Wireless body area network communication system interruption probability analyzing method based on k-mu shadow fading channel

Abstract

The invention provides a wireless body area network communication system interruption probability analyzing method based on a k-mu shadow fading channel. The method includes the steps of firstly establishing a system model, sequentially calculating the conditional probability density function, the unconditional probability density function and the generalized nonholonomic moment generating function of the signal-interference ratio of a receiver end, and finally calculating the interruption probability of the wireless body area network communication system under the co-channel interference condition. The method effectively solves the technical problem of lack of means for accurately analyzing the interruption probability properties of a wireless body area network communication system, and takes the influence of co-channel interference and additive white Gaussian noise on the system performance into consideration. An enclosed interruption probability expression is obtained by analyzing the statistical characteristics of the signal-interference noise ratio (SINR) of the receiving end, thereby providing theoretical foundation and basis for evaluating the influence of channel fading characteristics and co-channel interference on the wireless body area network communication system.

Description

Technical field [0001] The invention relates to a method for accurately analyzing the interruption probability of a wireless body area network communication system, and more specifically, to a wireless body area network communication system based on a κ-μ shadow fading channel in the case of co-channel interference. Analytical method. Background technique [0002] In the wireless body area network, the mobile device is usually close to the human body. Therefore, the communication link between the device and the device is susceptible to random obstruction by the surrounding environment and the human body, and is affected by the human body movement, and the shadow effect will be aggravated. The κ-μ channel model assumes that the received signal comes from multiple clusters, and the direct component is a deterministic value. Therefore, only the multipath effect of signal fading is considered, and the influence of random occlusion is ignored. Taking into account the shadow effect on...

AI Technical Summary

Problems solved by technology

[0002] In wireless body area networks, mobile devices are usually close to the human body. Therefore, the communication link between devices is vulnerable to the random occlusion of the surrounding environment and the human body, and the shadow effect will be exacerbated by the movement of the human body.

The κ-μ channel model assumes that the received signal comes from multiple clusters, and the direct component is a deterministic value. Therefore, only the multipath effect of signal fading is considered, and the influence of random occlusion is ignored.

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