Spectrum sensor deployment optimization method

Abstract

The invention provides a spectrum sensor deployment optimization method and device, and belongs to the technical field of spectrum mapping, and the method comprises the steps: taking a spectrum map based on model estimation as priori knowledge, and obtaining a global spectrum data error value of an initial deployment position set; generating adjusted error uncertainty according to the global spectrum data error value, and constructing an optimal position deployment equation by taking the minimum difference between the error uncertainty of the initial deployment position and the error uncertainty of the adjusted deployment position as a target, so as to optimize the coordinates of the deployment position of the sensor by using a position optimization algorithm; and obtaining the coordinates of the sensor deployment position which enables the optimal position deployment equation to obtain the minimum value, obtaining a target deployment position set, and realizing spectrum sensor deployment optimization. According to the method, the limitation of an existing method can be overcome, the main thought is that the mutual relation and restricting factors between sensor deployment and spectrum data completion are utilized, and the technology can be effectively combined with spectrum data completion combining a model and data.

Description

Technical Field

[0001] This application relates to the technical field of spectrum mapping, and more specifically, to a method for optimizing the deployment of spectrum sensors. Background Art

[0002] Spectrum maps have a wide range of applications in the field of wireless communication, such as network planning, interference coordination and suppression, power control, resource allocation, multi-hop routing, dynamic spectrum access, etc., and have become one of the key technologies for electromagnetic environment perception. In current research and applications, due to the limited number of spectrum monitoring sensors, complex and variable geographical environments, and constraints such as communication, computing, and storage resources, generating spectrum maps faces a huge challenge of sparse and unevenly distributed sampled data in space. Aiming at the limitation of the number of spectrum monitoring sensors, how to achieve efficient sensor deployment so that the sampled spectrum data can reflect the overall characteristics or distribution to the greatest extent, thereby improving the accuracy of spectrum map generation, has become a new research hotspot. The present invention focuses on a method for improving the accuracy of spectrum map generation through the optimization of sensor deployment.

[0003] Existing research on sensor deployment mainly focuses on optimization problems in specific scenarios: one is to maximize the monitoring efficiency by maximizing the coverage rate of the target area under the condition of limited sensor numbers; the other is to maximize the economic benefits by minimizing the number of sensors used while meeting certain performance requirements. By optimizing the deployment scheme of a limited number of sensors, sampled spectrum data containing more spectrum situation correlation information can be obtained.

[0004] There are mainly three categories of sensor deployment optimization methods: simple deployment methods, including directly adopting uniform, random, and hierarchical methods; traditional deployment techniques, including the effective independent method and QR decomposition; intelligent optimization algorithms, such as genetic algorithms, particle swarm optimization, and simulated annealing methods. However, aiming at the practical problems of sparse spatial sampling and uneven sampling distribution, how to combine sensor deployment with specific spectrum data completion methods to maximize the total information content of the sampled data and thus generate a globally optimal spectrum map has become a research hotspot. Summary of the Invention

[0005] In view of at least one defect or improvement requirement of the prior art, the present invention provides a method for optimizing the deployment of spectrum sensors, which realizes generating a higher-precision spectrum map by optimizing the deployment positions of sensors under a limited number of sensors.

[0006] In a first aspect, the present invention provides a method for optimizing the deployment of spectrum sensors, including:

[0007] Obtaining the global spectrum data error value of the initial deployment position set;

[0008] Generate an adjusted error uncertainty based on the global spectrum data error value; wherein, the adjusted error uncertainty is the error uncertainty obtained by updating the uncertainty of the adjusted deployment position after adjusting the initial deployment position of the sensor.

[0009] Construct an optimal position deployment equation with the goal of minimizing the difference between the error uncertainties of the initial deployment position and the adjusted deployment position, so as to optimize the coordinates of the sensor deployment position using a position optimization algorithm.

[0010] Obtain the coordinates of the sensor deployment position that makes the optimal position deployment equation reach the minimum value, obtain the target deployment position set, and realize the optimization of the spectrum sensor deployment.

