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Sparse nonparametric body area channel probability representation method

A nonparametric, body domain technique for probabilistic representation based on finite samples

Inactive Publication Date: 2014-02-12
XIDIAN UNIV
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Problems solved by technology

However, these two methods require a large number of samples to maintain the accuracy required, and in practice only a limited number of samples can be obtained in the body center case

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  • Sparse nonparametric body area channel probability representation method
  • Sparse nonparametric body area channel probability representation method
  • Sparse nonparametric body area channel probability representation method

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Embodiment Construction

[0049] The sparse non-parametric probability characterization method in the present invention will be described in detail below with reference to the accompanying drawings.

[0050] The first step is to solve the empirical distribution function. The density function can be defined as:

[0051] F ( x ) = P ( X ≤ x ) = ∫ - ∞ x p ( t ) dt , - - - ( 1 )

[0052] where F(x) is the corresponding distribution function. by solving the equation The corresponding density function can be obtained, where The problem is that in the actual body center situation, F(x) is unknown, so it is imp...

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Abstract

The invention discloses a sparse nonparametric body area channel probability representation method. The method comprises the following steps: S1, adopting an electromagnetic wave transceiver in a body area network to collect data, S2, establishing an empirical distribution function and solving the empirical distribution function through a step function, S3, adopting an empirical distribution function approximate density function and establishing a regression function, S4, solving the regression function through linear programming, S5, obtaining a corresponding probability function through derivation of the regression function, and S6, evaluating and analyzing an obtained sparse nonparametric probability model. A representation model proposed in the method is not restricted by specific propagation circumstances, and is more applicable to wireless communication in a body area network because of nonparametric property. The method overcomes the problem that the requirements on simple capacity of traditional models are rigorous, ensures that coefficients of a large amount of linear combinations are zero by controlling the quantity of support vectors during the process of regression, and realizes the sparsification of regression coefficients.

Description

technical field [0001] The invention belongs to the technical field of radio wave propagation, and relates to a time domain finite difference technology and a channel representation method, in particular to a probability representation method based on finite samples of a body domain channel. Background technique [0002] For the statistical model of radio wave propagation, an important part is the representation of probability. In many communication environments, probability is used as an important means of characterizing radio wave propagation, such as terrestrial environments (see: Matthias Patzold, Ulrich Killat, and Frank Laue , "A Deterministic Digital Simulation Model for Suzuki Processes with Application to a Shadowed Rayleigh Land Mobile Radio Channel," IEEE Transactions on Vehicular Technology, Vol.45, No.2.pp.318-331, 1996.), Mobile Satellite Communications ( See: Chun Loo, "A Statistical Model for a Land Mobile Satellite Link," IEEE Transactions on Vehicular Techn...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 杨晓东卡马尔·侯赛因·阿巴西任爱锋张志亚赵伟
Owner XIDIAN UNIV
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