Deep neural network design with SLNR and SINR criterions for downlink power allocation in multi-cell multi-user massive MIMO systems

Abstract

In this paper, we propose a deep learning approach for solving power allocation problems in massive MIMO networks. We use signal-to-interference-plus-noise-ratio (SINR) and signal-to-leak-plus-noise ratio (SLNR) criteria for linear precoder design to define the max–min and max-prod power allocation challenges. The power allocation process to each user equipment in the base station coverage takes a long time and is inefficient, hence numerous base stations are deployed to serve multiple user equipments. As a result, we develop a deep neural network (DNN) framework in which the user’s equipment position is utilized to train the deep model, which is then used to forecast the ideal power distribution depending on the user’s location. Compared to the traditional optimization approach, the DNN design helps to obtain the optimal solution of the power allocation problem within a short time via a quick-inference process. Simulation results show that the SINR criterion outperforms the SLNR one. Meanwhile, deep learning achieves excellent results in forecasting power allocation with an accuracy of 85% for the max–min strategy and 99% for the max-product approach.

Keywords

• Deep neural networks
• Massive MIMO
• Power allocation
• Signal-to-leak-plus-noise ratio (SLNR)
• Signal-to-interference-plus-noise ratio (SINR)