Energy Efficiency Optimization in UAV-Assisted CF-mMIMO with URLLC Using Deep Learning

This paper investigates the energy efficiency (EE) of Cell-free massive multiple-input multiple-output (CFmMIMO) systems under Ultra-reliable low-latency communication (URLLC) constraints. Specifically, we consider a UAVassisted CF-mMIMO uplink framework and propose an iterativebased algorithm for resource allocation to optimize EE. Additionally, a low-complexity deep neural network (DNN) is designed to approximate the proposed resource allocation scheme. Compared to traditional algorithms, the DNN approach delivers real-time, stable outputs. Simulation results demonstrate the effectiveness of the proposed method.