Fast-Fading Channel and Power Optimization of the Magnetic Inductive Cellular Network

TL;DR

This paper models the fast-fading channel in vehicle magnetic induction communication, analyzes its impact on network throughput, and proposes power control algorithms to optimize performance in underground environments.

Contribution

It introduces a novel 3D fast-fading model for VMI channels and develops power control algorithms using game theory and Q-learning to enhance network throughput.

Findings

Fast-fading causes more uniformly distributed channel coefficients.

The derived CDF and PDF accurately characterize VMI fast-fading.

Proposed algorithms improve throughput in simulations.

Abstract

The cellular network of magnetic Induction (MI) communication holds promise in long-distance underground environments. In the traditional MI communication, there is no fast-fading channel since the MI channel is treated as a quasi-static channel. However, for the vehicle (mobile) MI (VMI) communication, the unpredictable antenna vibration brings the remarkable fast-fading. As such fast-fading cannot be modeled by the central limit theorem, it differs radically from other wireless fast-fading channels. Unfortunately, few studies focus on this phenomenon. In this paper, using a novel space modeling based on the electromagnetic field theorem, we propose a 3-dimension model of the VMI antenna vibration. By proposing ``conjugate pseudo-piecewise functions'' and boundary $p(x)$ distribution, we derive the cumulative distribution function (CDF), probability density function (PDF) and the expectation of the VMI fast-fading channel. We also theoretically analyze the effects of the VMI fast-fading on the network throughput, including the VMI outage probability which can be ignored in the traditional MI channel study. We draw several intriguing conclusions different from those in wireless fast-fading studies. For instance, the fast-fading brings more uniformly distributed channel coefficients. Finally, we propose the power control algorithm using the non-cooperative game and multiagent Q-learning methods to optimize the throughput of the cellular VMI network. Simulations validate the derivation and the proposed algorithm.