Statistical Channel Modeling and Estimation for PMMA-Based Vehicle-Mounted Magnetic Communication Systems

In subterranean or disaster-stricken environments, unmanned vehicles demand robust communication systems capable of penetrating dense structural obstructions. Low-frequency permanent magnet mechanical antennas (PMMAs), employing rotating permanent magnets as compact antennas, demonstrate superior signal penetration in soil and water, along with advantages in miniaturization and power efficiency. However, vehicular magnetic communication (MC) utilizing mechanical antennas remains underdeveloped; Existing research predominantly assumes quasi-static antenna operation, overlooking realistic dynamic vibration scenarios inherent to mobile platforms. This paper establishes the statistical characterization of fast fading channels in MC systems via analytical derivation. We develop a boundary Chi-square distribution model based on vehicular vibration dynamics and formulate a corresponding channel estimation algorithm employing the Expectation-Maximization (EM) framework. Numerical validations confirm the channel's statistical properties and the algorithm's convergence behavior. The proposed algorithm demonstrates operational efficacy in dynamic environments, attaining sub-4% normalized root mean square error (NRMSE) at 15 dB signal-to-noise ratio (SNR).