Research On On - Body Wireless Channel Network Composite Channel Model

Nowadays, with the development of science and technology and the progress of society, social structure began to change and the aging problem was particularly prominent. In order to solve this problem, many scholars had done a lot of research and the proposing of wireless body area network (WBAN) was ready to resolve this problem. Wireless Body Area Network is a high-speed short-range wireless local area network, which the lightweight, compact ultra-low power smart wearable sensors were placed on the body surface or implanted into the body, real-time monitoring the wearer’s health condition. Doctors can access the collected data to real-time do some diagnosis and treatment for them. Because the signal’s transmission range of wireless body area network is generally confined to around the body, and which is a complex transport environment, communication channels as an integral part of the communication network, so the research of channel model for wireless body area network is essential.This article studied what channel models were suitable for on-body wireless body area network and took care of channel modeling and simulation problems, the main work of this paper consists of the following components:Firstly, the article detailed introduce the development of wireless body area network and channel models, namely applying common channel models to wireless body area network and established Rician method simulation algorithm which was used to simulate the channel models. Moreover, on the basis of the existing channel model, this paper developed a new channel model and deduced its probability density function.Secondly, this article also further researched the RLMN channel model and using a simple and direct method deduced its probability density function and cumulative distribution function. In addition to, the article simulated the probability density function, cumulative distribution function, level crossing rate and average fade duration of the RLMN channel model.Thirdly, the focus of this article was to research the composite model of WBAN channel to find the most suitable WBAN channel model. First of all, the channel characteristics of the actual body area network was measured and using RMS algorithm separated the measured data to fast fading and low fading signal. In addition that the separated signals was estimated using moment estimation method and the parameters was certificated using chi-square test method. Finally, logarithmic method of moment was used for the estimation of α-μ-lognormal composite model and RLMN composite model to get their MGF in logarithmic form. Then the estimated parameters were got according to the determined MGF based on the measured data. The first-order such as the CDF and PDF and second order statistical characteristics such as LCR and ADF were simulated by adjusting RLMN model different factors. The simulation proved the correctness of estimated parameters and applicability of the model.