Ultra-short wave link establishment parameter optimization method and system under complex terrain condition

Abstract

The invention discloses an ultra-short wave link establishment parameter optimization method and system under a complex terrain condition. The optimization method comprises an evaluation system construction step, an index membership function determination step, a communication link communication performance calculation step, a fuzzy clustering analysis step and a parameter optimization analysis step. According to the communication equipment parameter optimization method under the complex terrain condition, under the complex terrain environment, the influence of the environment on wireless communication signals is fully considered, the basic capacity and attributes of the equipment are combined, parameter optimization of the equipment is completed, effective link establishment of communication receiving and transmitting equipment is achieved, and the method is suitable for various types of communication systems.

Description

Technical Field

[0001] The invention belongs to the technical field of wireless communication, and in particular to a method and system for optimizing parameters of ultra-short wave link establishment under complex terrain conditions. Background Art

[0002] At present, the research on ultra-short wave equipment link communication is mainly focused on point-to-point communication and networking communication. Among them, point-to-point communication can adopt multi-level communication link redundancy backup, combined with link switching to ensure communication connection, or based on real-time link quality change monitoring, update and iterate communication parameters to achieve communication; in terms of networking communication, the focus is on using multiple antennas and multiple terminals to achieve high-speed data transmission, or frequency hopping multiple access to ensure the quality of communication and networking performance. The above research is mainly aimed at studying how to maintain the connection efficiently and reliably after the link is established under ideal channel transmission conditions, and does not involve the process of selecting equipment parameters when the communication link is established. In actual application, the increase in radio wave propagation loss caused by complex terrain and the differences in equipment passability, mobility and endurance caused by different equipment forms under complex terrain conditions will affect the efficiency of the communication link establishment process. Therefore, the setting of ultra-short wave link establishment parameters under complex terrain conditions needs to take into account multiple factors for optimization.

[0003] To establish a UHF communication link, the communication parameters of the transceiver equipment must be set first. The commonly used strategy now is to complete the connection through automatic connection with pre-set parameters or manual input of auxiliary link establishment. Under complex terrain conditions, directly using the preset parameters under ideal propagation conditions will cause link failure or poor signal quality. If the manual method is used, it is impossible to quickly and accurately weigh the influence of multiple factors such as the environment and equipment form to complete effective settings. Therefore, it is necessary to design a method for optimizing the parameters of UHF link establishment under complex terrain conditions. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for optimizing the parameters of ultra-short wave link establishment under complex terrain conditions, which can take into account multiple factors such as the characteristics of radio wave propagation, the shape of the equipment, and the endurance of the equipment, and efficiently complete the optimization matching of the parameters of ultra-short wave link establishment under complex terrain conditions.

[0005] The technical solution to achieve the purpose of the present invention is: a method for optimizing the parameters of ultra-short wave link establishment under complex terrain conditions, comprising the following steps:

[0006] 10) Evaluation system construction: Combine the communication environment of the equipment and the capability parameters of the communication equipment itself to determine the evaluation index system that needs to be considered for the communication between the sender and the receiver;

[0007] 20) Communication link model matching: Match the radio wave propagation model of the link according to the location of the communication sender and receiver and the terrain environment;

[0008] 30) Calculation of evaluation system index parameters: Based on the radio wave propagation model of the communication link and the parameters of the equipment, calculate the various index parameters in the evaluation system;

[0009] 40) Determination and calculation of membership function: For each indicator in the evaluation system, based on its impact on the communication process, determine the respective membership function and calculate the membership value of each indicator;

[0010] 50) Fuzzy clustering parameter optimization: Based on the needs, various parameter configurations are classified and optimized through clustering to obtain the communication configuration parameters that best match the needs.

