Predicting Wireless Channel Features using Neural Networks

TL;DR

This paper explores using neural networks to predict unobservable user-channel features, like AoD, from observable BS-side measurements, demonstrating potential for improved wireless channel estimation.

Contribution

It introduces a neural network-based approach to infer user-side channel features from BS observations using ray-tracing data calibrated with real measurements.

Findings

Neural networks can predict AoD from BS-observed features.

Correlations exist between BS measurements and user channel features.

Data-driven methods show promise for channel feature prediction.

Abstract

We investigate the viability of using machine-learning techniques for estimating user-channel features at a large-array base station (BS). In the scenario we consider, user-pilot broadcasts are observed and processed by the BS to extract angle-of-arrival (AoA) specific information about propagation-channel features, such as received signal strength and relative path delay. The problem of interest involves using this information to predict the angle-of-departure (AoD) of the dominant propagation paths in the user channels, i.e., channel features not directly observable at the BS. To accomplish this task, the data collected in the same propagation environment are used to train neural networks. Our studies rely on ray-tracing channel data that have been calibrated against measurements from Shinjuku Square, a famous hotspot in Tokyo, Japan. We demonstrate that the observed features at the BS side are correlated with the angular features at the user side. We train neural networks that exploit different combinations of measured features at the BS to infer the unknown parameters at the users. The evaluation based on standard statistical performance metrics suggests that such data-driven methods have the potential to predict unobserved channel features from observed ones.