# MetaSSD: Meta-Learned Self-Supervised Detection

arXiv: https://arxiv.org/abs/2205.15271 · HTML: https://mlsanity.com/paper/2205.15271/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 30 May 2022.

## Builds on (most cited first, 14 of 14)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Learning to learn by gradient descent by gradient descent](https://mlsanity.com/paper/1606.04474/) (2016)
- [Learning to Detect](https://mlsanity.com/paper/1805.07631/) (2018)
- [Model-Driven Deep Learning for Physical Layer Communications](https://mlsanity.com/paper/1809.06059/) (2018)
- [Deep Unfolding for Communications Systems: A Survey and Some New Directions](https://mlsanity.com/paper/1906.05774/) (2019)
- [Adaptive Neural Signal Detection for Massive MIMO](https://mlsanity.com/paper/1906.04610/) (2019)
- [Deep MIMO Detection](https://mlsanity.com/paper/1706.01151/) (2017)
- [DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection](https://mlsanity.com/paper/2002.03214/) (2020)
- [Data-Driven Factor Graphs for Deep Symbol Detection](https://mlsanity.com/paper/2002.00758/) (2020)
- [Meta Learning-based MIMO Detectors: Design, Simulation, and Experimental Test](https://mlsanity.com/paper/2012.04379/) (2020)
- [Meta-ViterbiNet: Online Meta-Learned Viterbi Equalization for Non-Stationary Channels](https://mlsanity.com/paper/2103.13483/) (2021)
- [Learned Conjugate Gradient Descent Network for Massive MIMO Detection](https://mlsanity.com/paper/1906.03814/) (2019)
- [Deep Learning-Aided Projected Gradient Detector for Massive Overloaded MIMO Channels](https://mlsanity.com/paper/1806.10827/) (2018)

## Abstract (arXiv)

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 predict the input symbols, where training symbols are used to train the model. 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.