磁感应蜂窝网络的快衰落信道与功率优化
Fast-Fading Channel and Power Optimization of the Magnetic Inductive Cellular Network
AI总结：
研究VMI通信中快衰落现象，基于电磁场定理建模，推导相关函数并分析其对网络吞吐量的影响，用非合作博弈和多智能体Q学习方法提出功率控制算法优化VMI网络吞吐量，得出与无线快衰落研究不同的结论。
摘要：
磁感应（MI）通信的蜂窝网络在长距离地下环境中有应用前景。传统MI通信中MI信道被视为准静态信道无快衰落，但车辆（移动）MI（VMI）通信中，天线振动带来显著快衰落。本文基于电磁场定理提出新空间模型，推导VMI快衰落信道的相关函数，分析其对网络吞吐量的影响，得出不同结论。最后用非合作博弈和多智能体Q学习方法提出功率控制算法优化VMI网络吞吐量，仿真验证了推导和算法。
英文摘要：
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.
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