[0011] A system for optimizing parameters for establishing ultra-short wave links under complex terrain conditions, used to implement the above method, the system comprising:

[0012] Evaluation system building module: Combine the environment of device communication and the capability parameters of the communication device itself to determine the evaluation index system that needs to be considered for the communication between the sender and the receiver;

[0013] Communication link model matching module: matches the radio wave propagation model of the link according to the location of the communication sender and receiver and the terrain environment;

[0014] Evaluation system indicator parameter calculation module: based on the communication link radio wave propagation model and equipment parameters, calculate the various indicator parameters in the evaluation system;

[0015] Membership function determination and calculation module: for each indicator in the evaluation system, based on its impact on the communication process, determine the respective membership function and calculate the membership value of each indicator;

[0016] Fuzzy clustering parameter optimization module: Based on the needs, various parameter configurations are classified and optimized through clustering to obtain the communication configuration parameters that best match the needs.

[0017] Compared with the prior art, the present invention has the following significant advantages:

[0018] (1) Optimizing matching parameters: In complex terrain environments, based on several configurable parameters of the device, the connectivity of the device link is taken as the premise, and the factors such as the device shape, device accessibility, and device endurance are weighed to comprehensively balance the matching link establishment parameters;

[0019] (2) Good versatility: The present invention combines specific terrain conditions to analyze their impact on radio wave propagation, and is suitable for optimal setting of communication parameters of ultra-short wave equipment under various complex terrain conditions;

[0020] (3) Strong practicality: Based on the analysis of the impact of complex terrain on radio wave propagation loss, the present invention takes into account practical factors such as the specific form of the equipment and the endurance of the equipment, and is in line with the application scenarios of actual communication equipment.

[0021] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of a method for optimizing ultrashort wave link building parameters under complex terrain conditions according to the present invention.

[0023] Figure 2 This is a schematic diagram of a single blade obstacle.

[0024] Figure 3 This is a schematic diagram of a multi-edged obstacle.

[0025] Figure 4 It is the double-peak diffraction model Bullington algorithm.

[0026] Figure 5 This is the cluster analysis diagram of scenario 1.

[0027] Figure 6 This is the cluster analysis diagram of scenario 2. DETAILED DESCRIPTION

[0028] like Figure 1 As shown, the present invention proposes a method for optimizing parameters of ultra-short wave link establishment under complex terrain conditions, comprising the following steps:

[0029] 10) Evaluation system construction: Combine the communication environment of the equipment and the capability parameters of the communication equipment itself to determine the evaluation index system that needs to be considered for the communication between the sender and the receiver;

[0030] The evaluation system construction (10) step comprises:

[0031] 11) Determination of primary indicators: Determine the primary indicator parameters that need to be considered based on the demand for establishing ultra-short wave communication links in complex terrain environments;

[0032] The first-level indicator determination (11) step comprises:

[0033] 111) Determination of the “receiving connectivity” index: Communication link connectivity under complex terrain conditions, the sender and receiver are called fixed stations and mobile stations respectively, the communication connection process is analyzed from the perspective of fixed stations, the communication link from mobile stations to fixed stations is considered, and the “receiving connectivity” index is set;

[0034] 112) Determination of the “sending connectivity” index: Communication link connectivity under complex terrain conditions, the sending and receiving sides are called fixed stations and mobile stations respectively, the communication connectivity process is analyzed from the perspective of fixed stations, the communication link from fixed stations to mobile stations is considered, and the “sending connectivity” index is set;

[0035] 113) Determination of the "equipment form" index: Under complex terrain conditions, the form and parameters of the equipment will affect the equipment's passability, antenna installation height, working efficiency and endurance, so the "equipment form" index is set;

[0036] 12) Determination of secondary indicators: Based on the primary indicators, clarify and refine the corresponding secondary indicator parameters;

[0037] The step of determining the secondary index (12) comprises:

[0038] 121) The second-level indicator of the first-level indicator “receiving accessibility” is determined: the second-level indicator corresponding to this first-level indicator is “fixed station receiving signal power level”;

[0039] 122) The second-level indicator of the first-level indicator "transmission availability" is determined: the second-level indicator corresponding to the first-level indicator is "power level of mobile station received signal";

[0040] 123) Determination of the secondary indicators of the first-level indicator "equipment form": the corresponding secondary indicators of this first-level indicator include "passability", "antenna height", "working efficiency" and "equipment endurance";

[0041] Under complex terrain conditions, differences in equipment morphology affect the probability of whether the equipment can move and reach its destination, so the "passability" indicator is set;

[0042] The higher the antenna is erected, the more difficult it is to erect and install, so the "antenna height" indicator is set;

[0043] Under the premise that the quality of the received signal at the receiving end is the same, the gain of the transmitting end antenna and the transmitted power jointly affect the working efficiency of the transmitting end device, so the "working efficiency" indicator is set;

[0044] Under complex terrain conditions, the power supply endurance of different types of equipment is different, so the "equipment endurance" indicator is set.

