  Symbol detection plays an important role in the implementation of digital
receivers. In this work, we propose ViterbiNet, which is a data-driven symbol
detector that does not require channel state information (CSI). ViterbiNet is
obtained by integrating deep neural networks (DNNs) into the Viterbi algorithm.
We identify the specific parts of the Viterbi algorithm that are
channel-model-based, and design a DNN to implement only those computations,
leaving the rest of the algorithm structure intact. We then propose a
meta-learning based approach to train ViterbiNet online based on recent
decisions, allowing the receiver to track dynamic channel conditions without
requiring new training samples for every coherence block. Our numerical
evaluations demonstrate that the performance of ViterbiNet, which is ignorant
of the CSI, approaches that of the CSI-based Viterbi algorithm, and is capable
of tracking time-varying channels without needing instantaneous CSI or
additional training data. Moreover, unlike conventional Viterbi detection,
ViterbiNet is robust to CSI uncertainty, and it can be reliably implemented in
complex channel models with constrained computational burden. More broadly, our
results demonstrate the conceptual benefit of designing communication systems
to that integrate DNNs into established algorithms.
