Channel knowledge map construction method based on expectation maximization algorithm

A technology of expectation maximization and knowledge map, applied in the field of sixth-generation communication, to achieve high estimation accuracy, overcome low accuracy, and wide application range

Pending Publication Date: 2022-01-14
SOUTHEAST UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to provide a channel knowledge map construction method based on the expectation maximization algorithm to solve the technical problem that t

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  • Channel knowledge map construction method based on expectation maximization algorithm
  • Channel knowledge map construction method based on expectation maximization algorithm
  • Channel knowledge map construction method based on expectation maximization algorithm

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Embodiment Construction

[0027] In order to better understand the purpose, structure and function of the present invention, a channel knowledge map construction method based on the expectation maximization algorithm of the present invention will be further described in detail in conjunction with the accompanying drawings.

[0028] For a wireless communication system comprising a fixed base station and a specific site with multiple mobile users, the present invention acquires the channel knowledge (such as path loss, angle of arrival, etc.) etc.) data X, which contains the user's location information q and the channel knowledge r of the corresponding location, according to the expert knowledge, the relevant channel knowledge is statistically modeled to obtain the corresponding probability density function p(r|q, θ), where θ represents the mixed channel The set of parameters for the model. The measurement data is collected by the network manager, combined with the probability density function of the cha...

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Abstract

The invention discloses a channel knowledge map construction method based on an expectation maximization algorithm. The method comprises the following steps: obtaining channel knowledge data of a local communication environment through any mode of offline ray tracing simulation, offline field measurement or online real-time measurement; carrying out statistical modeling on related channel knowledge according to expert knowledge, estimating K-class parameters of a mixed statistical model by using an EM algorithm, and constructing a channel knowledge map reflecting a local signal propagation environment; and when a user needs to communicate, obtaining real-time position information through GPS, Beidou, cellular positioning, laser radar and self-sensor positioning modes, and obtaining channel knowledge of a target position by using the previously constructed channel knowledge map and a mode based on inverse distance weighting. Therefore, the method is used for environment perception adaptive communication. According to the invention, the problems of low accuracy, large data storage capacity demand and high training complexity of a channel prediction method based on a pure model or pure data are solved, so that the cost for acquiring real-time channel state information is reduced.

Description

technical field [0001] The invention belongs to the technical field of the sixth generation communication, and in particular relates to a channel knowledge map construction method based on an expectation maximization algorithm. Background technique [0002] With the development of the sixth generation (6G) mobile communication technology research, the realization of ultra-wide coverage, ultra-large-scale links, ultra-high capacity, and extremely low-latency communication has become the focus of attention. Accurate prediction and estimation of wireless channels is the cornerstone of mobile communication network research, and is crucial to the design, analysis and optimization of wireless communication networks. For the 6G communication facing 2030, with the further increase of channel dimension and the huge overhead required for channel training, the development of next-generation communication technology is facing great challenges. However, due to the complexity of the actu...

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Application Information

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IPC IPC(8): H04W24/06H04L25/02H04B17/391H04W4/029G06K9/62
CPCH04W24/06H04L25/0202H04B17/3913H04W4/029G06F18/2321G06F18/23
Inventor 曾勇李坤李培铭许杰
Owner SOUTHEAST UNIV
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