[0045] 20) Communication link model matching: Match the radio wave propagation model of the link according to the location of the communication sender and receiver and the terrain environment;

[0046] The communication link model matching (20) step comprises:

[0047] 21) Determination of basic transmission loss on quasi-smooth terrain: Determination of basic transmission loss on smooth terrain in urban areas, suburbs, rural roads, open areas and forest areas;

[0048] The basic transmission loss determination (21) step on the quasi-smooth terrain comprises:

[0049] Basic transmission loss L in urban areas, suburbs, rural roads, open areas and forest areas b The formulas are:

[0050]

[0051] Among them, L b is the basic transmission loss, measured in dB; f is the radio frequency, measured in megahertz (MHz); h is the frequency of the radio wave, measured in megahertz (MHz); b represents the antenna height of the fixed station, in meters (m); h m represents the antenna height of the mobile terminal, in meters (m); a represents the density of buildings; d is the distance from the mobile terminal to the fixed station, in kilometers (km). The exponent r is calculated as follows:

[0052]

[0053] 22) Determination of basic transmission loss for hilly and mountainous paths: Determine the basic transmission loss for hilly and mountainous paths;

[0054] The step of determining the basic transmission loss of the hilly and mountainous path (22) comprises:

[0055] L′ b (Hills, Mountains) = AA + L b (quasi-smooth terrain) + 20lg (d / d′) (7)

[0056] Among them, AA represents the diffraction loss caused by an obstacle (mountain) to radio wave propagation, and the unit is decibel (dB); Figure 2 and Figure 3 Where d is the distance from the mobile terminal to the fixed station, in kilometers (km); d′ is the distance between the mobile terminal and the nearest blade-shaped obstacle (mountain), in kilometers (km); L b is the basic transmission loss, and the specific calculation is shown in step (21).

[0057] The calculation formula of AA is:

[0058]

[0059] Where v represents the Fresnel clearance, and its relationship with the height difference h between the mountain peak and the transceiver is as shown in the formula:

[0060]

[0061] like Figure 2As shown, d1 and d2 are the distance from the fixed end to the blade-shaped obstacle and the distance from the mobile end to the blade-shaped obstacle, respectively, in kilometers (km), and λ is the wavelength of the electromagnetic wave, in millimeters (mm).

[0062] In the case of double-peak diffraction, that is, when there are two blade-shaped obstacles, the Bullington algorithm is used to convert the heights of the two peaks between the fixed end and the mobile station into a single peak, such as Figure 4 As shown. Starting from the fixed end, they are called the first peak and the second peak, respectively, where d_T is the distance from the fixed end to the first peak, d_R is the distance between the mobile end and the second peak, in kilometers (km), h1 and h2 are the heights of the first peak and the second peak, respectively, h is the height of the peak after equivalent use of the Bullington algorithm, in meters (m), d1 is the distance between the equivalent peak and the fixed end, and d2 is the distance between the mobile end and the equivalent peak, in kilometers (km).

[0063] From the figure, we can get d1 = (d_T × h) / h1, d2 = (d_R × h) / h2, and d_TR = d1 + d2, so we can get h (d_T / h1 + d_R / h2) = d_TR, then,

[0064] h=d_TR(h1×h2) / (d_T×h2+d_R×h1) (10)

[0065] The Fresnel clearance v is then used to solve the loss of a single blade peak to obtain the correction factor AA in the model.

[0066] The Bullington algorithm is still used for models with three peaks (three blade-shaped obstacles) and above. The three peaks are equivalent to double peaks in turn. The specific idea is the same as the loss calculation of the above double peaks. Finally, the equivalent Fresnel clearance v is obtained, and the correction factor AA in the model is obtained using the loss of a single blade peak.

