A novel training sequence applied to DCS-based channel estimation

Xu, Wenjun; Yu, Xiao; Li, Yanfei; Si, Lu; Yang, Zhanxin

doi:10.1109/cc.2018.8543050

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…With the estimated CSI, transmitted symbols can be recovered at the receiver [7]. Only by adopting a suitable signal detection strategy for the OFDM wireless communication system, can the receiver detect OFDM signals with a lower bit error rate (BER) and thereby complete the whole high-quality signals recovery process [8]- [10]. Nowadays, deep learning, a key technology for artificial intelligence (AI), has attracted the attention of many researchers, and it has achieved good practical application results in the fields of image processing and speech recognition [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

A Signal Detection Scheme Based on Deep Learning in OFDM Systems

Pan¹,

Liu²,

Wang³

et al. 2021

Preprint

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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.

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