Withdrawn - 04. Using Generative AI to Simulate Realistic Millimeter-Wave Channel Measurements

Moving into the millimeter-wave (mmWave) wireless spectrum (30 – 300 GHz) is a critical next step for Wi-Fi, mobile devices, and many other applications that currently use sub-6 GHz bands, which are congested and do not offer the necessary data rates for future technology. Devices operating in the mmWave band, however, require advanced channel discovery (identifying paths between transmitter and receiver) and beam-steering capabilities in order to overcome the inherent limitations of these higher frequencies. Measuring mmWave channels in a lab is time-consuming, especially for time-varying channels, making it difficult to produce enough data to reliably evaluate different beam-steering algorithms or antenna designs. In this work, we explore the use of generative AI to produce synthetic channel data that could then be used in place of measured channel data. We discuss the challenges of creating realistic, dynamic mmWave channels by starting with simplistic numerical simulations to create a wide range of channel types and to add in extraneous channels that should be avoided by the beam-steering algorithm. We finish by training a generative AI algorithm on our simple simulated data, and evaluate whether this approach can produce unlimited new synthetic data with realistic properties. While the initial simulations naturally limit the dimension of the problem (perhaps to unrealistically small values), future work will improve on this and other features.

generative AI

synthetic data

wireless communication

millimeter-wave 

Mid-Level

Knowledge

Women in Statistics and Data Science 2025