[0067] 30) Calculation of evaluation system index parameters: Based on the radio wave propagation model of the communication link and the parameters of the equipment, calculate the various index parameters in the evaluation system;

[0068] The evaluation system index parameter calculation (30) step comprises:

[0069] 31) Calculation of “fixed station received signal power level”: Calculate the fixed station received signal power level under corresponding conditions using the basic parameters of the fixed station and the mobile station, including the mobile station transmit power, the antenna gain of each station, and the link transmission loss;

[0070] The step of calculating the “fixed station received signal power level” comprises:

[0071] The formula for calculating the power level of the signal received by the fixed station is shown in (11), in dB:

[0072] P RM =P TS +G S +G M -L f (11)

[0073] Among them, P RM Represents the fixed station received signal power level, P TS is the signal power sent by the mobile terminal, G S Represents the antenna gain of the mobile device, G M is the antenna gain of the fixed station equipment, L f Represents the loss of radio wave propagation.

[0074] 32) Calculation of “Mobile station received signal power level”: Calculate the mobile station received signal power level under corresponding conditions using the basic parameters of the fixed station and the mobile station, including the fixed station transmit power, the antenna gain of each station, and the link transmission loss;

[0075] The calculation step of the "mobile terminal received signal power level" includes

[0076] The power level of the signal received by the mobile station is calculated using the formula (12), in dB:

[0077] P RS =P TM +G M +G S -L f (12)

[0078] Among them, P RS Represents the mobile station receiving signal power level, P TM is the signal power sent by the fixed station, G S Represents the antenna gain of the mobile station equipment, G M is the antenna gain of the fixed station equipment, L f Represents the loss of radio wave propagation.

[0079] 40) Determination and calculation of membership function: For each indicator in the evaluation system, based on its impact on the communication process, determine the respective membership function and calculate the membership value of each indicator;

[0080] The membership function determination and calculation steps include:

[0081] 41) Determination of membership function: Determine the membership function of the parameter based on the impact of each indicator on the evaluation system;

[0082] The membership function determination step comprises:

[0083] 411) Determination of the membership function of “fixed station received signal power level”: Based on the impact of “fixed station received signal power level” on the evaluation system, the membership function of the indicator parameter is determined;

[0084] The membership function of this indicator can refer to the large-scale indicator, that is,

[0085]

[0086] Among them, [b,+] is the optimal interval, a is the intolerable lower limit, that is, the minimum receiving power of the fixed station when the link is connected, and b represents the maximum receiving power of the fixed station that can be tolerated. This value needs to balance the capability of the sending device on the other end and the demand for link communication performance, but its value must be greater than a and can be determined based on specific scenarios and expert opinions.

[0087] 412) Determination of membership function of “mobile station received signal power level”: Determine the membership function of the indicator parameter based on the influence of “mobile station received signal power level” on the evaluation system;

[0088] Among them, [b,+] is the optimal interval, a is the intolerable lower limit, that is, the minimum receiving power of the mobile station when the link is connected, and b represents the maximum receiving power of the mobile station that can be tolerated. This value needs to balance the capability of the sending device on the other end and the demand for link communication performance, but its value must be greater than a and can be determined based on specific scenarios and expert opinions.

[0089] 413) Determination of “Passability” membership function: Determine the membership function based on the impact of the equipment’s “passability” on the evaluation system.

[0090] The “passability” index affects the mobility and passability of equipment under complex terrain conditions, and the degree of membership can be determined based on expert opinions.

[0091] 414) Determination of membership function of “antenna height”: Based on the impact of “antenna height” on the evaluation system, its membership function is determined.

[0092] The membership function of this indicator refers to the inverse membership function, that is,

[0093]

[0094] Where x is the normalized antenna height.

[0095] 415) Determination of membership function of “work efficiency”: Based on the impact of “work efficiency” on the evaluation system, its membership function is determined.

[0096] The membership function of “working efficiency” is the same as the function of “antenna height”, as shown in formula (14). In this case, x represents the normalized working efficiency of the device.

