An Efficient Wireless Channel Estimation Model for Environment Sensing

TL;DR

This paper introduces a machine learning-based wireless channel estimation model using a tapped delay line approach, enabling detailed environment sensing and change detection with high accuracy and low computational requirements.

Contribution

The paper proposes a novel TDL-based channel estimation scheme that enhances environment awareness and detects dynamic changes, outperforming traditional CSI methods.

Findings

Accurately estimates path delays and gains with RMS error < -40dB at high SNR.

Detects interference from flying drones in estimated channel states.

Demonstrates low computation time and minimal training data needs.

Abstract

This paper presents a novel and efficient wireless channel estimation scheme based on a tapped delay line (TDL) model of wireless signal propagation, where a data-driven machine learning approach is used to estimate the path delays and gains. The key motivation for our novel channel estimation model is to gain environment awareness, i.e., detecting changes in path delays and gains related to interesting objects and events in the field. The estimated channel state provides a more detailed measure to sense the field than the single-tap channel state indicator (CSI) in current OFDM systems. Advantages of this approach also include low computation time and training data requirements, making it suitable for environment awareness applications. We evaluate this model's performance using Matlab's ray-tracing tool under static and dynamic conditions for increased realism instead of the standard evaluation approaches that rely on classical statistical channel models. Our results show that our TDL-based model can accurately estimate the path delays and associated gains for a broad-range of locations and operating conditions. Root-mean-square estimation error was less than $10^{-4}$, or $-40$dB, for SNR $\geq 60$dB in all of our experiments. Our results show that interference of a flying drone on signal multipaths, in a preliminary experiment, can be detected in estimated channel states which, otherwise, remains obscured in conventional CSI.