CNN-Based End-to-End CPU-AP-UE Power Allocation for Spectral Efficiency Enhancement in Cell-Free Massive MIMO Networks

Yoon-Ju Choi, Ji-Hee Yu, Seung-Hwan Seo, Seong-Gyun Choi, Hye-Yoon Jeong, Ja-Eun Kim, Myung-Sun Baek, Young-Hwan You and Hyoung-Kyu Song ()
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Yoon-Ju Choi: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Ji-Hee Yu: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Seung-Hwan Seo: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Seong-Gyun Choi: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Hye-Yoon Jeong: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Ja-Eun Kim: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Myung-Sun Baek: Department of Artificial Intelligence and Information Technology, Sejong University, Seoul 05006, Republic of Korea
Young-Hwan You: Department of Computer Engineering, Sejong University, Seoul 05006, Republic of Korea
Hyoung-Kyu Song: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea

Mathematics, 2025, vol. 13, issue 9, 1-17

Abstract: Cell-free massive multiple-input multiple-output (MIMO) networks eliminate cell boundaries and enhance uniform quality of service by enabling cooperative transmission among access points (APs). In conventional cellular networks, user equipment located at the cell edge experiences severe interference and unbalanced resource allocation. However, in cell-free massive MIMO networks, multiple access points cooperatively serve user equipment (UEs), effectively mitigating these issues. Beamforming and cooperative transmission among APs are essential in massive MIMO environments, making efficient power allocation a critical factor in determining overall network performance. In particular, considering power allocation from the central processing unit (CPU) to the APs enables optimal power utilization across the entire network. Traditional power allocation methods such as equal power allocation and max–min power allocation fail to fully exploit the cooperative characteristics of APs, leading to suboptimal network performance. To address this limitation, in this study we propose a convolutional neural network (CNN)-based power allocation model that optimizes both CPU-to-AP power allocation and AP-to-UE power distribution. The proposed model learns the optimal power allocation strategy by utilizing the channel state information, AP-UE distance, interference levels, and signal-to-interference-plus-noise ratio as input features. Simulation results demonstrate that the proposed CNN-based power allocation method significantly improves spectral efficiency compared to conventional power allocation techniques while also enhancing energy efficiency. This confirms that deep learning-based power allocation can effectively enhance network performance in cell-free massive MIMO environments.

Keywords: cell-free massive MIMO; power allocation; CNN; CPU-AP-UE optimization; deep learning; spectral efficiency; energy efficiency (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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