[0097] 416) Determination of the membership function of “power supply mode”: Based on the impact of “power supply mode” on the evaluation system, its membership function is determined.

[0098] The “equipment power supply mode” indicator affects the equipment’s ability to continue working under complex terrain conditions, its sending and receiving performance, etc. The degree of membership can be determined based on expert opinions.

[0099] 42) Membership function calculation: Calculate the corresponding membership function result based on the specific parameters or actual conditions of each indicator.

[0100] 50) Fuzzy clustering parameter optimization: Based on the demand, various parameter configurations are classified and optimized through clustering to obtain the communication configuration parameters that best match the demand.

[0101] The fuzzy clustering parameter optimization step comprises:

[0102] 51) Establishment of the original matrix of the analysis object: Suppose there are n samples to be classified, these samples constitute a set X = {x1, x2, ... x n}, each sample has m characteristic indicators, namely x k ={x k1 ,x k2 ,…x km}, k = 1, ..., n, then we get the original data matrix A consisting of the sample set and its characteristic index, A = {x kl} n×m .

[0103] 52) Establishment of standard data matrix: After determining the membership function of each indicator, the value of the membership function is used to replace the indicator itself, that is, the values ​​of all samples corresponding to each indicator are compressed to the range of [0, 1] according to their appropriateness, and the standard data matrix B is obtained, B = {x′ kl} n×m .

[0104] 53) Fuzzy similarity matrix establishment: The standard data matrix sample object x″ i and x″ j The similarity relationship between ij ∈[0,1] to represent, and establish similarity relationship clustering matrix R = {r ij} (n+1)×m , can be determined by the minimum and maximum method, then

[0105] 54) Establishment of fuzzy equivalent matrix: Use the transitive closure method to transform the matrix R into t(R), that is, calculate When t(R) satisfies the transitivity, the fuzzy similarity matrix becomes a fuzzy equivalence matrix.

[0106] 55) Fuzzy cluster analysis: Select different thresholds α and obtain the matrix t(R) α The classification relationship is obtained. When α=1, each object is a class in its own right. As the α value decreases, the objects gradually merge from fine to coarse, and finally a dynamic clustering pedigree diagram is obtained, thereby determining the samples that are classified into the same category as the demand indicators and obtaining the optimal results of the equipment parameters.

[0107] The present invention is further described below with examples.

[0108] Example

[0109] In order to intuitively illustrate the beneficial effects of the present invention, the following simulation experiment was carried out on the method of the present invention. The system simulation adopts Matlab software, and the parameter setting does not affect the generality.

[0110] The simulation parameters are set as follows: the fixed station equipment is vehicle-mounted, the transmission power is P = 30w, an omnidirectional antenna is used, the antenna gain is G = 0dBi, the antenna height is 1,8m, and considering the vehicle height, the antenna height is about H = 4m. The parameters of the mobile terminal are shown in Table 1 below, and Table 2 is a corresponding table of various types of mobile terminal equipment (parameters) and evaluation system indicators.

[0111] Table 1 List of indicators of various types of mobile terminals (parameters)

[0112]

[0113]

[0114] Table 2 Correspondence between various types of equipment (parameters) and evaluation system indicators

[0115]

[0116] Scenario 1: To ensure that the fixed station receives signal energy above the threshold of -114dBm, the power of both the receiving and transmitting signals must be maximized, and the equipment antenna must be mounted at a high height.

[0117] 1) Based on the above five equipment performance index parameters, the standard data matrix B is established as shown below;

[0118]

[0119] 2) Based on the above five equipment performance index parameters, combined with the requirement of "maximum signal receiving and sending capabilities, and strong equipment endurance", the standard data matrix B' is established as shown below;

[0120]

[0121] 3) According to the above standard data matrix expression, the fuzzy similarity relationship is established as follows;

[0122]

[0123] 4) Transform the fuzzy similarity relationship matrix into a fuzzy equivalence relationship matrix

[0124] At this time, the fuzzy similarity relationship matrix does not have transitivity, so let t(R) = R 2 , using the square self-synthesis method to obtain the fuzzy equivalent relationship matrix

[0125]

[0126] R 4 =R 8 , at this time t(R)=R 4 is the fuzzy equivalence matrix.

