# Masked Symbol Modeling for Demodulation of Oversampled Baseband Communication Signals in Impulsive Noise-Dominated Channels

arXiv: https://arxiv.org/abs/2512.01428 · HTML: https://mlsanity.com/paper/2512.01428/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 1 Dec 2025.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [State-of-the-art Speech Recognition With Sequence-to-Sequence Models](https://mlsanity.com/paper/1712.01769/) (2017)
- [Error Correction Code Transformer](https://mlsanity.com/paper/2203.14966/) (2022)
- [In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Models](https://mlsanity.com/paper/2311.06101/) (2023)
- [Radio Foundation Models: Pre-training Transformers for 5G-based Indoor Localization](https://mlsanity.com/paper/2410.00617/) (2024)
- [Impulsive Noise Mitigation in Powerline Communications Using Sparse Bayesian Learning](https://mlsanity.com/paper/1303.1217/) (2013)

## Abstract (arXiv)

Recent breakthroughs in natural language processing show that attention mechanism in Transformer networks, trained via masked-token prediction, enables models to capture the semantic context of the tokens and internalize the grammar of language. While the application of Transformers to communication systems is a burgeoning field, the notion of context within physical waveforms remains under-explored. This paper addresses that gap by re-examining inter-symbol contribution (ISC) caused by pulse-shaping overlap. Rather than treating ISC as a nuisance, we view it as a deterministic source of contextual information embedded in oversampled complex baseband signals. We propose Masked Symbol Modeling (MSM), a framework for the physical (PHY) layer inspired by Bidirectional Encoder Representations from Transformers methodology. In MSM, a subset of symbol aligned samples is randomly masked, and a Transformer predicts the missing symbol identifiers using the surrounding "in-between" samples. Through this objective, the model learns the latent syntax of complex baseband waveforms. We illustrate MSM's potential by applying it to the task of demodulating signals corrupted by impulsive noise, where the model infers corrupted segments by leveraging the learned context. Our results suggest a path toward receivers that interpret, rather than merely detect communication signals, opening new avenues for context-aware PHY layer design.