A Deep Hybrid Intelligent Framework for Dynamic Downlink Power Allocation in Cell-Free Massive MIMO Systems

Cell-free massive multiple-input multiple-output (CF-mMIMO) systems have emerged as a promising architecture for beyond-5G wireless networks because they can provide user-centric coverage, improved spectral efficiency, and reduced cell-boundary limitations. However, dynamic downlink power allocation remains challenging due to user mobility, time-varying channel conditions, interference coupling, and the need to maintain Quality of Service (QoS) under practical transmit-power constraints. This paper proposes a Deep Hybrid Intelligent (DHI) framework for dynamic downlink power allocation in CF-mMIMO systems. The proposed framework integrates Soft Actor–Critic (SAC) reinforcement learning with three power-control strategies: DHI-Max-Min, DHI-Max-Product, and DHI-Max-Sum-Rate. The SAC agent learns adaptive power-allocation policies from the network state, while L-BFGS-B refinement is applied to the Max-Product and Max-Sum-Rate strategies to improve the power-allocation decisions under bounded transmit power. The framework is evaluated using a CF-mMIMO scenario with 64 access points and 32 pieces of user equipment distributed over a 1000 × 1000 m2 area. The simulation results show that DHI-Max-Sum-Rate achieves the highest sum spectral efficiency, while DHI-Max-Min provides the strongest QoS-oriented performance with a QoS satisfaction rate of 93.75%. In addition, DHI-Max-Product and DHI-Max-Sum-Rate achieve mean computational times of 0.0690 s and 0.0696 s, respectively, compared with 0.63 s for the DDPG benchmark. These results demonstrate that the proposed DHI framework provides an adaptive and computationally efficient solution for QoS-aware downlink power allocation in dynamic CF-mMIMO networks.

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