CN-121984619-A - Heterogeneous network channel state cooperative sensing method

CN121984619ACN 121984619 ACN121984619 ACN 121984619ACN-121984619-A

Abstract

The application discloses a heterogeneous network channel state collaborative sensing method, which relates to the technical field of communication and comprises the steps of obtaining local channel state sensing observation values of all nodes, normalizing receiver sensitivity and a group of basic probability distribution values reflecting the support degree of evidence on channel mutex state assumption, quantifying the difference degree caused by interference among different nodes by utilizing evidence distances, determining the credibility of all the nodes and classifying the interference degree according to the difference degree, calculating a classification correction factor of each node, calculating a sensing observation value history consistency factor of all the nodes to quantify the reliability of the nodes, finally carrying out weighted fusion on the normalized receiver sensitivity, the classification correction factor and the sensing observation value history consistency factor to obtain fusion weights of all the nodes, and further carrying out weighted fusion on the local channel state sensing observation values to obtain a final channel state sensing result. The application can improve the perceptibility of the real state of the heterogeneous network channel.

Inventors

ZHAO ZHIYONG
MAO ZHONGYANG
PAN YAOZONG
WANG MENGJIAO

Assignees

中国人民解放军海军航空大学

Dates

Publication Date: 20260505
Application Date: 20260210

Claims (10)

