An Interpretable Low-complexity Model for Wireless Channel Estimation
CoRR(2024)
摘要
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 -40dB, for
SNR ≥ 30dB 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.
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