Signal detection method based on model-driven deep learning

A deep learning and signal detection technology, applied in neural learning methods, biological neural network models, channel estimation, etc., can solve problems such as low signal reliability
CN112637093AActive Publication Date: 2021-04-09QILU UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Applications(China)
Current Assignee / Owner
QILU UNIV OF TECH
Publication Date
2021-04-09

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Abstract

The invention relates to a signal detection method based on model-driven deep learning. According to the invention, a channel estimation and signal detection model is established based on an OFDM system. Channel estimation adopts a combined neural network model taking an MMSE estimator based on DFT and FC-DNN as sub-networks, pilot frequency data distributed in a self-adaptive mode are preprocessed through the MMSE estimator, DNN network initialization information is extracted, and a more accurate channel estimation model is obtained according to a training learning network ChannelEstNe. The SignalDetNet adopts a ZF equalization detection preprocessor, and the LSTM and the DNN form a combined network, so that final signal detection is realized, and an original signal is recovered. According to the structure, the mode that the OFDM system processes signals block by block is kept, sending data with linear and nonlinear distortion in the OFDM system can be recovered, the initialization training speed is higher by combining a traditional algorithm, and therefore the deployment efficiency is improved.
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Description

technical field

[0001] The invention belongs to the field of intelligent communication, and relates to a signal detection method based on model-driven deep learning, in particular to a signal detection method of an OFDM wireless communication receiver based on model-driven and deep learning. Background technique

[0002] The OFDM receiver scheme mainly includes two functional modules of channel estimation and signal detection, that is, firstly obtain accurate channel state information (CSI) through channel estimation, and then use the estimated CSI to restore the transmitted signal. Most of the traditional channel estimation and signal detection technologies use complex algorithms to improve the receiving performance of the communication system. However, for the current 5G wireless communication that requires high dimensions, high speed, and high density, the high complexity calculation greatly affects the communication performance. effectiveness. Intelligent communication ...

Claims

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