A Random Field-Theoretic Framework for Electromagnetic Information Computation

We present a comprehensive full-wave random field (RF) Green’s function (GF) computational framework tailored to electromagnetic (EM) information theory (EIT) and stochastic electromagnetics, with direct relevance to communication systems. The framework is anchored in exact dyadic Green’s functions, including the transceiver antenna current Green’s function (ACGF), hence shifting the emphasis from internal structural details to input–output relations aligned with the spirit of signal processing principles. The Karhunen–Loève expansion (KLE) is employed to represent all fields in terms of orthogonal modal bases, enabling physically consistent simulations over arbitrary spatial domains. Our results demonstrate that, even for input Gaussian RFs, EM communication systems may give rise to non-Gaussian and improper complex RFs. We also show how mutual information can be computed directly from intrinsic EM fluctuations in noise-free settings. Overall, this novel computational method furnishes a flexible and efficient platform for analyzing and optimizing EM information in emerging communication technologies.