An Interpretable Low-complexity Model for Wireless Channel Estimation

With the advent of machine learning, there has been renewed interest in the problem of wireless channel estimation. This paper presents a novel low-complexity 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. Advantages of this approach include low computation time and training data requirements, as well as interpretability since the estimated model parameters and their variance provide comprehensive representation of the dynamic wireless multipath environment. 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 using 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 remained less than $10^{-4}$, or $-40$dB, for SNR $\geq 30$dB in all of our experiments. The key motivation for the 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 channel state with multipath delays and gains is a detailed measure to sense the field than the single-tap channel state indicator calculated in current OFDM systems.