Attention Neural Network for Downlink Cell-Free Massive MIMO Power Control

The downlink power control is challenging in a cell-free massive multiple-input multiple-output (CFmMIMO) system because of the non-convexity of the problem. This paper proposes a computationally efficient deep-learning algorithm to solve the max-min power control optimization problem subject to power constraints. To solve this problem, it presents an attention neural network(ANN) composed using the masked multi-head attention network modules, which are building blocks of the popular transformer neural network. The ANN solves the downlink power control problem of CFmMIMO in the presence of pilot contamination (non-orthogonal pilot sequences). The paper first translates the constrained optimization problem to an unconstrained one parameterized by the weights of the ANN. These weights are trained in an unsupervised fashion. The performance of the ANN power control algorithm is demonstrated using numerical simulations. The paper also provides a computational complexity analysis of the method.