1. The cooperative sensing method for the heterogeneous network channel state is characterized by comprising the following steps of: The method comprises the steps of obtaining a local channel state perception observation value, a normalized receiver sensitivity and a group of basic probability distribution values of a current statistical period of each node in a target area heterogeneous network, wherein the group of basic probability distribution values comprises a first basic probability distribution value and a second basic probability distribution value, the first basic probability distribution value and the second basic probability distribution value respectively represent the support degree of evidence on a first assumption and a second assumption, the first assumption indicates that no service data pulse exists in a channel, the second assumption indicates that service data pulse exists in the channel, the evidence indicates an energy observation value, and the local channel state perception observation value is determined according to the number of flow pulses and the pulse duration; according to a group of basic probability distribution function values of all nodes, calculating evidence distances of any node pair, and determining the credibility of each node based on the evidence distances of all node pairs; Dividing all nodes into different interference levels according to the credibility of each node, and determining a classification correction factor of each node; Determining a historical consistency factor of the sensing observation value of each node according to the local channel state sensing observation value of the last statistical period of each node and the final channel state sensing result of the last statistical period; Determining fusion weights of all the nodes according to the normalized receiver sensitivity, the classification correction factors and the perceived observation history consistency factors of all the nodes; and according to the fusion weight of each node, carrying out weighted fusion on the local channel state sensing observation values of the current statistical period of each node to obtain the final channel state sensing result of the current statistical period.
2. The heterogeneous network channel state collaborative awareness method according to claim 1, wherein the local channel state awareness observations are determined according to a local channel state awareness observations calculation model, and wherein the mathematical expression of the local channel state awareness observations calculation model is: Wherein, the Representing nodes Is a local channel state aware observation of (a); The number of all available frequency points in the target area heterogeneous network is represented; Representing nodes The number of neighbor nodes within a hop range; And Respectively representing neighbor nodes in statistical period At the frequency point The total duration of all flow pulses received and the total duration of all flow pulses transmitted; And Respectively representing neighbor nodes in statistical period At the frequency point The number of kinds of all pulse waveforms received and the number of kinds of all pulse waveforms transmitted; And Respectively representing neighbor nodes in statistical period At the frequency point Last received first The number of flow pulses and the number of emitted pulses of the seed waveform The number of flow pulses of the seed waveform; And Respectively representing neighbor nodes in statistical period At the frequency point Last received first Pulse duration and firing of flow pulses of a seed waveform Pulse duration of the flow pulses of the seed waveform; And Respectively represent nodes in statistical period At the frequency point The total duration of all flow pulses received and the total duration of all flow pulses transmitted; And Respectively represent nodes in statistical period At the frequency point The number of kinds of all pulse waveforms received and the number of kinds of all pulse waveforms transmitted; And Respectively represent nodes in statistical period At the frequency point Last received first The number of flow pulses and the number of emitted pulses of the seed waveform The number of flow pulses of the seed waveform; And Respectively represent nodes in statistical period At the frequency point Last received first Pulse duration and firing of flow pulses of a seed waveform Pulse duration of the flow pulses of the seed waveform; representing the length of time of the statistical period.
3. The heterogeneous network channel state collaborative awareness method according to claim 1, wherein determining the fusion weight of each node according to the normalized receiver sensitivity, the classification correction factor and the awareness observer history consistency factor of each node specifically comprises: Determining the fusion weight of each node by using a fusion weight model according to the normalized receiver sensitivity, the classification correction factor and the perception observation value history consistency factor of each node, wherein the mathematical expression of the fusion weight model is as follows: Wherein, the Representing nodes Is a fusion weight of (2); Representing nodes Is provided for the normalized receiver sensitivity; Representing nodes Classification correction factors of (2); Representing nodes A perceived observation history consistency factor of (2); 、 And Respectively representing the normalized weights of the corresponding terms.
4. The heterogeneous network channel state collaborative awareness method according to claim 1, wherein the normalized receiver sensitivity is determined according to a normalized receiver sensitivity model, and the mathematical expression of the normalized receiver sensitivity model is: Wherein, the Representing nodes Is provided for the normalized receiver sensitivity; Representing nodes A receiver sensitivity maximum tolerance value; Representing within a statistical period Is a receiver sensitivity average detection value; Representing nodes Is used for the receiver sensitivity nominal value.
5. The method for collaborative awareness of heterogeneous network channel states according to claim 1, wherein the obtaining of a set of basic probability distribution function values for a current statistical period of each node specifically comprises: acquiring all energy observation values of the current statistical period of each node; and calculating a set of basic probability distribution function values of the current statistical period of each node according to all the energy observation values of the current statistical period of each node.
6. The collaborative awareness method of heterogeneous network channel states of claim 1 wherein a set of base probability distribution function values is determined based on a base probability distribution function model having a mathematical expression: Wherein, the Representing nodes Is a first basic probability distribution function value of (c), Indicating an assumption that no traffic data bursts are present in the channel; Representing nodes Is used to determine the second base probability distribution function value, Indicating an assumption that there is a traffic data burst in the channel; representing nodes within a statistical period Fusion data of all energy observations of (a); , Representing the number of sampling points in the statistical period; , representing nodes within a statistical period Signal to noise ratio of (2); ; 。
7. the heterogeneous network channel state collaborative awareness method according to claim 5, wherein the evidence distance is Jousselme, and the Jousselme distance is calculated according to the following formula: Wherein, the Representing nodes And node Jousselme distance of (a); Representing nodes Vectors corresponding to a set of basic probability distribution function values; Representing nodes A vector corresponding to a set of base probability distribution function values, Representing nodes Is a first basic probability allocation function value; Representing nodes Is a second basic probability allocation function value; , Representing an inner product operation; 。
8. The heterogeneous network channel state collaborative awareness method according to claim 6, wherein determining the credibility of each node based on the evidence distance of all node pairs comprises: For any node pair, determining the similarity of two nodes in the node pair by using a similarity calculation formula according to the evidence distance of the node pair, wherein the similarity calculation formula is as follows: Wherein, the Representing nodes And node Similarity of (2); representing the number of all nodes in the target area heterogeneous network; accumulating the similarity between the node and each other node aiming at any node to obtain the support degree of the node; For any node, calculating the reliability of the node according to the support degree and the reliability calculation formula of all the nodes, wherein the reliability calculation formula is as follows: Wherein, the Representing nodes Is the confidence level of (2); Representing nodes Is a support degree of (2); Representing nodes Is a support of (1).
9. The collaborative awareness method of heterogeneous network channel states according to claim 1, wherein the classification of all nodes into different interference levels and the determination of classification correction factors for each node are based on the credibility of each node, specifically comprising: Determining average credibility according to the credibility of all the nodes; dividing nodes with reliability higher than average reliability into undisturbed nodes, and setting a classification correction factor of the undisturbed nodes to be 1; Dividing nodes with reliability smaller than or equal to the average reliability and larger than 0.5 times of the average reliability into medium-degree interfered nodes, and calculating a classification correction factor of the medium-degree interfered nodes according to a medium-degree interference classification correction factor model, wherein the mathematical expression of the medium-degree interference classification correction factor model is as follows: Wherein, the A classification correction factor representing a moderately interfered node; representing the number of moderately interfered nodes; represent the first The credibility of each moderately interfered node; representing the number of all nodes in the target area heterogeneous network; Representing nodes Is the confidence level of (2); Dividing nodes with reliability less than or equal to 0.5 times of average reliability into severely interfered nodes, and calculating classification correction factors of the severely interfered nodes according to a severe interference classification correction factor model, wherein the mathematical expression of the severe interference classification correction factor model is as follows: Wherein, the A classification correction factor representing severely interfered nodes; representing the number of severely interfered nodes; represent the first The trustworthiness of the severely interfered node.
10. The heterogeneous network channel state collaborative awareness method of claim 1, wherein the awareness observation history consistency factor is determined from an awareness observation history consistency factor model having a mathematical expression: Wherein, the Representing nodes A perceived observation history consistency factor of (2); Representing nodes A local channel state aware observation of a last statistical period of (a); representing the final channel state sensing result of the last statistical period.

