Channel modeling method based on channel feature generative adversarial networks
This paper proposes an improved model of generative adversarial networks(GAN)tailored for channel feature generation,named as channel feature generative adversarial networks(CFGAN).Using a completely unsupervised learning channel feature method,the model utilizes the mutual information relationship between the linear coding vector and the gen-erated channel,alongside variational mutual information maximization principles,to establish a correspondence between the coding vector and channel characteristics.The CFGAN model is trained using a dataset of measured indoor power line chan-nel data.The trained CFGAN can learn different channel feature distributions.Simulation shows that in a large dynamic range channel with an attenuation amplitude of-80～-10 dB,CFGAN can generate four types of channel models with signifi-cant differences based on the learned channel characteristics,and the difference in channel characteristics between the gen-erated channel and the measured channel is less than 2%.
generative adversarial networkschannel modelingmutual information
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