Electromagnetic Information Theory: Fundamentals, Paradigm Shifts, and Applications

TL;DR

This work presents electromagnetic information theory (EIT) as a framework that integrates Shannon information theory with Maxwellian electromagnetism to model and optimize next-generation MIMO systems. It introduces an EM-precoding/EM-combining paradigm and a dyadic Green’s function–based channel model that supports arbitrary antenna shapes and precision in channel characterization, unifying hardware imperfections with propagation physics. The paper applies EIT to densely spaced MIMO, near-field communications, and tri-polarized MIMO, providing both analytic capacity expressions, extended channel models, and validation through simulations and indoor/outdoor measurements, demonstrating the benefits and practical challenges. Overall, EIT delivers physically grounded models and design tools that can improve spectral efficiency and reliability for 5G‑Advanced and beyond, while outlining concrete research directions to overcome real‑world engineering hurdles.

Abstract

This paper explores the emerging research direction of electromagnetic information theory (EIT), which aims to integrate traditional Shannon-based methodologies with physical consistency, particularly the electromagnetic properties of communication channels. We propose an EIT-based multiple-input multiple-output (MIMO) paradigm that enhances conventional spatially-discrete MIMO models by incorporating the concepts of electromagnetic (EM) precoding and EM combining. This approach aims to improve the modeling of next-generation systems while remaining consistent with Shannon's theoretical foundations. We explore typical EIT applications, such as densely spaced MIMO, near-field communications, and tri-polarized antennas, and analyze their channel characteristics through theoretical simulations and measured datasets. The paper also discusses critical research challenges and opportunities for EIT applications from an industrial perspective, emphasizing the field's potential for practical applications.

Figures (14)

• Figure 1: Block diagrams of the conventional MIMO paradigm and the proposed EIT-based MIMO paradigm. a) Conventional MIMO paradigm; b) Proposed EIT-based MIMO paradigm.
• Figure 2: The proposed EIT-based model is able to account for arbitrary shapes of transmit antennas at the base station.
• Figure 3: The proposed EIT-based model is able to model arbitrary precisions of the channel characterization.
• Figure 4: Schematic diagram of the measurement equipment.
• Figure 5: Channel capacity with different antenna spacings based on the simulated channel.