Super-Resolution Estimation of UWB Channels including the Diffuse Component -- An SBL-Inspired Approach

TL;DR

This paper introduces an iterative, SBL-inspired algorithm for super-resolving and estimating both specular and diffuse components of UWB channels, demonstrating high accuracy and robustness even below the Rayleigh resolution limit.

Contribution

The novel algorithm effectively detects and estimates UWB channel components, including diffuse parts, outperforming existing methods and adapting thresholds based on extreme-value analysis.

Findings

Accurately detects specular components below Rayleigh resolution limit

Robustly estimates diffuse components and channel noise

Outperforms state-of-the-art super-resolution channel estimators

Abstract

In this paper, we present an iterative algorithm that detects and estimates the specular components and estimates the diffuse component of single-input-multiple-output (SIMO) ultra-wide-band (UWB) multipath channels. Specifically, the algorithm super-resolves the specular components in the delay-angle-of-arrival domain and estimates the parameters of a parametric model of the delay-angle power spectrum characterizing the diffuse component. Channel noise is also estimated. In essence, the algorithm solves the problem of estimating spectral lines (the specular components) in colored noise (generated by the diffuse component and channel noise). Its design is inspired by the sparse Bayesian learning (SBL) framework. As a result the iteration process contains a threshold condition that determines whether a candidate specular component shall be retained or pruned. By relying to results from extreme-value analysis the threshold of this condition is suitably adapted to ensure a prescribed probability of detecting spurious specular components. Studies using synthetic and real channel measurement data demonstrate the virtues of the algorithm: it is able to still detect and accurately estimate specular components, even when their separation in delay and angle is down to half the Rayleigh resolution limit (RRL) of the equipment; it is robust in the sense that it tends to return no more specular components than the actual ones. Finally, the algorithm is shown to outperform a state-of-the-art super-resolution channel estimator.