[0011] According to the spectrum sensor deployment optimization method described above, obtaining the global spectrum data error value of the initial deployment position set includes:

[0012] Obtain the global spectrum data error value based on the global spectrum data estimated value and the global spectrum data collected value of the initial deployment position set; wherein, the global spectrum data estimated value is the spectrum data generated by a propagation model constructed according to the prior information of the initial deployment position.

[0013] According to the spectrum sensor deployment optimization method described above, generating the adjusted error uncertainty based on the global spectrum data error value includes:

[0014] Obtain the initial error uncertainty based on the initial deployment position set;

[0015] Adjust the sensor deployment positions in the initial deployment position set to obtain an adjusted deployment position set;

[0016] Update the error uncertainty of the adjusted deployment position set based on the initial error uncertainty to obtain the updated error uncertainty.

[0017] According to the spectrum sensor deployment optimization method described above, updating the error uncertainty of the adjusted deployment position set based on the initial error uncertainty to obtain the updated error uncertainty includes:

[0018] Set the initial error uncertainty of the initial deployment coordinate points in the adjusted deployment position set to zero;

[0019] Update the initial error uncertainty of the optimized deployment coordinate points in the adjusted deployment position set, and the specific update method is:

[0020] u i =(1 - w)·u i

[0021] where u iis the uncertainty of the deployment coordinate point, w is the weight, and the way to obtain this weight is where d(s i , s j ) represents the distance between the initial deployment coordinate point s i and each point s i within the d-neighborhood of s j .

[0022] According to the spectrum sensor deployment optimization method described above, the optimal position deployment equation is specifically as follows:

[0023]

[0024] where s * is the next optimal position deployment coordinate point, J(·) is the total reduction in error uncertainty after optimizing the sensor deployment positions in the initial deployment position set, is the target deployment position set, and s is the deployment coordinate point.

[0025] According to the spectrum sensor deployment optimization method described above, the total reduction in error uncertainty after optimizing the sensor deployment positions in the initial deployment position set is specifically as follows:

[0026]

[0027] where represents the index set of all discrete coordinates whose distance from point s is less than d, and Δu i (s) is the absolute reduction in the posterior uncertainty of the i-th point after adding point s.

[0028] According to the spectrum sensor deployment optimization method described above, the coordinates of the sensor deployment position that minimizes the optimal position deployment equation are obtained using a greedy algorithm to obtain the target deployment position set.

[0029] According to the spectrum sensor deployment optimization method described above, based on the global spectrum data estimated value and the global spectrum data collected value of the initial deployment position set, the global spectrum data error value is obtained, including:

[0030] Obtain the global spectrum data estimated value, calculate the error based on the global spectrum data estimated value and the global spectrum data collected value to obtain the spectrum error value of the initial deployment position;

[0031] Perform a complement calculation on the spectrum error value using a spectrum data complementation algorithm to obtain the global spectrum data error complemented value;

[0032] Perform a normalization process on the global spectrum data error complemented value to obtain the global spectrum data error value.

[0033] In a second aspect, the present invention provides a spectrum sensor deployment optimization device, comprising:

[0034] a global spectrum data error value acquisition module, configured to acquire the global spectrum data error value of the initial deployment position set;

[0035] a first processing module, configured to optimize the sensor deployment positions in the initial deployment position set by using a sequential position optimization algorithm to obtain an adjusted deployment position set, and obtain an initial error uncertainty according to the adjusted deployment position set;

[0036] a second processing module, configured to update the error uncertainty of the adjusted deployment position set based on the initial error uncertainty to obtain an updated error uncertainty, and construct an optimal position deployment equation according to the updated error uncertainty and the adjusted deployment position set;

[0037] an optimization calculation module, configured to iteratively solve the optimal position deployment equation to obtain a target deployment position set, so as to implement spectrum sensor deployment optimization.

[0038] In a third aspect, the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, the steps of any one of the above-mentioned spectrum sensor deployment optimization methods are implemented.

