MetaSSD: Meta-Learned Self-Supervised Detection
- Title
- MetaSSD: Meta-Learned Self-Supervised Detection
- Authors
- KIM, DONGWOO; Park, Moon Jeong; Ok, Jungseul; JEON, YO SEB
- Date Issued
- 2022-06-27
- Publisher
- IEEE
- Abstract
- Deep learning-based symbol detector gains increasing attention due to the simple algorithm design than the traditional model-based algorithms such as Viterbi and BCJR. The supervised learning framework is often employed to train a model, where true symbols are necessary. There are two major limitations in the supervised approaches: a) a model needs to be retrained from scratch when new train symbols come to adapt to a new channel status, and b) the length of the training symbols needs to be longer than a certain threshold to make the model generalize well on unseen symbols. To overcome these challenges, we propose a meta-learning-based self-supervised symbol detector named MetaSSD. Our contribution is two-fold: a) meta-learning helps the model adapt to a new channel environment based on experience with various meta-training environments, and b) self-supervised learning helps the model to use relatively less supervision than the previously suggested learning-based detectors. In experiments, MetaSSD outperforms OFDM-MMSE with noisy channel information and shows comparable results with BCJR. Further ablation studies show the necessity of each component in our framework. © 2022 IEEE.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/113551
- ISSN
- 2157-8095
- Article Type
- Conference
- Citation
- International Symposium on Information Theory (ISIT), 2022-06-27
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.