# Data-Driven Symbol Detection for Intersymbol Interference Channels with Bursty Impulsive Noise

arXiv: https://arxiv.org/abs/2405.10814 · HTML: https://mlsanity.com/paper/2405.10814/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 38 references that are in the map.
- First posted on arXiv 17 May 2024.

## Builds on (most cited first, 15 of 15)

- [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)
- [Model-free Training of End-to-end Communication Systems](https://mlsanity.com/paper/1812.05929/) (2018)
- [ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection](https://mlsanity.com/paper/1905.10750/) (2019)
- [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)
- [Physics-Based Deep Learning for Fiber-Optic Communication Systems](https://mlsanity.com/paper/2010.14258/) (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)
- [Blind Channel Estimation and Joint Symbol Detection with Data-Driven Factor Graphs](https://mlsanity.com/paper/2401.12627/) (2024)
- [On the Robustness of Deep Learning-aided Symbol Detectors to Varying Conditions and Imperfect Channel Knowledge](https://mlsanity.com/paper/2401.12645/) (2024)

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

- [Robust Blind Channel Estimation for Bursty Impulsive Noise with a Constrained EM Approach](https://mlsanity.com/paper/2504.03685/) (2025)

## Abstract (arXiv)

We developed machine learning approaches for data-driven trellis-based soft symbol detection in coded transmission over intersymbol interference (ISI) channels in presence of bursty impulsive noise (IN), for example encountered in wireless digital broadcasting systems and vehicular communications. This enabled us to obtain optimized detectors based on the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm while circumventing the use of full channel state information (CSI) for computing likelihoods and trellis state transition probabilities. First, we extended the application of the neural network (NN)-aided BCJR, recently proposed for ISI channels with additive white Gaussian noise (AWGN). Although suitable for estimating likelihoods via labeling of transmission sequences, the BCJR-NN method does not provide a framework for learning the trellis state transitions. In addition to detection over the joint ISI and IN states we also focused on another scenario where trellis transitions are not trivial: detection for the ISI channel with AWGN with inaccurate knowledge of the channel memory at the receiver. Without access to the accurate state transition matrix, the BCJR- NN performance significantly degrades in both settings. To this end, we devised an alternative approach for data-driven BCJR detection based on the unsupervised learning of a hidden Markov model (HMM). The BCJR-HMM allowed us to optimize both the likelihood function and the state transition matrix without labeling. Moreover, we demonstrated the viability of a hybrid NN and HMM BCJR detection where NN is used for learning the likelihoods, while the state transitions are optimized via HMM. While reducing the required prior channel knowledge, the examined data-driven detectors with learned trellis state transitions achieve bit error rates close to the optimal full CSI-based BCJR, significantly outperforming detection with inaccurate CSI.