[0039] The spectrum sensor deployment optimization method and device provided by the present invention use the spectrum map based on model estimation as prior knowledge to acquire the global spectrum data error value of the initial deployment position set. An adjusted error uncertainty is generated according to the global spectrum data error value, and an optimal position deployment equation is constructed with the goal of minimizing the difference between the error uncertainties of the initial deployment position and the adjusted deployment position, so as to optimize the coordinates of the sensor deployment positions by using a position optimization algorithm; the coordinates of the sensor deployment positions that make the optimal position deployment equation obtain the minimum value are acquired to obtain a target deployment position set, so as to implement spectrum sensor deployment optimization. The limitations of the existing methods can be overcome. The main idea is to utilize the mutual relationship and restrictive factors between sensor deployment and spectrum data completion, and this technology can be effectively combined with spectrum data completion that combines models and data.

[0040] The spectrum sensor deployment optimization method proposed by the present invention can generate a spectrum map with higher accuracy by optimizing the sensor deployment positions under the condition of a limited number of sensors. On the other hand, under the constraint of the spectrum map accuracy, the required minimum number of sensors and their position distributions can be obtained, thereby improving the spectrum sampling efficiency or saving economic costs. This method combines the error considerations in the actual deployment scenario and uses mathematical models and algorithm optimization techniques to improve the overall performance and efficiency of the spectrum sensor network. It is of great significance for spectrum management and the effective utilization of wireless communication resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0042] Figure 1 is a schematic flowchart of the spectrum sensor deployment optimization method provided by the present invention;

[0043] Figure 2 is a schematic structural diagram of the spectrum sensor deployment optimization device provided by the present invention;

[0044] Figure 3 is a schematic structural diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention with reference to the drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0046] It should be noted that in the description of the embodiments of the present invention, the terms "include", "comprise", or any other variant thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article, or device. Without further limitations, the element defined by the phrase "including one..." does not exclude the existence of additional identical elements in the process, method, article, or device including the element.

[0047] The following will be combined with Figures 1-3Describe the spectrum sensor deployment optimization method and device provided by the embodiments of the present invention.

[0048] Figure 1 It is a schematic flowchart of the spectrum sensor deployment optimization method provided by the present invention. As Figure 1 shown, it includes but is not limited to the following steps (STEP, abbreviated as S):

[0049] S102: Obtain the global spectrum data error value of the initial deployment position set.

[0050] The initial deployment position set refers to the initial layout scheme of the sensors in the spectrum monitoring area. The global spectrum data error value is a quantification value of the difference between the spectrum data collected by the sensors and the real spectrum environment under this initial layout.

[0051] Optionally, obtaining the global spectrum data error value of the initial deployment position set in the spectrum sensor deployment optimization method provided by the present invention includes:

[0052] Obtain the global spectrum data error value according to the global spectrum data estimated value and the global spectrum data collected value of the initial deployment position set.

[0053] Among them, the global spectrum data estimated value is the spectrum data generated by the propagation model constructed according to the prior information of the initial deployment position. For example, the mean squared error (MSE) or the mean absolute error (MAE) can be used to measure the global spectrum data error value.

[0054] S104: Generate the adjusted error uncertainty according to the global spectrum data error value.

[0055] Among them, the adjusted error uncertainty refers to the error uncertainty obtained by updating the uncertainty of the adjusted deployment position after adjusting the initial deployment position of the sensor.

[0056] For example, using Bayesian theory, combining prior knowledge and new observation data, dynamically update the error uncertainty, and quantify the uncertainty of the error by calculating the entropy value of the error information; or use Kalman filtering to recursively update the error uncertainty.

[0057] Optionally, generating the adjusted error uncertainty according to the global spectrum data error value in the spectrum sensor deployment optimization method provided by the present invention includes:

[0058] Obtain the initial error uncertainty according to the set of initial deployment positions; adjust the sensor deployment positions in the set of initial deployment positions to obtain a set of adjusted deployment positions; update the error uncertainty of the set of adjusted deployment positions based on the initial error uncertainty to obtain the updated error uncertainty.

