Gaussian Process Modeling of Specular Multipath Components

Abstract

The consideration of ultra-wideband (UWB) and mm-wave signals allows for a channel description decomposed into specular multipath components (SMCs) and dense/diffuse multipath. In this paper, the amplitude and phase of SMCs are studied. Gaussian Process regression (GPR) is used as a tool to analyze and predict the SMC amplitudes and phases based on a measured training data set. In this regard, the dependency of the amplitude (and phase) on the angle-of-arrival/angle-of-departure of a multipath component is analyzed, which accounts for the incident angle and incident position of the signal at a reflecting surface—and thus for the reflection characteristics of the building material—and for the antenna gain patterns. The GPR model describes the similarities between different data points. Based on its model parameters and the training data, the amplitudes of SMCs are predicted at receiver positions that have not been measured in the experiment. The method can be used to predict a UWB channel impulse response at an arbitrary position in the environment.

Gaussian Process for Radiance Functions on the Sphere

“…Gaussian processes (GPs) have proven to be a highly flexible modelling technique for spatial and temporal data or processes. GPs can suit a large variety of problems such as speech waveforms [Wil19], geophysical data [CVVMM*16], wireless communication channels [NRL*20], among others. Many well‐known stochastic processes such as Brownian motions, Langevin processes and Wiener processes are all special cases of GP.…”