Description

Heterogeneous network channel state cooperative sensing method Technical Field The application relates to the technical field of communication, in particular to a cooperative sensing method for heterogeneous network channel states. Background Channel state sensing is an important research branch of radio spectrum sensing, generally adopts an energy detection mode to obtain a frequency resource use state, provides decision basis for service data access of users, and is beneficial to improving the utilization efficiency of network resources. The channel state sensing result is generally described in two modes, namely qualitative description and quantitative description. Qualitative description is to divide the channel state into busy and idle states, which can be accomplished by a single node through local awareness. The carrier sense technique employed by the CSMA (CARRIER SENSE Multiple Access) protocol is typically representative. The method is simple and easy to realize, but has larger dependence on the reliability of local sensing equipment, and the sensing performance is easily influenced by a hidden terminal, so that the application limitation is larger. The quantitative description is to quantitatively analyze the channel state, and the congestion degree of the current channel is represented by specific numerical values through carrying out flow pulse statistics on the dimensions of time domain, frequency domain, space domain, polarization domain and the like. The local perception method calculates a channel load statistic value by counting the flow pulse number of a time-frequency domain, and realizes channel state quantization. But the reliability of the sensing result is lower, the sensing capability of the local sensing device is greatly dependent, and the sensing result is easily influenced by noise uncertainty. The collaborative sensing method is that a plurality of nodes respectively count the flow pulse number and upload the flow pulse number to a fusion center, and fusion weights are distributed according to the distance between the nodes and the fusion center, and the final channel state quantized value is calculated in a fusion mode. The method overcomes the dependence on single node equipment and solves the problem of hidden terminals, but in the prior art, each node equipment is often assumed to be homogeneous, each communication network in a sensing area is considered to be isomorphic and have the same waveform system, and the influence caused by a complex electromagnetic environment is not considered, so that the calculation result is difficult to reflect the real state of a channel and has certain application limitation. Disclosure of Invention The application aims to provide a collaborative sensing method for heterogeneous network channel states, which can improve the sensing capability of the real states of the heterogeneous network channels. In order to achieve the above object, the present application provides the following solutions: in a first aspect, the present application provides a cooperative sensing method for heterogeneous network channel states, including: The method comprises the steps of obtaining a local channel state perception observation value, a normalized receiver sensitivity and a group of basic probability distribution values of a current statistical period of each node in a target area heterogeneous network, wherein the group of basic probability distribution values comprises a first basic probability distribution value and a second basic probability distribution value, the first basic probability distribution value and the second basic probability distribution value respectively represent the support degree of evidence on a first assumption and a second assumption, the first assumption indicates that no service data pulse exists in a channel, the second assumption indicates that service data pulse exists in the channel, the evidence indicates an energy observation value, and the local channel state perception observation value is determined according to the number of flow pulses and the pulse duration; according to a group of basic probability distribution function values of all nodes, calculating evidence distances of any node pair, and determining the credibility of each node based on the evidence distances of all node pairs; Dividing all nodes into different interference levels according to the credibility of each node, and determining a classification correction factor of each node; Determining a historical consistency factor of the sensing observation value of each node according to the local channel state sensing observation value of the last statistical period of each node and the final channel state sensing result of the last statistical period; Determining fusion weights of all the nodes according to the normalized receiver sensitivity, the classification correction factors and the perceived observation history consistency factors of all the nodes; an