Energy Consumption Optimization in Cell-Free Massive MIMO Systems with Hybrid Energy Supply Based on Improved Sparrow Search Algorithm

Large-scale distributed antennas deployed in cell-free massive multiple-input multiple-output (CF-mMIMO) systems necessitate substantial power supply, which leads to high system cost and energy consumption. To address this issue, we consider the design of access points (APs) equipped with energy harvesting capabilities in CF-mMIMO systems, enabling a hybrid energy supply from both renewable energy and grid energy. Due to spatio-temporal uneven distribution of renewable energy, we enable the system to have energy transfer capabilities, enhancing energy utilization efficiency. Based on this framework, we formulate an optimization problem to minimize grid energy consumption by jointly optimizing power control and energy collaboration. To solve this problem, we propose an improved sparrow search algorithm (ISSA), which incorporates the penalty factor to ensure quality of service (QoS) requirements and leverages the Lévy flight strategy to avoid local optima. Furthermore, we enhance the search performance by integrating Gaussian and insertion mutations. Simulation results show that our proposed scheme significantly reduces run time and grid energy consumption compared to the considered general approaches.