We investigate the viability of using machinelearning techniques for estimating user-channel features at a large-array base station (BS). In the scenario we consider, userpilot broadcasts are observed and processed by the BS to extract angle-of-arrival (AoA) specific information about propagationchannel 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.
Navabi et al. REF have introduced a channel prediction using neural network, which aims to predict the angle-of-departure (AoD) of dominant path in base stations for mobile communications.
Predicting Wireless Channel Features Using Neural Networks
2018 IEEE International Conference on Communications (ICC)
2018 IEEE International Conference on Communications (ICC)
Computer Science Engineering Mathematics