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Channel simulation implementation method based on conditional generative adversarial network

A technology of condition generation and channel simulation, which is applied in the field of communication, can solve problems such as inapplicability, and achieve good simulation results

Active Publication Date: 2019-09-27
SHANGHAI UNIV
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Problems solved by technology

The channels generated by these "parameterized" methods are obviously not suitable for network performance evaluation.

Method used

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  • Channel simulation implementation method based on conditional generative adversarial network
  • Channel simulation implementation method based on conditional generative adversarial network
  • Channel simulation implementation method based on conditional generative adversarial network

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Embodiment Construction

[0026] Such as figure 1 As shown, it is a schematic diagram of the communication system involved in this embodiment, including: a transmitter, a channel, and a receiver. Taking an OFDM signal as an example, its fading model is y(t)=h[x(t)], where: x( t) is the transmitted signal, y(t) is the received signal, and the influence of noise is also included in the channel model h[·].

[0027] The specific steps of this embodiment include:

[0028] Step 1. Acquisition of the original data set of the paired sending signal and receiving signal used for cGAN training: Create an OFDM signal pairing data set, collect the sending signal and receiving signal pairing, and the modulation method adopts but is not limited to OFDM multi-carrier modulation, where: The frequency of N subcarriers is f k , k=1,2,...,N, the multi-carrier modulated signal in the ith OFDM symbol is: When the subcarrier adopts ordinary digital modulation, that is, without beamforming, the i-th OFDM symbol subcarrier...

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Abstract

The invention discloses a channel simulation implementation method based on a conditional generative adversarial network. An original data set is generated from actually sent and received signal pairs, then the original data set is expressed in a double-channel two-dimensional time-frequency domain signal mode and used for training of a conditional generative adversarial network model, and the trained conditional generative adversarial network model can accurately simulate channels. According to the method, the advantages of effectiveness and accuracy of the cGAN in learning data probability distribution and success of the cGAN in the field of image generation are applied to channel modeling. A number of measured and acquired transmission signals and reception signals are trained as paired data sets to train a generative adversarial network, and the generative network is equivalent to a model of the channel when a discriminator in the generative adversarial network cannot identify the reception signals in the real channel and the reception signals generated by the generative network.

Description

technical field [0001] The present invention relates to a technology in the communication field, in particular to a channel simulation implementation method based on conditional Generative Adversarial Nets (cGAN). Background technique [0002] For wireless communication systems, wireless channel modeling has always been a basic task for the theoretical analysis and practical application of wireless communication systems. An accurate channel model can help understand the physical impact of different wireless channels on transmitted signals. Existing channel modeling mainly relies on "parametric" methods, that is, relying on certain parameters to characterize complex wireless channel environments. Channels generated by these "parameterized" methods are obviously not suitable for network performance evaluation. Contents of the invention [0003] Aiming at the above-mentioned deficiencies in the prior art, the present invention proposes a channel simulation implementation met...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04B17/391
CPCH04B17/3911
Inventor 孙彦赞朱文星张舜卿吴雅婷方勇徐树公
Owner SHANGHAI UNIV
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