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Cellular network base station state time-varying model establishing method based on Bayesian network

A Bayesian network and model building technology, which is applied to the establishment of time-varying statistical models of cellular network base station states. The field of cellular network base station state time-varying model establishment based on Bayesian structure learning can solve adaptive adjustment, Bayesian The computational complexity of the Si network structure is high, but the computational complexity is low, and the effect of low complexity is achieved.

Active Publication Date: 2015-09-30
XIDIAN UNIV
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  • Abstract
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Problems solved by technology

[0006] The purpose of the present invention is to overcome the deficiency of above-mentioned learning method, has proposed a kind of cellular network base station state time-varying model establishment method based on Bayesian network, to solve Bayesian network structure calculation complexity is higher and can not according to the number of network nodes Changes to make adaptive adjustments, and apply the Bayesian network with low computational complexity to the establishment of the time-varying model of the state of the cellular network base station in the mobile communication system, thereby reducing the complexity of establishing the time-varying model of the state of the cellular network base station

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  • Cellular network base station state time-varying model establishing method based on Bayesian network
  • Cellular network base station state time-varying model establishing method based on Bayesian network
  • Cellular network base station state time-varying model establishing method based on Bayesian network

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Embodiment 1

[0053] In the field of modern mobile communications, the data transmission of mobile primary users in the actual cellular network is affected by the dynamic change of the base station switch state. The prediction of the time-varying law of the base station state in the cellular network can reduce the collision of mobile user services and increase the reliability of data transmission. To study the prediction of the time-varying law of the base station state needs to reduce the complexity and increase the reliability. In this regard, the typical Bayesian network structure can be used to reduce the complexity of learning the Bayesian structure to reduce the state of the cellular network base station. The complexity of the time-varying model.

[0054] In order to achieve effective low-complexity prediction of cellular network base station status, refer to figure 1 , the present invention proposes a method for establishing a time-varying model of a cellular network base station sta...

Embodiment 2

[0060] The cellular network base station state time-varying model establishment method based on Bayesian network is the same as embodiment 1, wherein the establishment of Bayesian network model in step (2) includes the following steps:

[0061] 2.1. Define the nodes of the Bayesian network:

[0062] The base station state f perceived by the secondary sensing device i,t Defined as a Bayesian network node, the nodes are ordered in the time domain, and the state of the i-th base station observed by the secondary sensing device at time t, f i,t ∈O={0,1}, O represents the state set, 0 and 1 represent the base station off (off) and on (on) state, respectively, i∈M={1,2,...M}, M represents the observed base station Sequence, t∈T={1,2,...T}, T represents the observation time sequence. In this example, the secondary sensing device perceives the state of the base station every 1 second, and the state of the base station observed by the secondary sensing device is randomly generated by...

Embodiment 3

[0069] The cellular network base station state time-varying model establishment method based on Bayesian network is the same as embodiment 1 and embodiment 2, and its step 2.3 described obtains the conditional probability table by the learning of Bayesian network structure and comprises the following steps:

[0070] 2.3a Calculate the parameter matrix C from the first-order fully connected Bayesian network established in 2.1 and 2.2 above:

[0071]

[0072] In the formula, C[x,M-i+1] represents the element of row x and column M-i+1 of parameter matrix C, x=0,1,...,2 M -1, i={1,2,...M}; M represents the maximum value of the number of observed base stations; Indicates rounding down operation; \Indicates touch operation;

[0073]2.3b Calculate the operator parameter matrix F according to the parameter matrix C obtained above:

[0074] F = nom ( F ) / ( nom...

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Abstract

The invention discloses a cellular network base station state time-varying model establishing method based on a Bayesian network. The cellular network base station state time-varying model establishing method comprises the following steps of (1) using the existing actual cellular network as a scene, sensing states of a base station switch in a system model by using secondary sensing equipment in a cellular network, collecting sensing data and forming an observation sequence; (2) creating a Bayesian network model by using the observation sequence and learning the model according to a Bayesian structure learning algorithm of a totally connected graph and condition mutual information to obtain a value of a dependency relation between a conditional probability chart and nodes; and (3) establishing a time-varying statistic model of the states of the cellular network base station by using the value of the dependency relation between the conditional probability chart and the nodes. By the cellular network base station state time-varying model establishing method, the problem that the existing method is high in complexity and cannot be adaptively adjusted along with change of the nodes of the network is solved, by the base station state time-varying model with low complexity, data business collision probability of master mobile users of a cellular network is reduced effectively, and data transmission efficiency in the network is improved.

Description

technical field [0001] The invention relates to the field of communication technology, in particular to the field of mobile communication, and specifically relates to a method for establishing a time-varying model of a cellular network base station state based on Bayesian structure learning, which is suitable for establishing a time-varying statistical model of a cellular network base station state. Background technique [0002] In recent years, the purpose of cognitive wireless network design is no longer just to improve spectrum utilization, but has broader goals, such as: higher quality of service, low energy consumption, etc. To achieve the above goals, statistical knowledge of primary network state resource management and system control for ideal cognitive radio operation based on the integration of spectrum sensing, environment learning, statistical reasoning and predictive behavior becomes necessary. The establishment of such statistical knowledge will go beyond many ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W16/22H04W24/02
CPCH04W16/22H04W24/02H04W88/08
Inventor 韩维佳张莹莹盛敏张琰王玺钧李建东腾伟
Owner XIDIAN UNIV
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