[0127] 5) Cluster analysis

[0128] like Figure 5 As shown, it can be seen intuitively that device (parameter) 3 and the added demand sample, that is, device (parameter) 6, are in the same category and are the best matching choices. At the same time, devices (parameters) 4 and 5 are suboptimal choices.

[0129] Scenario 2: To ensure that the fixed station receives signal energy above the threshold of -114dBm, the power of the received and transmitted signals is required to be lower than that in Scenario 1. At the same time, the height of the mobile station antenna is lower than that in Scenario 1.

[0130] 1) Based on the above five equipment performance index parameters, the standard data matrix B is established as shown below;

[0131]

[0132] 2) The performance index parameters of the above five equipments require that "the power of receiving and transmitting signals is lower than that in scenario 1, and the height of the mobile station antenna is lower than that in scenario 1". The standard data matrix B′ is established in combination with the requirements as shown below;

[0133]

[0134] 3) According to the above standard data matrix expression, the fuzzy similarity relationship is established as follows;

[0135]

[0136] (3) Transform the fuzzy similarity relationship matrix into a fuzzy equivalence relationship matrix

[0137] At this time, the fuzzy similarity relationship matrix does not have transitivity, so let t(R) = R 2 , using the square self-synthesis method to obtain the fuzzy equivalent relationship matrix

[0138]

[0139] R 4 =R 8 , at this time t(R)=R 4 is the fuzzy equivalence matrix.

[0140] 5) Cluster analysis

[0141] like Figure 6 As shown, it can be seen intuitively that device (parameter) 2 and the added demand sample, that is, device (parameter) 6, are in the same category and are the best matching choice. At the same time, device (parameter) 1 is the suboptimal choice.

[0142] The present invention has been described above in a manner of illustration using embodiments. Those skilled in the art should understand that the present invention is not limited to the embodiments described above, and various changes, modifications and substitutions may be made without departing from the scope of the present invention.

Claims

1. A method for optimizing the parameters of ultra-short wave link establishment under complex terrain conditions, characterized in that: The steps include: 10) Evaluation system construction: Combine the communication environment of the equipment and the capability parameters of the communication equipment itself to determine the evaluation index system that needs to be considered for the communication between the sender and the receiver; 20) Communication link model matching: Match the radio wave propagation model of the link according to the location of the communication sender and receiver and the terrain environment; 30) Calculation of evaluation system index parameters: Based on the radio wave propagation model of the communication link and the parameters of the equipment, calculate the various index parameters in the evaluation system; 40) Determination and calculation of membership function: For each indicator in the evaluation system, based on its impact on the communication process, determine the respective membership function and calculate the membership value of each indicator; 50) Fuzzy clustering parameter optimization: Based on the needs, various parameter configurations are classified and optimized through clustering to obtain the communication configuration parameters that best match the needs.

2. The method for optimizing parameters for establishing ultra-short wave links under complex terrain conditions according to claim 1 is characterized in that: The evaluation system construction steps include: 11) Determination of primary index: According to the demand for establishing ultra-short wave communication links in complex terrain environments, the primary index parameters to be considered are determined. The primary index determination steps include: 111) Determination of the "receiving connectivity" index: Communication link connectivity under complex terrain conditions, the sender and receiver are called fixed stations and mobile stations respectively, the communication connection process is analyzed from the perspective of fixed stations, the communication link from mobile stations to fixed stations is considered, and the "receiving connectivity" index is set; 112) Determination of the "sending connectivity" index: Communication link connectivity under complex terrain conditions, the sending and receiving sides are called fixed stations and mobile stations respectively, the communication connectivity process is analyzed from the perspective of fixed stations, the communication link from fixed stations to mobile stations is considered, and the "sending connectivity" index is set; 113) Determination of the "equipment form" indicator: Under complex terrain conditions, the form and parameters of the equipment will affect the equipment's passability, antenna installation height, working efficiency and endurance, so the "equipment form" indicator is set; 12) Determination of secondary indicators: Based on the primary indicators, the corresponding secondary indicator parameters are clearly specified and refined. The secondary indicator determination steps include: 121) The second-level indicator of the first-level indicator "receiving accessibility" is determined: the second-level indicator corresponding to this first-level indicator is "fixed station receiving signal power level"; 122) The second-level indicator of the first-level indicator "transmission availability" is determined: the second-level indicator corresponding to the first-level indicator is "power level of mobile station received signal"; 123) The secondary indicators of the first-level indicator "equipment form" are determined: the secondary indicators corresponding to this first-level indicator include "passability", "antenna height", "working efficiency" and "equipment endurance".

