Regional spectrum sensing equipment deployment method based on adaptive grid and dynamic clustering

Abstract

The invention belongs to the technical field of electromagnetic spectrum sensing monitoring, and particularly discloses a regional spectrum sensing equipment deployment method based on adaptive grids and dynamic clustering, which can accurately deploy equipment according to the actual distribution of electromagnetic spectrum activity through the adaptive grids and the dynamic clustering, improve the utilization efficiency of the equipment, avoid resource waste and reduce the cost. The method ensures that a key area and a high-activity area are monitored more densely, improves the monitoring precision and comprehensiveness, enhances the monitoring effect, dynamically adjusts the deployment position and number of equipment according to the real-time change and distribution characteristics of the electromagnetic spectrum activity in the area, remarkably improves the efficiency and precision of electromagnetic spectrum sensing, and improves the accuracy of the electromagnetic spectrum sensing. The purpose of meeting the monitoring requirement by using the least equipment is achieved, the flexibility and adaptability are high, different monitoring requirements and scenes are adapted, and the equipment purchase, installation and maintenance cost is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of electromagnetic spectrum perception and monitoring, and specifically discloses a method for deploying regional spectrum perception equipment based on adaptive grids and dynamic clustering. Background Art

[0002] With the advancement of science and technology, human activities utilizing the electromagnetic spectrum are increasing, and the management and monitoring of the electromagnetic spectrum in specific areas is becoming increasingly important. When conducting electromagnetic spectrum sensing in a specific area, how to achieve comprehensive and accurate spectrum monitoring using the minimum number of monitoring devices is a key issue. Currently, traditional task-driven electromagnetic spectrum monitoring often lacks flexibility and adaptability in equipment deployment, and cannot effectively optimize based on the actual distribution of electromagnetic spectrum activity in a region, resulting in inefficient equipment utilization and suboptimal monitoring results. Summary of the Invention

[0003] In order to solve the problems in the background technology, the present invention discloses a regional spectrum sensing device deployment method based on adaptive grid and dynamic clustering. According to the real-time changes and distribution characteristics of electromagnetic spectrum activities in the region, the deployment location and number of devices are dynamically adjusted to achieve the purpose of using the least equipment to meet monitoring needs, reduce equipment deployment costs, and significantly improve the efficiency and accuracy of electromagnetic spectrum perception.

[0004] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions:

[0005] A method for deploying regional spectrum sensing devices based on adaptive grids and dynamic clustering includes the following steps:

[0006] Step 1: Regional grid division and electromagnetic spectrum assessment

[0007] Initially divide the target area into grids based on its shape and size, and evaluate the intensity of electromagnetic spectrum activity in each grid by combining historical data, electromagnetic radiation source information, and field measurement results;

[0008] Step 2: Adaptive grid adjustment and dynamic clustering

[0009] Combined with the electromagnetic spectrum activity intensity assessment of each grid in step 1, adaptive grid adjustment is performed based on the actual activity intensity within the grid, subdividing grids with high activity intensity and merging grids with low activity intensity. Based on the similarity measurement between grids, a clustering algorithm is used to cluster the adjusted grids into multiple clusters, and the adjusted grids are dynamically clustered.

[0010] Step 3: Deploy sensing devices

[0011] Determine the core points of each cluster and deploy sensing devices at the core points to ensure effective coverage;

[0012] Step 4: Real-time Monitoring and Adjustment

[0013] Perform real-time monitoring. After a period of time, obtain the new electromagnetic spectrum activity intensity value of the adjusted grid. According to the new monitoring data, repeat Steps 1 to 3 to optimize the device deployment.

[0014] Furthermore, in the method for deploying regional spectrum sensing devices based on adaptive grids and dynamic clustering, in Step 3, the core point is determined by calculating the centroid or selecting the position with the maximum activity intensity in the grid as the core point.

[0015] Furthermore, in the method for deploying regional spectrum sensing devices based on adaptive grids and dynamic clustering, when performing regional grid division and electromagnetic spectrum evaluation, first evenly divide the target area into several initial grids. Assume the target area is rectangular, with length L and width W, and divide the area into n×m initial grids. The side lengths of each grid are Δx = L / n and Δy = W / m respectively;

[0016] Based on the statistical analysis of historical electromagnetic spectrum data, estimate the average electromagnetic spectrum activity intensity at the position of each grid (i,j) over a past period of time. Considering the type, power, distance, and propagation characteristics of known electromagnetic radiation sources in the vicinity, calculate the influence degree on the corresponding grid. Combining prior knowledge from on-site measurements or simulation results, obtain a more accurate initial activity intensity evaluation, and assign an electromagnetic spectrum activity intensity value Α to each grid (i,j). .