[0059] In an alternative embodiment, the updating of the error uncertainty of the adjusted deployment position set based on the initial error uncertainty to obtain the updated error uncertainty includes:

[0060] Zero out the initial error uncertainty of the initial deployment coordinate points in the set of adjusted deployment positions;

[0061] Update the initial error uncertainty of the optimized deployment coordinate points in the set of adjusted deployment positions, and the specific updating method is:

[0062] u i =(1 - w)·u i

[0063] where ui is the uncertainty of the deployment coordinate point, w is the weight, and the acquisition method of this weight is where d(s i , s j ) represents the distance between the initial deployment coordinate point s i and each point s i within the d-neighborhood of s j .

[0064] S106: Construct an optimal position deployment equation with the goal of minimizing the difference in error uncertainty between the initial deployment position and the adjusted deployment position.

[0065] The optimal position deployment equation, which is used to describe the relationship between the sensor position and the spectrum data error, aims to minimize the difference in error uncertainty between the initial deployment position and the adjusted deployment position. Define the minimization of the difference in error uncertainty as the objective function, and set constraint conditions according to the physical limitations of the sensors (such as the deployment area, the number of sensors, etc.), for example:

[0066]

[0067] where s * is the next optimal position deployment coordinate point, J(·) is the total reduction in error uncertainty after optimizing the sensor deployment positions in the set of initial deployment positions, is the target deployment position set, and s is the deployment coordinate point.

[0068] Optionally, the sum of the reduced error uncertainties after optimizing the sensor deployment positions in the spectrum sensor deployment optimization method provided by the present invention is specifically:

[0069]

[0070] where Δu i (s) is the absolute reduction in the posterior uncertainty of the i-th point after adding point s.

[0071] S108: Optimize the coordinates of the sensor deployment positions using a position optimization algorithm, such as a sequential position optimization algorithm, gradient descent method, or greedy algorithm, etc., to optimize the coordinates of the sensor deployment positions.

[0072] By iteratively solving the optimal position deployment equation, find the set of sensor deployment position coordinates that minimizes the objective function, i.e., the target deployment position set. For example, set the number of iterations or error threshold, and stop the iteration when the conditions are met.

[0073] The present invention combines dynamic adjustment, error uncertainty update, and optimization algorithms, which can effectively reduce spectrum data errors, improve the accuracy and reliability of sensor deployment, and is applicable to sensor layout optimization tasks in complex spectrum environments.

[0074] Based on the content of the above embodiments, as an optional embodiment, the position optimization algorithm provided by the present invention is a sequential position optimization technique. The application of this algorithm will be further described below in combination with the content of a specific embodiment.

[0075] According to the prior information of the target area, establish a propagation model to estimate the global spectrum data.

[0076] Define M as the geographical environment information such as buildings and elevation in the target area, and P T , L T are the prior information of signal propagation such as transmitter power and position respectively. Using, for example, a propagation model or ray tracing method, the estimated global spectrum data can be calculated. The method φ based on the model can estimate the global spectrum data

[0077]

[0078] Define the proportion of pre-sampled sensors as ρ, and the number of pre-sampled sensors is equal to ρN. Through pre-sampling, sampled spectrum data can be obtained Pre-sampling is, for example, obtaining a small amount of spectrum data based on the measurements of the deployed sensors.

[0079] Use to represent the spectrum error at the sampling position, where e nDenote the deviation between the global spectrum data acquisition value at the nth sampling position and the corresponding estimated value in It reflects the error between the global spectrum data acquisition value obtained at the sampling position and the estimated value of the global spectrum data estimated based on the model. The calculation formula is as follows:

[0080]

[0081] Use E=(e 1 , e 2 ,..., e L ) to represent the global spectrum error. Using the spectrum data completion algorithm ψ, the spectrum error values at the sampling points are completed to all positions to obtain the global spectrum error E. The calculation formula is as follows:

[0082]

[0083] Considering that the obtained spectrum error value E above is a predicted value of the actual error and reflects an absolute error value. It is normalized and mapped to the range [0,1]. The normalization calculation formula is as follows:

[0084]

[0085] where E min and E max represent the minimum and maximum values in E respectively.