3. The method for optimizing parameters for establishing ultra-short wave links under complex terrain conditions according to claim 1 is characterized in that: The communication link model matching step comprises: 21) Determination of basic transmission loss on quasi-smooth terrain: Determination of basic transmission loss on smooth terrain in urban areas, suburbs, rural roads, open areas and forest areas; 22) Determination of basic transmission loss for hilly and mountainous paths: Determine the basic transmission loss for hilly and mountainous paths; 23) Radio wave propagation loss model matching: According to the specific scenario, select and determine the corresponding transmission loss model.

4. The method for optimizing parameters for establishing ultra-short wave links under complex terrain conditions according to claim 3 is characterized in that: The basic transmission loss determination calculation on the quasi-smooth terrain includes: Basic transmission loss L in urban areas, suburbs, rural roads, open areas and forest areas b The formulas are, Among them, L b is the basic transmission loss, f is the radio frequency, h is b represents the antenna height at the fixed end, h m represents the antenna height of the mobile terminal, a represents the density of buildings, d is the distance from the mobile terminal to the fixed terminal, and the exponent r is calculated as follows:

5. The method for optimizing parameters for establishing ultra-short wave links under complex terrain conditions according to claim 3 is characterized in that: The basic transmission loss determination calculation for the hilly and mountainous paths includes: L′ b (Hills, Mountains) = AA + L b (quasi-smooth terrain) + 20lg (d / d′) (7) Among them, AA represents the diffraction loss caused by an obstacle to radio wave propagation, d is the distance from the mobile terminal to the fixed station, d′ is the distance between the mobile terminal and the nearest blade-shaped obstacle, and L b is the basic transmission loss; The calculation formula of AA is: Where v represents the Fresnel clearance, and its relationship with the height difference h between the mountain peak and the transceiver is shown in the formula: d1 and d2 are the distances from the fixed end to the blade-shaped obstacle and the distance from the mobile end to the blade-shaped obstacle, respectively, and λ is the wavelength of the electromagnetic wave; In the case of double-peak diffraction, that is, when there are two blade-shaped obstacles, the Bullington algorithm is used to equate the heights of the two peaks between the fixed end and the mobile station to a single peak; the specific method is that starting from the fixed end, the two peaks are respectively called the first peak and the second peak, where d_T is the distance from the fixed end to the first peak, d_R is the distance between the mobile end and the second peak, h1 and h2 are the heights of the first peak and the second peak respectively, then h is the height of the peak after being equivalent by the Bullington algorithm, d1 is the distance between the equivalent peak and the fixed end, and d2 is the distance between the mobile end and the equivalent peak; We get d1 = (d_T × h) / h1, d2 = (d_R × h) / h2, and d_TR = d1 + d2, and we get h (d_T / h1 + d_R / h2) = d_TR, then, h=d_TR(h1×h2) / (d_T×h2+d_R×h1) (10) Then, the Fresnel clearance v is used to solve the loss of a single blade peak, and the correction factor AA in the model is obtained; The Bullington algorithm is still used for models with three peaks or more. The three peaks are equivalent to double peaks in turn, and the equivalent Fresnel clearance v is finally obtained. The correction factor AA in the model is obtained using the loss of a single edge peak.

