# Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems

arXiv: https://arxiv.org/abs/2008.03977 · HTML: https://mlsanity.com/paper/2008.03977/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2020.

## Built on by (most cited first, 9 of 9)

- [RC-Struct: A Structure-based Neural Network Approach for MIMO-OFDM Detection](https://mlsanity.com/paper/2110.02219/) (2021)
- [Detect to Learn: Structure Learning with Attention and Decision Feedback for MIMO-OFDM Receive Processing](https://mlsanity.com/paper/2208.09287/) (2022)
- [Deep Learning in Wireless Communication Receiver: A Survey](https://mlsanity.com/paper/2501.17184/) (2025)
- [Agentic Wireless Communication for 6G: Intent-Aware and Continuously Evolving Physical-Layer Intelligence](https://mlsanity.com/paper/2602.17096/) (2026)
- [A Signal Detection Scheme Based on Deep Learning in OFDM Systems](https://mlsanity.com/paper/2107.13423/) (2021)
- [A Comparative Study of Deep Learning and Iterative Algorithms for Joint Channel Estimation and Signal Detection in OFDM Systems](https://mlsanity.com/paper/2303.03678/) (2023)
- [Digital Twin Enabled Site Specific Channel Precoding: Over the Air CIR Inference](https://mlsanity.com/paper/2501.16504/) (2025)
- [Brownian Bridge Diffusion-Based Joint Channel Estimation and Data Detection for Jamming-Resilient Receivers](https://mlsanity.com/paper/2606.28778/) (2026)
- [CRFCAN: A Complex-Valued Cross-Domain Residual Network for Joint Channel and Phase Noise Estimation in Sub-THz OFDM Systems](https://mlsanity.com/paper/2609.12244/) (2026)

## Abstract (arXiv)

In this paper, we propose a novel deep learning based approach for joint channel estimation and signal detection in orthogonal frequency division multiplexing (OFDM) systems by exploring the time and frequency correlation of wireless fading channels. Specifically, a Channel Estimation Network (CENet) is designed to replace the conventional interpolation procedure in pilot-aided estimation scheme. Then, based on the outcome of the CENet, a Channel Conditioned Recovery Network (CCRNet) is designed to recover the transmit signal. Experimental results demonstrate that CENet and CCRNet achieve superior performance compared with conventional estimation and detection methods. In addition, both networks are shown to be robust to the variation of parameter chances, which makes them appealing for practical implementation.