# On the Robustness of Deep Learning-aided Symbol Detectors to Varying Conditions and Imperfect Channel Knowledge

arXiv: https://arxiv.org/abs/2401.12645 · HTML: https://mlsanity.com/paper/2401.12645/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 23 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Model-Based Deep Learning](https://mlsanity.com/paper/2012.08405/) (2020)
- [Deep Learning-Based Communication Over the Air](https://mlsanity.com/paper/1707.03384/) (2017)
- [Deep Learning Methods for Improved Decoding of Linear Codes](https://mlsanity.com/paper/1706.07043/) (2017)
- [A Very Brief Introduction to Machine Learning With Applications to Communication Systems](https://mlsanity.com/paper/1808.02342/) (2018)
- [Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks](https://mlsanity.com/paper/1805.06350/) (2018)
- [End-to-End Learning of Communications Systems Without a Channel Model](https://mlsanity.com/paper/1804.02276/) (2018)
- [End-to-end Deep Learning of Optical Fiber Communications](https://mlsanity.com/paper/1804.04097/) (2018)
- [Neural Network Detection of Data Sequences in Communication Systems](https://mlsanity.com/paper/1802.02046/) (2018)
- [Data-Driven Symbol Detection via Model-Based Machine Learning](https://mlsanity.com/paper/2002.07806/) (2020)
- [Learned Factor Graphs for Inference from Stationary Time Sequences](https://mlsanity.com/paper/2006.03258/) (2020)
- [Concept and Experimental Demonstration of Optical IM/DD End-to-End System Optimization using a Generative Model](https://mlsanity.com/paper/1912.05146/) (2019)
- [End-to-End Optimized Transmission over Dispersive Intensity-Modulated Channels Using Bidirectional Recurrent Neural Networks](https://mlsanity.com/paper/1901.08570/) (2019)

## Built on by (most cited first, 1 of 1)

- [Data-Driven Symbol Detection for Intersymbol Interference Channels with Bursty Impulsive Noise](https://mlsanity.com/paper/2405.10814/) (2024)

## Abstract (arXiv)

Recently, a data-driven Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm tailored to channels with intersymbol interference has been introduced. This so-called BCJRNet algorithm utilizes neural networks to calculate channel likelihoods. BCJRNet has demonstrated resilience against inaccurate channel tap estimations when applied to a time-invariant channel with ideal exponential decay profiles. However, its generalization capabilities for practically-relevant time-varying channels, where the receiver can only access incorrect channel parameters, remain largely unexplored. The primary contribution of this paper is to expand upon the results from existing literature to encompass a variety of imperfect channel knowledge cases that appear in real-world transmissions. Our findings demonstrate that BCJRNet significantly outperforms the conventional BCJR algorithm for stationary transmission scenarios when learning from noisy channel data and with imperfect channel decay profiles. However, this advantage is shown to diminish when the operating channel is also rapidly time-varying. Our results also show the importance of memory assumptions for conventional BCJR and BCJRNet. An underestimation of the memory largely degrades the performance of both BCJR and BCJRNet, especially in a slow-decaying channel. To mimic a situation closer to a practical scenario, we also combined channel tap uncertainty with imperfect channel memory knowledge. Somewhat surprisingly, our results revealed improved performance when employing the conventional BCJR with an underestimated memory assumption. BCJRNet, on the other hand, showed a consistent performance improvement as the level of accurate memory knowledge increased.