  This paper investigates a novel method for designing linear precoders with
finite alphabet inputs based on autoencoders (AE) without the knowledge of the
channel model. By model-free training of the autoencoder in a multiple-input
multiple-output (MIMO) system, the proposed method can effectively solve the
optimization problem to design the precoders that maximize the mutual
information between the channel inputs and outputs, when only the input-output
information of the channel can be observed. Specifically, the proposed method
regards the receiver and the precoder as two independent parameterized
functions in the AE and alternately trains them using the exact and
approximated gradient, respectively. Compared with previous precoders design
methods, it alleviates the limitation of requiring the explicit channel model
to be known. Simulation results show that the proposed method works as well as
those methods under known channel models in terms of maximizing the mutual
information and reducing the bit error rate.
