Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems
arXiv:2008.03977 · doi:10.1109/LCOMM.2020.3014382
Abstract
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.
arXiv commentTo appear in IEEE Communications Letters
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Cited by in corpus (7)
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- RC-Struct: A Structure-based Neural Network Approach for MIMO-OFDM Detection
- Detect to Learn: Structure Learning with Attention and Decision Feedback for MIMO-OFDM Receive Processing
- A Comparative Study of Deep Learning and Iterative Algorithms for Joint Channel Estimation and Signal Detection in OFDM Systems
- Deep Learning based Channel Extrapolation for Large-Scale Antenna Systems: Opportunities, Challenges and Solutions
- Digital Twin Enabled Site Specific Channel Precoding: Over the Air CIR Inference
- A Signal Detection Scheme Based on Deep Learning in OFDM Systems