Deep Learning-based Joint Pilot and Data Power Control in Cell-Free Massive MIMO Networks

A deep learning (DL)-based joint pilot and data power control algorithm that solves the sum rate maximization problem in a cell-free massive multiple-input multiple-output (MIMO) system is proposed. The sum rate optimization problem for the uplink is formulated subject to per-user total transmit energy budget constraints, where user pilot and data power allocations are optimized to maximize the system sum rate. Instead of solving the non-convex problem using mathematical optimization theory, we utilize a data-driven solution approach to learn the optimal solutions. Specifically, we model a deep neural network (DNN) and train it via unsupervised learning using a custom loss function that captures the sum rate optimization objective and transmit energy constraints in the optimization problem. This unsupervised learning approach has a simpler and more flexible model training stage since it does not require labeled data for model training as in supervised learning. Simulation results show that the proposed DNN-based joint pilot and data power control algorithm improves the system sum rate compared to equal power and equal energy allocation heuristics and data power control-only approach. Furthermore, the joint power allocation results in significant energy savings (around 60 %) compared to fixed power allocation schemes.