  Electromagnetic information theory (EIT) is an emerging interdisciplinary
subject that integrates classical Maxwell electromagnetics and Shannon
information theory. The goal of EIT is to uncover the information transmission
mechanisms from an electromagnetic (EM) perspective in wireless systems.
Existing works on EIT are mainly focused on the analysis of EM channel
characteristics, degrees-of-freedom, and system capacity. However, these works
do not clarify whether EIT can improve wireless communication systems. To fill
in this gap, in this paper, we provide a novel example that EIT can improve the
performance of classical minimum mean squared error (MMSE) channel estimators
by replacing the channel covariance matrix with an EM correlation function
(EMCF). Specifically, by averaging the solutions of Maxwell's equations over a
tunable angular distribution, we obtain a spatio-temporal correlation function
(STCF) of the EM channel, which we name as the EMCF. Since classical MMSE
estimators can exploit prior information contained in the channel covariance
matrix, the substitution of EMCF for the covariance matrix introduces EM side
information into MMSE estimators. Furthermore, we dynamically tune the EMCF
parameters to better fit the channel observations. Simulation results show that
the proposed EIT-MMSE channel estimator outperforms traditional MMSE
estimators, thus proving that EIT is beneficial to wireless communication
systems.
