A Flexible Low-Complexity DNN Solution for Power Control in Cell-Free Massive MIMO

Cell-free massive multiple-input multiple-output (MIMO) is a technology that combines ultra-dense cellular networks and massive MIMO to enhance wireless transmission efficiency, being a promising solution for future communication scenarios. However, its implementation poses a significant computational load, which can hinder real-time implementation. The use of machine learning comes as a great solution to reduce this computational complexity. In this context, the present work explores the application of a supervised, low-complexity deep neural network (DNN) for power control, aiming to significantly reduce computational costs. The DNN was evaluated across various scenarios, generating a comprehensive dataset. The proposed DNN outperforms existing methods, demonstrating superior performance with three power optimization schemes: max-min spectral efficiency (SE) fairness, sum SE maximization, and fractional power control (FPC). Importantly, these results are consistent across diverse propagation conditions. A noteworthy outcome of this study is the achievement of a three-orders-of-magnitude reduction in computation time when compared to conventional techniques, paving the way for the practical implementation of cell-free massive MIMO systems.