Symbol-Level Online Channel Tracking for Deep Receivers.

Deep neural networks (DNNs) allow digital receivers to operate in complex environments by learning from data corresponding to the channel input-output relationship. Since communication channels change over time, DNN-aided receivers may be required to retrain periodically, which conventionally involves excessive pilot signaling at the cost of reduced spectral efficiency. In this paper, we study how one can obtain data for retraining deep receivers without sending pilots or relying on specific protocol redundancies, by combining self-supervision with active learning concepts. We focus on the recently proposed ViterbiNet receiver, which integrates into the Viterbi algorithm a DNN for learning the channel. To enable self-supervision, we use the soft-output Viterbi algorithm to evaluate the decision confidence for each of the detected symbols in a given word. Then, to overcome learning with erroneous data, we choose a subset of the recovered symbols to be used for retraining via active learning. The proposed method selects decision-directed data whose confidence is not too low to result in inaccurate labeling, yet not too high to preserve sufficient diversity of the data. We demonstrate that self-supervised symbol-level training yields a performance within a small gap of the Viterbi algorithm with instantaneous channel knowledge.