell-free massive MIMO networks: a hierarchical deep reinforcement learning approach

Tan, Fangqing ; Deng, Quanxuan ; Liu, Qiang

Cham: Springer International Publishing

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