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A channel estimation method based on deep learning and data-pilot assistance

A technology of deep learning and channel estimation, which is applied in channel estimation, baseband system, digital transmission system, etc., and can solve problems that do not take into account the influence of time-varying channel

Active Publication Date: 2022-05-10
ZHEJIANG UNIV
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Problems solved by technology

[0005] However, the AE-based scheme does not take into account the influence of channel time-variation, while the STA-DNN scheme is affected by the accuracy of the STA output

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  • A channel estimation method based on deep learning and data-pilot assistance
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Embodiment Construction

[0039] In order to describe the present invention more specifically, the technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0040] The present invention mainly comprises two parts in concrete implementation process: (1) determine the parameter of LSTM-MLP network and off-line training neural network; estimate.

[0041] A data frame of an OFDM system includes L OFDM symbols, and each OFDM symbol utilizes K orthogonal subcarriers to transmit data symbols and pilot symbols in parallel. Let l be the index of OFDM symbols in a frame, l∈[1,2,…,L], k be the subcarrier index, in is the set of subcarrier numbers used to transmit data symbols and pilots, K is The number of elements in the set; let X l (k) represents the symbol sent on the kth subcarrier in the lth OFDM symbol, then all the symbols transmitted on the lth OFDM symbol are Assuming that the channel has quasi-static...

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Abstract

The invention discloses a channel estimation method based on deep learning and data pilot assistance, and designs an LSTM-MLP network combining LSTM network and MLP, and the LSTM-MLP network learns the channel through a large number of channel data samples in the offline training process Time dependence and frequency dependence. In order to solve the problem of insufficient pilots, the present invention utilizes the DPA method to use the corrected data symbols in the demapping process as pilots to estimate the current channel value, providing more useful channel information as input for the LSTM-MLP network; at the same time, for To solve the error caused by channel noise and channel time-varying in the DPA process, the present invention uses the offline trained LSTM-MLP network to track the time-varying channel and eliminate noise, compensate for the error caused by the DPA process, and provide reliable signal demodulation channel estimate.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to a channel estimation method based on deep learning and data pilot assistance. Background technique [0002] In recent years, Orthogonal frequency division multiplexing (OFDM) technology has been widely used in communication system design because it can realize parallel transmission of high-speed serial data through frequency division multiplexing. However, in a more complex propagation environment, the wireless channel not only causes frequency selective fading and time selective fading due to multipath and Doppler effects, but also may be a non-stationary wireless fading channel, which makes the traditional channel estimation method in The OFDM system cannot get satisfactory performance under the limited pilot placement. Therefore, it has become a top priority to design an efficient channel estimation method for OFDM systems for dual-selective non-sta...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L25/02
CPCH04L25/0254H04L25/0204H04L25/021H04L25/022
Inventor 单杭冠潘景李荣鹏
Owner ZHEJIANG UNIV
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