[0086] Phase 2: Uncertainty-aware location selection

[0087] Use U=(u 1 , u 2 ,…, u L ) to represent the error uncertainty between the spectrum map of the estimated global spectrum data estimate and the true spectrum map of the global spectrum data acquisition value. This value reflects the degree of error uncertainty. A larger uncertainty indicates a higher expected error value.

[0088] Through the pre-sampling, error completion, and normalization processes introduced above, the normalized error is obtained. Furthermore, the initial value of the error uncertainty is calculated using the normalized error obtained based on pre-sampling.

[0089] Considering that a certain number of sensors have been deployed in the pre-sampling, and the error uncertainty at the corresponding positions changes with the acquisition of the true spectrum data. Based on the above considerations, we give the following update method for the error uncertainty:

[0090] (1) For the deployed position s i , the uncertainty drops to 0, that is, ui = 0.

[0091] (2) For s i at every point s within the d-neighborhood of s j , the uncertainty is updated through u i = (1 - w)·u i , where the weight w is determined by the weight formula of inverse distance weighting (IDW).

[0092]

[0093] where d(s i , s j ) represents the distance between s i and s j .

[0094] To simplify the expression, the function η(·) is used to represent the update of the above error uncertainty. It should be noted that when gradually adjusting the initial deployment position of the sensor and adding sensors one by one, each additional sensor means obtaining new sampled spectral data, which will affect the attenuation uncertainty. Therefore, every time a sensor is added to the deployment, the error uncertainty needs to be updated. For the newly added sampling position point s, the update function of the error uncertainty can be expressed as:

[0095] U = η(U, s).

[0096] By updating all s in the pre-sampling position set , we obtain the initialized error uncertainty U 0 .

[0097] Sequential position optimization is used to optimize the sensor deployment. After obtaining the initialized error uncertainty U 0 , the sensor positions are selected and the uncertainty is updated in real time, aiming to minimize the error uncertainty.

[0098] The function J(·) is introduced to represent the total reduction in error uncertainty after optimizing the sensor deployment positions in the initial deployment position set. After adding sensors to the deployment, the error uncertainty will decrease. Therefore, this formula is used to calculate the reduction value of the error uncertainty. Its expression can be formally defined as follows:

[0099]

[0100] where represents the index set of all discrete coordinates whose distance from point s is less than d, and Δu i (s) is the absolute reduction in the posterior uncertainty of the i-th point after adding point s.

[0101] The objective function for sensor deployment optimization is transformed into the uncertainty-aware sensor deployment location optimization given by the following formula, which is formally expressed as:

[0102]

[0103] The objective function given by the above formula needs to find the optimal deployment location set from all grid position sets That is, the target deployment location set This problem is a combinatorial optimization problem and belongs to the NP-hard problem, and the optimal solution cannot be obtained. Therefore, in this embodiment, a greedy algorithm is used to solve the approximate solution of the optimal deployment location set in this problem. The main idea of the greedy algorithm is as follows:

[0104] First, add the pre-sampling position set to the sampling set , and initialize the error uncertainty U to U 0 . Update the error uncertainty after adding the pre-sampling position points through the formula.

[0105] Subsequently, find the next optimal position that can minimize the error uncertainty to the greatest extent, which can be expressed by the following formula:

[0106]

[0107] where \ and ∪ represent the relative complement and union operations on sets respectively.

[0108] Add the current optimal deployment position s * to . Continue to find the next optimal position until the set contains N sampling points.

[0109] The present invention can be applied to spectrum mapping to correct the estimated results by using the sampled real spectrum data based on the model-based estimation results, and realize online update.

[0110] The present invention can also be applied to spectrum mapping to improve the accuracy of spectrum map construction through uncertainty-aware sensor deployment optimization under the conditions of limited prior information and the number of sensors.

