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Channel feature transfer method based on recurrent generative adversarial network

A channel characteristic and channel technology, applied in the field of channel characteristic migration based on cyclic generative adversarial network, can solve the problem of missing input signal and output signal sample, etc.

Active Publication Date: 2022-08-02
NO 54 INST OF CHINA ELECTRONICS SCI & TECH GRP
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0012] Aiming at the difficulties existing in the migration of existing channel characteristics, that is, the problem of self-adaptive processing of channel characteristic migration under complex channel characteristics and the lack of channel input signal and output signal sample pairs, the present invention proposes a channel characteristic migration method based on generative confrontation network

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  • Channel feature transfer method based on recurrent generative adversarial network
  • Channel feature transfer method based on recurrent generative adversarial network
  • Channel feature transfer method based on recurrent generative adversarial network

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Experimental program
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Embodiment 1

[0102] This embodiment implements a method for migrating channel characteristics based on a generative adversarial network. The modulation mode of the data used is DBPSK, and the channel types used are flat fading channels and white noise channels. The implementation process of this embodiment is summarized as follows:

[0103] (1) Signal data set construction. A signal data set is generated for DBPSK modulation and flat fading channel + white noise channel.

[0104] (2) Use the convolutional neural network to realize the one-dimensional signal channel characteristic transfer each module of the GAN network.

[0105] (3) Perform the training of the channel feature transfer GAN network.

[0106] (4) Use the trained GAN network to generate channel characteristic transfer simulation signals.

[0107] (5) The quality evaluation of the channel characteristic migration simulation signal is carried out.

[0108] Each implementation link is described in detail below.

[0109] (1) ...

Embodiment 2

[0148]This embodiment implements a channel characteristic migration method based on a generative adversarial network, where the modulation mode of the data used is FSK, and the channel type used is a frequency selective channel. The implementation process of this embodiment is summarized as follows:

[0149] (1) Signal data set construction. Generate signal data sets for FSK modulation and frequency selective channels.

[0150] (2) Use the convolutional neural network to realize the one-dimensional signal channel characteristic transfer each module of the GAN network.

[0151] (3) Perform the training of the channel feature transfer GAN network.

[0152] (4) Use the trained GAN network to generate channel characteristic transfer simulation signals.

[0153] (5) The quality evaluation of the channel characteristic migration simulation signal is carried out.

[0154] Wherein, the steps (2), (3), and (4) are implemented in the same manner as in Embodiment 1, and will not be d...

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Abstract

The invention belongs to the technical field of wireless communication signal transmission channel simulation, and discloses a channel characteristic migration method based on a Cyclic Generative Adversarial Network (CycleGAN), which mainly includes the following links. Signal data set construction: Generate a variety of original modulated signals and channel superimposed signals as training data. Structure design of channel characteristic transfer network: Based on the Cycle‑GAN network framework, a channel characteristic transfer GAN network for one-dimensional signals is constructed. Network model training: Select signal samples for training to obtain the signal after channel migration, design the loss function and use the optimization algorithm to update the model parameters until the trained channel characteristic migration network is obtained. Channel characteristic migration simulation signal generation and evaluation: Calculate the bit error rate, frequency offset and amplitude fading of the signal after channel migration. The channel migration method designed by the present invention does not need to construct a mathematical model of the channel explicitly, and does not depend on the sample pair of the input signal and the output signal of the channel, and has practical application value.

Description

technical field [0001] The invention relates to the field of wireless communication signal transmission channel simulation, in particular to a channel characteristic migration method based on a cyclically generated confrontation network. Background technique [0002] The life of modern people is closely related to wireless signals. Devices such as smartphones and computers need to use wireless signals for communication. However, in the process of propagation, different propagation channels, that is, channels, will cause different levels of interference to the signal, which may cause signal distortion and other effects. Therefore, the research on channels is of great practical significance. As an important direction of channel research, channel characteristic migration has developed rapidly and has been widely used in military and civilian fields: in the military field, it can be used to interfere with the enemy's electronic communication system; in the civilian field, it can...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04B17/391G06N3/04G06K9/62
CPCH04B17/3912G06N3/045G06F18/214
Inventor 陈丽张君毅谭毅华刘芳冯奇闫培
Owner NO 54 INST OF CHINA ELECTRONICS SCI & TECH GRP