CNN-Based End-to-End CPU-AP-UE Power Allocation for Spectral Efficiency Enhancement in Cell-Free Massive MIMO Networks  
[PERSON]  
1 Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea; [EMAIL_ADDRESS] (Y-J.C.); [EMAIL_ADDRESS] (J-H.Y. ); [EMAIL_ADDRESS] (S-H.S. ); [EMAIL_ADDRESS] (S-G.C. ); [EMAIL_ADDRESS] (H.-Y.J); [EMAIL_ADDRESS] (J-E.K.) 12 Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea 23 Department of Artificial Intelligence and Information Technology, Sejong University, Seoul 05006, Republic of Korea; [EMAIL_ADDRESS] 3  
[PERSON]  
1 Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea; [EMAIL_ADDRESS] (Y-J.C.); [EMAIL_ADDRESS] (J-H.Y. ); [EMAIL_ADDRESS] (S-H.S. ); [EMAIL_ADDRESS] (S-G.C. ); [EMAIL_ADDRESS] (H.-Y.J); [EMAIL_ADDRESS] (J-E.K.) 12 Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea 23 Department of Artificial Intelligence and Information Technology, Sejong University, Seoul 05006, Republic of Korea; [EMAIL_ADDRESS] 3  
[PERSON]  
1 Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea; [EMAIL_ADDRESS] (J-H.Y. ); [EMAIL_ADDRESS] (S-H.S. ); [EMAIL_ADDRESS] (S-G.C. ); [EMAIL_ADDRESS] (H.-Y.J); [EMAIL_ADDRESS] (J-E.K.) 12 Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea 23 Department of Computer Engineering, Sejong University, Seoul 05006, Republic of Korea; [EMAIL_ADDRESS] (J-E.K.) 12 Department of Convergence Engineering, Sejong University, Seoul 05006, Republic of Korea; [EMAIL_ADDRESS] (J-E.K.) 12 Department of Convergence Engineering, Sejong University, Seoul 05006, Republic of Korea; [EMAIL_ADDRESS]. input multiple-output (MIMO) technology. Traditional cellular networks suffer from performance degradation at the cell edges, where user equipment (UE) experiences high interference and unbalanced resource allocation, leading to poor communication quality. To overcome these limitations, cell-free massive MIMO replaces cell boundaries with a cooperative architecture, allowing multiple access points (APs) to jointly serve user equipment across the network. This structure improves uniform quality of service and mitigates performance degradation at the cell edges. The APs are connected to a central processing unit (CPU) by a wired backhaul, enabling centralized coordination and effective interference mitigation [2]. However, the practical deployment of cell-free massive MIMO systems requires efficient power allocation strategies. In particular, power should be allocated both from the CPU to the APs and from the APs to the UEs [3]; both of these stages significantly influence the spectral efficiency (SE) and energy efficiency (EE) [4]. The amount of power assigned by the CPU determines the maximum transmission power of each AP and affects overall interference and power usage. Similarly, the way in which APs distribute power among UEs directly impacts each user's signal quality and data throughput. Therefore, holistic optimization of CPU-AP-UE power allocation is crucial for achieving high system performance with minimal energy wastage. Traditional power allocation methods such as equal power allocation and max-min power allocation rely on heuristic approaches [3]. While computationally simple, these techniques fail to adapt to dynamic network conditions [5]. Equal power allocation ignores spatial variations in channel quality, leading to inefficient resource usage. Max-min power allocation improves fairness by supporting the weakest UEs, but often sacrifices total throughput by underutilizing stronger links [6]. Recently, deep learning-based power control strategies have gained attention thanks to their ability to generalize across complex nonlinear environments.