# A Signal Detection Scheme Based on Deep Learning in OFDM Systems

arXiv: https://arxiv.org/abs/2107.13423 · HTML: https://mlsanity.com/paper/2107.13423/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 24 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [An Iterative BP-CNN Architecture for Channel Decoding](https://mlsanity.com/paper/1707.05697/) (2017)
- [Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems](https://mlsanity.com/paper/2008.03977/) (2020)
- [Cognitive Internet of Things: A New Paradigm beyond Connection](https://mlsanity.com/paper/1403.2498/) (2014)
- [An Amateur Drone Surveillance System Based on Cognitive Internet of Things](https://mlsanity.com/paper/1711.10738/) (2017)
- [Deep learning for channel estimation in FSO communication system](https://mlsanity.com/paper/1909.11003/) (2019)

## Abstract (arXiv)

Channel estimation and signal detection are essential steps to ensure the quality of end-to-end communication in orthogonal frequency-division multiplexing (OFDM) systems. In this paper, we develop a DDLSD approach, i.e., Data-driven Deep Learning for Signal Detection in OFDM systems. First, the OFDM system model is established. Then, the long short-term memory (LSTM) is introduced into the OFDM system model. Wireless channel data is generated through simulation, the preprocessed time series feature information is input into the LSTM to complete the offline training. Finally, the trained model is used for online recovery of transmitted signal. The difference between this scheme and existing OFDM receiver is that explicit estimated channel state information (CSI) is transformed into invisible estimated CSI, and the transmit symbol is directly restored. Simulation results show that the DDLSD scheme outperforms the existing traditional methods in terms of improving channel estimation and signal detection performance.