Multicell Power Control Under Rate Constraints With Deep Learning
Yinghan Li, Shengqian Han, Chenyang Yang
Abstract
In the paper we study a deep learning based method to solve the multicell downlink power control problem for sum rate maximization subject to per-user rate constraints and per-base station (BS) power constraints. The core difficulty of this problem is how to ensure that the learned power control results by the deep neural network (DNN) satisfy the per-user rate constraints. To tackle the difficulty, we propose to cascade a projection block after a traditional DNN, which projects the infeasible power control results onto the constraint set. The projection block is designed based on a geometrical interpretation of the constraints, which is of low complexity, meeting the real-time requirement of online applications. Explicit-form expression of the backpropagated gradient is derived for the proposed projection block, with which the DNN can be trained to directly maximize the sum rate via unsupervised learning. Simulation results demonstrate the advantages of the proposed method over existing deep learning and numerical optimization methods, and show the robustness of the proposed method to the model mismatch between training and testing datasets.