[0017] Furthermore, in the method for deploying regional spectrum sensing devices based on adaptive grids and dynamic clustering, according to experience, the overall electromagnetic spectrum activity level of the region, and the specific requirements of electromagnetic spectrum monitoring, determine the electromagnetic spectrum activity intensity threshold T for each initial grid;

[0018] When Α ij ≥T, further divide the grid into k 2 sub-grids, and the side length of each sub-grid is Δx / k and Δy / k;

[0019] When Aij < T, based on the adjacency relationship of the grids and the similarity of activity intensities, merge several adjacent low-activity intensity grids into a larger grid;

[0020] Define the similarity metric S ij,kl between grids, based on the following factors:

[0021] The difference in electromagnetic spectrum activity intensity values. Use Euclidean distance, Manhattan distance to compare the activity intensities of two grids. For example:

[0022] The positional relationship of the grids. Adjacent grids usually have a higher similarity;

[0023] The changing trend of electromagnetic spectrum activity intensity. If the activity intensity of two grids shows similar changing trends over a period of time, increase their similarity;

[0024] The adjusted grid is clustered into C clusters using the K-Means clustering algorithm, where each cluster is represented as Gc = {(i1, j1), (i2, j2), ...}.

[0025] Furthermore, the regional spectrum sensing device deployment method based on adaptive grid and dynamic clustering, for each cluster G c , determine the core points by any of the following methods:

[0026] (1) Calculate the coordinates of its center of mass (x c ,y c ), the centroid is calculated as: where |G c | represents cluster G c The number of grid cells;

[0027] (2) Select the location with the largest electromagnetic spectrum activity intensity value in the grid as the core point;

[0028] At each core point position (x c ,y c ) Deploy equipment while considering the actual performance parameters of the equipment, such as monitoring range, monitoring accuracy, and monitoring frequency band, to ensure that the cluster and surrounding areas can be effectively covered.

[0029] Furthermore, the regional spectrum sensing device deployment method based on adaptive grid and dynamic clustering obtains a new electromagnetic spectrum activity intensity value A' after a period of monitoring. ij ;

[0030] Recalculate the threshold T' and, based on the new data distribution and monitoring requirements, perform grid adjustment, clustering, and core point determination to optimize device deployment.

[0031] If the intensity of electromagnetic spectrum activity in a certain area increases or decreases significantly, it is necessary to re-grid and re-cluster, adjust the location of equipment, and increase or decrease the number of equipment;

[0032] Throughout the entire process, the goal is to minimize the number of devices N while meeting the accuracy and coverage requirements of electromagnetic spectrum sensing, that is, satisfying the following conditions:

[0033] (1)minN;

[0034] (2)st

[0035] (3)

[0036] in:

[0037] f(x c ,y c ) represents the core point (x c ,y c ) is used to describe the area range and monitoring effect that the device can effectively monitor at this location;

[0038] R is the total coverage requirement of the area, ensuring that the entire target area is within the monitoring range of the device and meets the specified coverage level;

[0039] It is the grid electromagnetic spectrum activity intensity value estimated based on the equipment monitoring data. The real-time activity intensity of each grid is estimated through equipment monitoring and data processing.

[0040] ∈ is the allowable error in accuracy, which is used to limit the deviation between the monitoring results and the actual electromagnetic spectrum activities to an acceptable range.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The method for deploying regional spectrum sensing equipment based on adaptive grid and dynamic clustering disclosed in the present invention can accurately deploy equipment according to the actual distribution of electromagnetic spectrum activities through adaptive grid and dynamic clustering, improve equipment utilization efficiency, avoid resource waste, ensure that key areas and high-activity areas are monitored more intensively, improve the accuracy and comprehensiveness of monitoring, enhance the monitoring effect, and dynamically adjust the deployment location and number of equipment according to the real-time changes and distribution characteristics of electromagnetic spectrum activities in the region, significantly improve the efficiency and accuracy of electromagnetic spectrum perception, and achieve the purpose of using the least equipment to meet monitoring needs. It has strong flexibility and adaptability, adapts to different monitoring needs and scenarios, and reduces the cost of equipment procurement, installation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a flow chart of the regional spectrum sensing device deployment method based on adaptive grid and dynamic clustering of the present invention. DETAILED DESCRIPTION

[0044] In order to better understand the present invention, the content of the present invention is further clearly set forth below in conjunction with the examples, but the protection content of the present invention is not limited to the following examples. In the following description, a large number of specific details are provided in order to provide a more thorough understanding of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details.

