Meta-ViterbiNet: Online Meta-Learned Viterbi Equalization for Non-Stationary Channels

Understand

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.