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Deep learning-based symbol detector gains increasing attention due to thesimple algorithm design than the traditional model-based algorithms such asViterbi and BCJR. The supervised learning framework is often employed topredict the input symbols, where training symbols are used to train the model.There are two major limitations in the supervised approaches: a) a model needsto be retrained from scratch when new train symbols come to adapt to a newchannel status, and b) the length of the training symbols needs to be longerthan a certain threshold to make the model generalize well on unseen symbols.To overcome these challenges, we propose a meta-learning-based self-supervisedsymbol detector named MetaSSD. Our contribution is two-fold: a) meta-learninghelps the model adapt to a new channel environment based on experience withvarious meta-training environments, and b) self-supervised learning helps themodel to use relatively less supervision than the previously suggestedlearning-based detectors. In experiments, MetaSSD outperforms OFDM-MMSE withnoisy channel information and shows comparable results with BCJR. Furtherablation studies show the necessity of each component in our framework.

MetaSSD: Meta-Learned Self-Supervised Detection