The Benefits of Electromagnetic Information Theory for Channel Estimation

• Published in: IEEE International Conference on Communications (2003) pp. 4869 - 4874
• Main Authors: Zhu, Jieao, Su, Xiaofeng, Wan, Zhongzhichao, Dai, Linglong, Cui, Tie Jun
• Format: Conference Proceeding
• Language: English
• Published: IEEE 09.06.2024
• Subjects: Channel estimation; Electromagnetic information theory (EIT); Electromagnetics; Feature extraction; Gaussian process regression (GPR); Gaussian processes; Kernel; kernel learning; spatial correlation function (SCF); Time-varying channels; Wireless communication
• ISSN: 1938-1883
• DOI: 10.1109/ICC51166.2024.10622245

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 degrees-of-freedom (DoF), system capacity, and characteristics of the electromagnetic channel. However, these works do not clarify whether EIT can improve wireless communication systems. To answer this question, in this paper, we provide a novel example of how to improve channel estimators by integrating EM knowledge into the classical MMSE channel estimator. Specifically, the EM knowledge is first encoded into a spatial correlation function (SCF) of the channel, which we term the EM kernel. This EM kernel plays the role of side information to the channel estimator. Since the EM kernel takes the form of Gaussian processes (GP), we propose the EIT-based Gaussian process regression (EIT-GPR) to derive the channel estimations. Furthermore, we propose EM kernel learning to fit the EM kernel to channel observations. Simulation results show that EIT benefits the channel estimator and enables it to outperform traditional isotropic MMSE algorithm, thus proving the practical values of EIT.