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This paper studies the problem of linear precoding for multiple-inputmultiple-output (MIMO) communication channels employing finite-alphabetsignaling. Existing solutions typically suffer from high computationalcomplexity due to costly computations of the constellation-constrained mutualinformation. In contrast to existing works, this paper takes a different pathof tackling the MIMO precoding problem. Namely, a data-driven approach, basedon deep learning, is proposed. In the offline training phase, a deep neuralnetwork learns the optimal solution on a set of MIMO channel matrices. Thisallows the reduction of the computational complexity of the precoderoptimization in the online inference phase. Numerical results demonstrate theefficiency of the proposed solution vis-\`a-vis existing precoding algorithmsin terms of significantly reduced complexity and close-to-optimal performance.

Deep-Learning Based Linear Precoding for MIMO Channels with Finite-Alphabet Signaling