Beyond Supervised Power Control in Massive MIMO Network: Simple Deep Neural Network Solutions

Power control in massive multiple input multiple output (MIMO) systems is an appealing technique to improve network performance and reliability. The traditional methods to solve such problems are based on the convex optimization theory, which incurs high computational complexity. In contrast, this work leverages deep neural networks to maximize the minimum data rate of the downlink users in the massive MIMO network. Based on the different setups in the practical systems, namely
in-distribution case
,
weakly out-of-distribution case
, and
out-of-distribution case
, we propose three approaches to solve them. Specifically, first, we establish a densely connected neural network for solving the power control problem. Then, for the case where the test data and training data come from the same distributions (the
in-distribution case
), we use the established neural network to obtain an approximately optimal power control strategy, and propose a self-training-based algorithm to use the unlabeled samples collected in the actual system to further improve the system performance. For the case that the test data and training data come from different distributions and a small amount of labeled target domain data can be obtained (the
weakly out-of-distribution case
), we propose a pre-training and fine-tuning algorithm to solve the problem. For the issue that we can not obtain the labels of the target domain data (the
out-of-distribution case
), we transform the problem into a covariate shift problem and propose an algorithm by weighting the loss function. Numerical results demonstrate that the proposed schemes not only match the optimal solution well but also have low online inference complexity.