Gaussian Process Methods for Estimating Radio Channel Characteristics
Anton Ottosson, Karlstrand, Viktor · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2020
Gaussian processes (GPs) as a Bayesian regression method have been around for some time. Since proven advant-ageous for sparse and noisy data, we explore the potential of Gaussian process regression (GPR) as a tool for estimating radiochannel characteristics. Specifically, we consider the estimation of a time-varying continuous transfer function from discrete samples. We introduce the basic theory of GPR, and employ both GPR and its deep-learning counterpart deep Gaussian process regression (DGPR)for estimation. We find that both perform well, even with few samples. Additionally, we relate the channel coherence bandwidth to a GPR hyperparameter called length-scale. The results show a tendency towards proportionality, suggesting that our approach offers an alternative way to approximate the coherence band-width.