Millimeter Wave Channel Modeling via Generative Neural Networks

TL;DR

This paper introduces a generative neural network-based approach for detailed millimeter wave channel modeling, capturing complex propagation characteristics with minimal assumptions, demonstrated on 28GHz urban air-to-ground channels.

Contribution

It proposes a novel two-stage generative neural network framework for millimeter wave channel modeling, combining link state prediction with path parameter generation from data.

Findings

Accurately models 28GHz urban air-to-ground channels

Captures complex relationships with minimal prior assumptions

Demonstrates effectiveness using ray-traced data

Abstract

Statistical channel models are instrumental to design and evaluate wireless communication systems. In the millimeter wave bands, such models become acutely challenging; they must capture the delay, directions, and path gains, for each link and with high resolution. This paper presents a general modeling methodology based on training generative neural networks from data. The proposed generative model consists of a two-stage structure that first predicts the state of each link (line-of-sight, non-line-of-sight, or outage), and subsequently feeds this state into a conditional variational autoencoder that generates the path losses, delays, and angles of arrival and departure for all its propagation paths. Importantly, minimal prior assumptions are made, enabling the model to capture complex relationships within the data. The methodology is demonstrated for 28GHz air-to-ground channels in an urban environment, with training datasets produced by means of ray tracing.