Deep Learning-Based Precoder Design in MIMO Systems With Finite-Alphabet Inputs

It is a challenge to apply the precoder design maximizing the mutual information with finite-alphabet inputs to practical multiple-input multiple-output (MIMO) systems, because it needs to iteratively solve an optimization problem, which is difficult to satisfy the requirement of real time. This letter develops a deep learning based precoding scheme, which employs the property of deep neural network (DNN) as approximator of functions. Simulation results show that a DNN can accurately learn the input-output relationship of a nearly optimal precoder achieved by the traditional interior-point method (IPM); moreover, in different MIMO scenarios, a trained DNN of small size offers almost the same performance as the nearly optimal precoder, but with huge improvement in efficiency, especially in cases of higher modulation and more antennas. The improved efficiency makes it possible to be applied to practical communication systems.