[0111] In application, the above method can either be effectively combined with the spectrum data completion method that combines models and data to obtain the best spectrum map, or be corrected by the sampled real spectrum data based on the results of specific model estimation to realize the online update of the spectrum map

[0112] The present invention also provides a spectrum sensor deployment optimization method device, and the device includes:

[0113] The planning model construction module is used to obtain the starting position and the ending position of the path to be planned, and construct an unmanned boat path planning model according to the sea area data and the unmanned boat structure data of the path to be planned by the unmanned boat;

[0114] The cost function construction module is used to construct a dynamic weighting function based on the unmanned boat path planning model to calculate the comprehensive cost value of each track;

[0115] The path planning module is used to obtain the comprehensive cost value of the track when the dynamic weighting function obtains the minimum value, so as to realize the path planning of the unmanned boat.

[0116] It should be noted that the spectrum sensor deployment optimization method device provided by the embodiment of the present invention can execute the spectrum sensor deployment optimization method described in any of the above embodiments during specific operation, and this embodiment will not be elaborated here.

[0117] Figure 3 It is a schematic structural diagram of an electronic device provided by the present invention. As Figure 3 shown, the electronic device may include: a processor 310, a communication interface 320, a memory 330, and a communication bus 340. Among them, the processor 310, the communication interface 320, and the memory 330 complete mutual communication through the communication bus 340. The processor 310 can call the logical instructions in the memory 330 to execute the spectrum sensor deployment optimization method, and the method includes:

[0118] S1: Obtain the global spectrum data error value of the initial deployment position set;

[0119] S2: Generate an adjusted error uncertainty according to the global spectrum data error value; wherein, the adjusted error uncertainty is the error uncertainty obtained by updating the uncertainty of the adjusted deployment position after adjusting the initial deployment position of the sensor;

[0120] S3: With the goal of minimizing the difference between the error uncertainties of the initial deployment position and the adjusted deployment position, construct an optimal position deployment equation to optimize the coordinates of the sensor deployment position by using a position optimization algorithm;

[0121] S4: Obtain the coordinates of the sensor deployment position that makes the optimal position deployment equation obtain the minimum value, and obtain the target deployment position set to realize spectrum sensor deployment optimization.

[0122] In addition, when the logical instructions in the above-mentioned memory 330 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0123] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the spectrum sensor deployment optimization method provided in the above-mentioned various embodiments. The method includes: obtaining a global spectrum data error value of an initial deployment position set; generating an adjusted error uncertainty according to the global spectrum data error value; wherein the adjusted error uncertainty is an error uncertainty obtained by updating the uncertainty of the adjusted deployment position after adjusting the initial deployment position of the sensor; taking the minimum difference between the error uncertainties of the initial deployment position and the adjusted deployment position as the target, constructing an optimal position deployment equation to optimize the coordinates of the sensor deployment position by using a position optimization algorithm; obtaining the coordinates of the sensor deployment position that makes the optimal position deployment equation obtain the minimum value to obtain a target deployment position set, and realizing the optimization of the spectrum sensor deployment.

[0124] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is configured to execute the spectrum sensor deployment optimization method provided in the above embodiments. The method includes: obtaining a global spectrum data error value of an initial deployment position set; generating an adjusted error uncertainty according to the global spectrum data error value; wherein the adjusted error uncertainty is an error uncertainty obtained by updating the uncertainty of the adjusted deployment position after adjusting the initial deployment position of the sensor; taking the minimum difference between the error uncertainties of the initial deployment position and the adjusted deployment position as the objective, constructing an optimal position deployment equation to optimize the coordinates of the sensor deployment position by using a position optimization algorithm; obtaining the coordinates of the sensor deployment position that makes the optimal position deployment equation obtain the minimum value, obtaining a target deployment position set, and realizing spectrum sensor deployment optimization.

[0125] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0126] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A spectrum sensor deployment optimization method, characterized in that: include: Obtaining a global spectrum data error value of an initial deployment position set; Generate an adjusted error uncertainty according to the global spectrum data error value; wherein the adjusted error uncertainty is an error uncertainty obtained by adjusting the initial deployment position of the sensor and updating the uncertainty of the adjusted deployment position; With the goal of minimizing the difference in error uncertainty between the initial deployment position and the adjusted deployment position, the optimal position deployment equation is constructed to optimize the coordinates of the sensor deployment position using the position optimization algorithm. The coordinates of the sensor deployment position that makes the optimal position deployment equation obtain the minimum value are obtained, and the target deployment position set is obtained to achieve spectrum sensor deployment optimization.

