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Millimeter Wave Channel Modeling via Generative Neural Networks

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Document pages: 6 pages

Abstract: Statistical channel models are instrumental to design and evaluate wirelesscommunication systems. In the millimeter wave bands, such models become acutelychallenging; they must capture the delay, directions, and path gains, for eachlink and with high resolution. This paper presents a general modelingmethodology based on training generative neural networks from data. Theproposed generative model consists of a two-stage structure that first predictsthe state of each link (line-of-sight, non-line-of-sight, or outage), andsubsequently feeds this state into a conditional variational autoencoder thatgenerates the path losses, delays, and angles of arrival and departure for allits propagation paths. Importantly, minimal prior assumptions are made,enabling the model to capture complex relationships within the data. Themethodology is demonstrated for 28GHz air-to-ground channels in an urbanenvironment, with training datasets produced by means of ray tracing.

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