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In this paper, we present our research on modeling a synthetic light fidelity (LiFi) channel model that uses a deep learning architecture called generative adversarial networks (GAN). A research in LiFi that requires the generation of many multipath channel impulse responses (CIRs) can benefit from our proposed model. For example, future developments of autonomous (deep learning-based) network management systems that use LiFi as one of its high-speed wireless access technologies might require a dataset of many CIRs. In this paper, we use TimeGAN, which is a GAN architecture for time-series data. We will show that modifications are necessary to adopt TimeGAN in our use case. Consequently, synthetic CIRs generated by our model can track long-term dependency of LiFi multipath CIRs. The Kullback–Leibler divergence (KLD) is used in this paper to measure the small difference between samples of synthetic CIRs and real CIRs. Lastly, we also show a simple demonstration of our model that can run on a small virtual machine hosted over the Internet. |