2. The spectrum sensor deployment optimization method according to claim 1, characterized in that: The obtaining of the global spectrum data error value of the initial deployment position set includes: A global spectrum data error value is obtained based on the global spectrum data estimation value and the global spectrum data acquisition value of the initial deployment position set; wherein the global spectrum data estimation value is spectrum data generated by a propagation model constructed based on prior information of the initial deployment position.

3. The spectrum sensor deployment optimization method according to claim 1, characterized in that: The step of generating the adjusted error uncertainty according to the global spectrum data error value comprises: The initial error uncertainty is obtained according to the initial deployment position set; Adjusting the sensor deployment positions in the initial deployment position set to obtain an adjusted deployment position set; The error uncertainty of the adjusted deployment position set is updated based on the initial error uncertainty to obtain the updated error uncertainty.

4. The spectrum sensor deployment optimization method according to claim 3, characterized in that: The step of updating the error uncertainty of the adjusted deployment position set based on the initial error uncertainty to obtain the updated error uncertainty includes: Returning the initial error uncertainty of the initial deployment coordinate point in the adjusted deployment position set to zero; The initial error uncertainty of the optimized deployment coordinate point in the adjusted deployment position set is updated, and the updating method is specifically as follows: u i =(1-w)·u i Among them, u i is the uncertainty of the deployment coordinate point, w is the weight, and the weight is obtained as follows: Among them, d(s i ,s j ) represents the initial deployment coordinate point s i and for s i Every point s in the d-neighborhood of j The distance between.

5. The spectrum sensor deployment optimization method according to claim 1, characterized in that: The optimal position deployment equation is specifically: Among them, s * is the next optimal position deployment coordinate point, J(·) is the sum of the error uncertainty reduction after optimizing the sensor deployment positions in the initial deployment position set, is the target deployment location set, and s is the deployment coordinate point.

6. The spectrum sensor deployment optimization method according to claim 5, characterized in that: The sum of the reduction in error uncertainty after optimizing the sensor deployment positions in the initial deployment position set is specifically: in, Represents the index set of all discrete coordinates whose distance from point s is less than d, Δu i (s) is the absolute reduction in the posterior uncertainty of the i-th point after point s is added.

7. The spectrum sensor deployment optimization method according to claim 1, characterized in that: A greedy algorithm is used to obtain the coordinates of the sensor deployment positions that minimize the optimal position deployment equation, and a target deployment position set is obtained.

8. The spectrum sensor deployment optimization method according to claim 2, characterized in that: According to the global spectrum data estimation value and the global spectrum data acquisition value of the initial deployment position set, the global spectrum data error value is obtained, including: Obtain a global spectrum data estimation value, perform error calculation based on the global spectrum data estimation value and the global spectrum data acquisition value, and obtain a spectrum error value of the initial deployment position; The spectrum error value is calculated by using a spectrum data completion algorithm to obtain a global spectrum data error completion value; The global spectrum data error complement value is normalized to obtain a global spectrum data error value.

9. A spectrum sensor deployment optimization device, characterized in that: include: A global spectrum data error value acquisition module, used to acquire a global spectrum data error value of an initial deployment position set; A first processing module is used to optimize the sensor deployment positions in the initial deployment position set by using a sequential position optimization algorithm to obtain an adjusted deployment position set, and obtain an initial error uncertainty according to the adjusted deployment position set; A second processing module is used to update the error uncertainty of the adjusted deployment position set based on the initial error uncertainty to obtain an updated error uncertainty, and construct an optimal position deployment equation according to the updated error uncertainty and the adjusted deployment position set; The optimization calculation module is used to iteratively solve the optimal position deployment equation to obtain a target deployment position set to achieve spectrum sensor deployment optimization.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the spectrum sensor deployment optimization method according to any one of claims 1 to 8 are implemented.

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