# Neural Network Detection of Data Sequences in Communication Systems

arXiv: https://arxiv.org/abs/1802.02046 · HTML: https://mlsanity.com/paper/1802.02046/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2018.

## Built on by (most cited first, 16 of 16)

- [Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization](https://mlsanity.com/paper/2205.02640/) (2022)
- [Model-Based Machine Learning for Communications](https://mlsanity.com/paper/2101.04726/) (2021)
- [Deep Task-Based Analog-to-Digital Conversion](https://mlsanity.com/paper/2201.12634/) (2022)
- [RC-Struct: A Structure-based Neural Network Approach for MIMO-OFDM Detection](https://mlsanity.com/paper/2110.02219/) (2021)
- [Knowledge Distillation Based Semantic Communications For Multiple Users](https://mlsanity.com/paper/2311.13789/) (2023)
- [Blind Channel Estimation and Joint Symbol Detection with Data-Driven Factor Graphs](https://mlsanity.com/paper/2401.12627/) (2024)
- [CMDNet: Learning a Probabilistic Relaxation of Discrete Variables for Soft Detection with Low Complexity](https://mlsanity.com/paper/2102.12756/) (2021)
- [Advancing Security in AI Systems: A Novel Approach to Detecting Backdoors in Deep Neural Networks](https://mlsanity.com/paper/2403.08208/) (2024)
- [Deep OFDM Channel Estimation: Capturing Frequency Recurrence](https://mlsanity.com/paper/2401.05436/) (2024)
- [Deep Learning-Based Detection for Marker Codes over Insertion and Deletion Channels](https://mlsanity.com/paper/2401.01155/) (2024)
- [On the Robustness of Deep Learning-aided Symbol Detectors to Varying Conditions and Imperfect Channel Knowledge](https://mlsanity.com/paper/2401.12645/) (2024)
- [Data-Driven Symbol Detection for Intersymbol Interference Channels with Bursty Impulsive Noise](https://mlsanity.com/paper/2405.10814/) (2024)
- [The Role, Trends, and Applications of Machine Learning in Undersea Communication: A Bangladesh Perspective](https://mlsanity.com/paper/2503.00669/) (2025)
- [Interference Cancellation GAN Framework for Dynamic Channels](https://mlsanity.com/paper/2208.08019/) (2022)
- [Learning Short Codes for Fading Channels with No or Receiver-Only Channel State Information](https://mlsanity.com/paper/2409.08581/) (2024)
- [Masked Neural Detection for Run-Length-Limited Channel Coding in Molecular Communication](https://mlsanity.com/paper/2606.12489/) (2026)

## Abstract (arXiv)

We consider detection based on deep learning, and show it is possible to train detectors that perform well without any knowledge of the underlying channel models. Moreover, when the channel model is known, we demonstrate that it is possible to train detectors that do not require channel state information (CSI). In particular, a technique we call a sliding bidirectional recurrent neural network (SBRNN) is proposed for detection where, after training, the detector estimates the data in real-time as the signal stream arrives at the receiver. We evaluate this algorithm, as well as other neural network (NN) architectures, using the Poisson channel model, which is applicable to both optical and molecular communication systems. In addition, we also evaluate the performance of this detection method applied to data sent over a molecular communication platform, where the channel model is difficult to model analytically. We show that SBRNN is computationally efficient, and can perform detection under various channel conditions without knowing the underlying channel model. We also demonstrate that the bit error rate (BER) performance of the proposed SBRNN detector is better than that of a Viterbi detector with imperfect CSI as well as that of other NN detectors that have been previously proposed. Finally, we show that the SBRNN can perform well in rapidly changing channels, where the coherence time is on the order of a single symbol duration.