[0045] Combined with attachment Figure 1 The present invention elaborates on the regional spectrum sensing device deployment method based on adaptive grids and dynamic clustering. Assuming that a given area of 10 square kilometers is to be sensed by the electromagnetic spectrum, it is initially divided into 10×10 grids based on a preliminary electromagnetic environment assessment. Based on historical data and field measurements, each grid is assigned an electromagnetic spectrum activity intensity value A. ij ;

[0046] Set a threshold T, subdivide grids with activity intensity higher than T, and merge grids with activity intensity lower than T;

[0047] When setting the threshold T, multiple factors need to be considered comprehensively: First, perform a statistical analysis of the electromagnetic spectrum activity intensity values of all grids in the area, and calculate their mean μ and standard deviation σ. A common setting method is to set the threshold T as the mean plus a multiple of the standard deviation, that is, T = μ + kσ, where k is a coefficient adjusted according to actual conditions. If the requirements for distinguishing electromagnetic spectrum activity are more stringent and you want to more accurately identify high-activity intensity areas for finer grid division, you can choose a larger k value. In this way, only those grids with activity intensity significantly higher than the average level will be further subdivided. Conversely, if you want to be relatively loose in the division of high and low activity intensity areas, or if the differences in electromagnetic spectrum activity within the area are not particularly significant, you can choose a smaller k value.

[0048] Another way to set the threshold T is based on experience and specific monitoring needs. For example, if it is known that there are specific hotspots of high electromagnetic spectrum activity in the area, such as large communication base stations, industrial areas, or important military facilities, and it is desired to separately segment these areas for treatment, then the threshold T can be set slightly lower than the activity intensity value of these hotspots.

[0049] In addition, the threshold T can be determined through multiple experiments and simulations. For different possible values, simulation analysis of equipment deployment can be performed to compare the equipment deployment effects under different thresholds, including indicators such as monitoring accuracy, coverage, and number of devices used. By comprehensively considering these factors, a threshold T value can be finally determined that can maximize the optimization of equipment deployment while meeting monitoring requirements.

[0050] Then, dynamic clustering is performed to determine the core points and deploy perception devices.

[0051] During equipment operation, changes in electromagnetic spectrum activity are monitored in real time, and grids, clusters, and equipment deployments are readjusted based on new data. If the intensity of electromagnetic spectrum activity in a certain area increases significantly, for example due to the commissioning of new communications facilities or sudden large-scale electromagnetic activity, the original equipment deployment may not meet monitoring requirements. In this case, the grid needs to be re-divided and further subdivided to improve monitoring accuracy. Clustering is then performed again, new core points are identified, and additional equipment deployment or adjustments to the location of existing equipment may be necessary. Conversely, if the intensity of electromagnetic spectrum activity in a certain area continues to decrease and remains at a low level for a period of time, consideration can be given to merging the grids in that area to reduce the monitoring burden on the equipment, and possibly removing some equipment as appropriate to improve overall equipment utilization efficiency.

[0052] Through practical application, it is verified that the method of the present invention can dynamically adjust the deployment location and number of equipment according to the real-time changes and distribution characteristics of electromagnetic spectrum activities in the region, while meeting the monitoring accuracy and coverage requirements, to ensure that key areas and high-activity areas are monitored more intensively, significantly improve the efficiency and accuracy of electromagnetic spectrum perception, improve the accuracy and comprehensiveness of monitoring, achieve the purpose of using the least equipment to meet monitoring needs, significantly reduce the number of deployed equipment, and reduce equipment deployment costs. It has broad application prospects and significant practical value.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limiting. Other modifications or equivalent substitutions made to the technical solution of the present invention by ordinary technicians in this field should be included in the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for deploying regional spectrum sensing devices based on adaptive grids and dynamic clustering, characterized by: The steps include: Step 1: Regional grid division and electromagnetic spectrum assessment Initially divide the target area into grids based on its shape and size, and evaluate the intensity of electromagnetic spectrum activity in each grid by combining historical data, electromagnetic radiation source information, and field measurement results; Step 2: Adaptive grid adjustment and dynamic clustering Combined with the electromagnetic spectrum activity intensity assessment of each grid in step 1, adaptive grid adjustment is performed based on the actual activity intensity within the grid, subdividing grids with high activity intensity and merging grids with low activity intensity. Based on the similarity measurement between grids, a clustering algorithm is used to cluster the adjusted grids into multiple clusters, and the adjusted grids are dynamically clustered. Step 3: Deploy sensing devices Determine the core points of each cluster and deploy sensing devices at the core points to ensure effective coverage; Step 4: Real-time monitoring and adjustment Real-time monitoring: After a period of time, obtain the new electromagnetic spectrum activity intensity value of the adjusted grid and repeat steps 1 to 3 based on the new monitoring data to optimize equipment deployment.

