  Deep neural networks (DNNs) based digital receivers can potentially operate
in complex environments. However, the dynamic nature of communication channels
implies that in some scenarios, DNN-based receivers should be periodically
retrained in order to track temporal variations in the channel conditions. To
this aim, frequent transmissions of lengthy pilot sequences are generally
required, at the cost of substantial overhead. In this work we propose a
DNN-aided symbol detector, Meta-ViterbiNet, that tracks channel variations with
reduced overhead by integrating three complementary techniques: 1) We leverage
domain knowledge to implement a model-based/data-driven equalizer, ViterbiNet,
that operates with a relatively small number of trainable parameters; 2) We
tailor a meta-learning procedure to the symbol detection problem, optimizing
the hyperparameters of the learning algorithm to facilitate rapid online
adaptation; and 3) We adopt a decision-directed approach based on coded
communications to enable online training with short-length pilot blocks.
Numerical results demonstrate that Meta-ViterbiNet operates accurately in
rapidly-varying channels, outperforming the previous best approach, based on
ViterbiNet or conventional recurrent neural networks without meta-learning, by
a margin of up to 0.6dB in bit error rate in various challenging scenarios.
