Fast-Fading Channel and Power Optimization of the Magnetic Inductive Cellular Network
<|reference_start|>Fast-Fading Channel and Power Optimization of the Magnetic Inductive Cellular Network: 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.<|reference_end|>
@article{ma2024fast-fading,
    title={Fast-Fading Channel and Power Optimization of the Magnetic Inductive
  Cellular Network},
    author={Honglei Ma and Erwu Liu and Zhijun Fang and Rui Wang and Yongbin Gao
  and Wenjun Yu and Dongming Zhang},
    journal={arXiv preprint arXiv:2406.04737},
    year={2024},
    doi={10.1109/TWC.2024.3425473 10.1109/TWC.2024.3425473 10.1109/TWC.2024.3425473},
    archivePrefix={arXiv},
    eprint={2406.04737},
    primaryClass={eess.SP cs.SY eess.SY}
}