2. The method for deploying regional spectrum sensing devices based on adaptive grid and dynamic clustering according to claim 1, wherein: In step 3, the core point is determined by calculating the centroid or selecting the location with the maximum activity intensity in the grid.

3. The method for deploying regional spectrum sensing devices based on adaptive grid and dynamic clustering according to claim 1 or 2, characterized in that: When performing regional grid division and electromagnetic spectrum assessment, the target area is first evenly divided into several initial grids. Assuming the target area is a rectangle with a length of L and a width of W, the area is divided into n×m initial grids, and the side lengths of each grid are Δx = L / n and Δy = w / m. Based on the statistical analysis of historical electromagnetic spectrum data, the average electromagnetic spectrum activity intensity of each grid (i, j) in the past period of time is estimated. The type, power, distance and propagation characteristics of the surrounding known electromagnetic radiation sources are considered to calculate the degree of impact on the corresponding grid. Combined with the prior knowledge of field measurements or simulation results, a more accurate initial activity intensity assessment is obtained, and an electromagnetic spectrum activity intensity value A is assigned to each grid (i, j). ij .

4. The method for deploying regional spectrum sensing devices based on adaptive grid and dynamic clustering according to claim 3, wherein: Determine the electromagnetic spectrum activity intensity threshold T for each initial grid based on experience, the overall electromagnetic spectrum activity level in the region, and the specific needs of electromagnetic spectrum monitoring; When ij ≥T, the grid is further subdivided into k 2 subgrids, each with a side length of Δx / k and Δy / k; When ij When <T, based on the adjacent relationship of the grids and the similarity of activity intensity, several adjacent low-activity intensity grids are merged into a larger grid; The adjusted grid is clustered into C clusters using the K-Means clustering algorithm, where each cluster is represented as Gc = {(i1, j1), (i2, j2), ...}.

5. The method for deploying regional spectrum sensing devices based on adaptive grid and dynamic clustering according to claim 4, wherein: For each cluster G c , determine the core points by any of the following methods: (1) Calculate the coordinates of its center of mass (x c ,y c ), the centroid is calculated as: where |G c | represents cluster G c The number of grid cells; (2) Select the location with the largest electromagnetic spectrum activity intensity value in the grid as the core point; At each core point position (x c ,y c ) Deploy equipment while considering the actual performance parameters of the equipment, such as monitoring range, monitoring accuracy, and monitoring frequency band, to ensure that the cluster and surrounding areas can be effectively covered.

6. The method for deploying regional spectrum sensing devices based on adaptive grid and dynamic clustering according to claim 5, characterized in that: After a period of monitoring, a new electromagnetic spectrum activity intensity value A' is obtained ij ; Recalculate the threshold T' and, based on the new data distribution and monitoring requirements, perform grid adjustment, clustering, and core point determination to optimize device deployment. If the intensity of electromagnetic spectrum activity in a certain area increases or decreases significantly, it is necessary to re-grid and re-cluster, adjust the location of equipment, and increase or decrease the number of equipment; Throughout the entire process, the goal is to minimize the number of devices N while meeting the accuracy and coverage requirements of electromagnetic spectrum sensing, that is, satisfying the following conditions: (1)minN; (2)s.t. (3) in: f(x c ,y c ) represents the core point (x c ,y c ) is used to describe the area range and monitoring effect that the device can effectively monitor at this location; R is the total coverage requirement of the area, ensuring that the entire target area is within the monitoring range of the device and meets the specified coverage level; It is the grid electromagnetic spectrum activity intensity value estimated based on the equipment monitoring data. The real-time activity intensity of each grid is estimated through equipment monitoring and data processing. ∈ is the allowable error in accuracy, which is used to limit the deviation between the monitoring results and the actual electromagnetic spectrum activities to an acceptable range.

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