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A method of OFDM channel estimation and signal detection based on deep learning

A channel estimation and signal detection technology, which is applied in the field of OFDM channel estimation and signal detection based on deep learning, can solve the problems of higher diversity of channel sample data and complex mechanism

Active Publication Date: 2021-01-12
BEIHANG UNIV
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Problems solved by technology

However, for the UAV data link OFDM channel estimation and signal detection problems in complex environments, the mechanism of multipath effects is more complex than that of general scenarios, and the diversity of channel sample data is required to be higher

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  • A method of OFDM channel estimation and signal detection based on deep learning
  • A method of OFDM channel estimation and signal detection based on deep learning
  • A method of OFDM channel estimation and signal detection based on deep learning

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Embodiment Construction

[0055] The specific implementation method of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0056] The present invention is an OFDM channel estimation and signal detection method based on deep learning, which uses a deep neural network to realize OFDM multipath channel estimation and original signal detection, and can quickly and accurately restore the original signal.

[0057] like figure 2 As shown, it specifically includes the following steps:

[0058] Step 1, generate a sample database of the OFDM multipath channel model matrix in a complex environment based on the existing Non-WSSUS channel model;

[0059] The present invention performs multi-path channel modeling in a complex environment based on a Non-Wide-Sense Stationary Uncorrelated Scattering (Non-WSSUS) channel model, and generates channel matrix sample data accordingly. The Non-WSSUS channel model is based on the time-frequency transformation function, w...

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Abstract

The invention discloses an OFDM channel estimation and signal detection method based on deep learning, which belongs to the field of unmanned aerial vehicle measurement and control communication. Firstly, based on the existing Non‑WSSUS channel model, a sample database of the OFDM multipath channel model matrix in a complex environment is generated; then, a neural network including a channel estimation subnetwork and a signal detection subnetwork is constructed, and the data samples of the multipath channel model matrix are used The neural network is trained; finally, the trained neural network is applied offline to the complex environment UAV OFDM data link system, and the channel is estimated and the signal is detected at the same time. The invention generates a channel sample set with a large amount of data that can reflect the characteristics of OFDM channels in a complex environment, so that the entire network can effectively reflect the nonlinear characteristics of wireless channels and transmission signals.

Description

technical field [0001] The invention belongs to the field of unmanned aerial vehicle measurement and control communication, and specifically refers to an OFDM channel estimation and signal detection method based on deep learning. Background technique [0002] The UAV data link is an important part of the UAV system, which realizes functions such as remote control telemetry and reconnaissance information return to the UAV platform. When the UAV performs tasks in a complex geographical terrain environment, the data link system is often affected by the "Multipath Effect (Multipath Effect)", that is, the data link receiver not only receives the direct wave from the transmitter, but also receives From different reflection surfaces in the environment (diffuse reflection or specular reflection) reflected waves with different amplitudes and phases, and then generate inter-symbol interference (Inter-Symbol-Interference, ISI), the width of the received signal is extended due to multip...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L25/02H04L25/03G06N3/04G06N3/08
CPCH04L25/0212H04L25/0242H04L25/03006G06N3/08G06N3/045
Inventor 刘春辉王美琳丁文锐
Owner BEIHANG UNIV
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