  The design of symbol detectors in digital communication systems has
traditionally relied on statistical channel models that describe the relation
between the transmitted symbols and the observed signal at the receiver. Here
we review a data-driven framework to symbol detection design which combines
machine learning (ML) and model-based algorithms. In this hybrid approach,
well-known channel-model-based algorithms such as the Viterbi method, BCJR
detection, and multiple-input multiple-output (MIMO) soft interference
cancellation (SIC) are augmented with ML-based algorithms to remove their
channel-model-dependence, allowing the receiver to learn to implement these
algorithms solely from data. The resulting data-driven receivers are most
suitable for systems where the underlying channel models are poorly understood,
highly complex, or do not well-capture the underlying physics. Our approach is
unique in that it only replaces the channel-model-based computations with
dedicated neural networks that can be trained from a small amount of data,
while keeping the general algorithm intact. Our results demonstrate that these
techniques can yield near-optimal performance of model-based algorithms without
knowing the exact channel input-output statistical relationship and in the
presence of channel state information uncertainty.