6. The method for optimizing parameters for establishing ultra-short wave links under complex terrain conditions according to claim 1 is characterized in that: The evaluation system indicator parameter calculation specifically includes: 31) "Fixed station received signal power level" calculation: using the basic parameters of the fixed station and the mobile station, including the mobile station transmit power, the antenna gain of the two stations, and the link transmission loss, calculate the fixed station received signal power level under the corresponding conditions; 32) Calculation of "Mobile Station Received Signal Power Level": Calculate the mobile station received signal power level under the corresponding conditions using the basic parameters of the fixed station and the mobile station, including the fixed station transmit power, the antenna gain of each station, and the link transmission loss.

7. The method for optimizing the parameters of ultra-short wave link establishment under complex terrain conditions according to claim 6 is characterized in that: The calculation formula for the fixed station received signal power level is shown in (11): P RM =P TS +G S +G M -L f (11) Among them, P RM Represents the fixed station received signal power level, P TS is the signal power level sent by the mobile station, G S Represents the antenna gain of the mobile station equipment, G M is the antenna gain of the fixed station equipment, L f Represents the loss of radio wave propagation; The mobile station received signal power level calculation formula (12): P RS =P TM +G M +G S -L f (12) Among them, P RS Represents the mobile station receiving signal power level, P TM is the signal power level sent by the fixed station, G S Represents the antenna gain of the mobile station equipment, G M is the antenna gain of the fixed station equipment, L f Represents the loss of radio wave propagation.

8. The method for optimizing parameters for establishing ultra-short wave links under complex terrain conditions according to claim 1 is characterized in that: The membership function determination and calculation includes: 41) Determination of membership function: Determine the membership function of the indicator parameter based on the impact of each indicator on the evaluation system, including: 411) Determination of the membership function of "fixed station received signal power level": Based on the impact of "fixed station received signal power level" on the evaluation system, determine the membership function of this indicator parameter; The membership function of this indicator is: Among them, [b,+] is the optimal interval, a is the intolerable lower limit, that is, the minimum receiving power of the fixed station when the link is connected, and b represents the maximum receiving power of the fixed station that can be tolerated. This value needs to balance the capability of the peer sending device and the demand for link communication performance, but its value must be greater than a; 412) Determination of the membership function of "Mobile station received signal power level": Based on the impact of "Mobile station received signal power level" on the evaluation system, the membership function of this indicator parameter is determined. Its membership function is the same as the membership function of "Mobile station received signal power level", where [b, +] is the optimal interval, a is the intolerable lower limit, that is, the minimum received power of the mobile station when the link is connected, and b represents the maximum tolerable received power of the mobile station. This value needs to balance the capability of the peer sending device and the demand for link communication performance, but its value must be greater than a; 413) Determination of "passability" membership function: Determine its membership function based on the impact of the equipment's "passability" on the evaluation system; 414) Determination of membership function of "antenna height": Determine its membership function based on the impact of "antenna height" on the evaluation system; The membership function of this indicator refers to the inverse membership function, that is, Where x is the normalized antenna height; 415) Determination of membership function of "work efficiency": Based on the impact of "work efficiency" on the evaluation system, its membership function is determined; the membership function of "work efficiency" is the same as that of "antenna height"; 416) Determination of the membership function of "power supply mode": Based on the impact of "power supply mode" on the evaluation system, its membership function is determined; the membership determination of "equipment power supply mode" includes: The "Equipment Power Supply Mode" indicator affects the equipment's ability to continue working in complex terrain conditions, and its transmission and reception performance. The degree of membership is determined based on expert opinions; 42) Membership function calculation: Calculate the corresponding membership function result based on the specific parameters or actual conditions of each indicator.

9. The method for optimizing parameters for establishing ultra-short wave links under complex terrain conditions according to claim 1 is characterized in that: The fuzzy clustering parameters preferably include: 51) Establishment of the original matrix of the analysis object: Suppose there are n samples to be classified, these samples constitute a set X = {x1, x2, ... x n }, each sample has m characteristic indicators, namely x k ={x k1 ,x k2 ,…x km }, k = 1, ..., n, then we get the original data matrix A consisting of the sample set and its characteristic index, A = {x kl } n×m ; 52) Establishment of standard data matrix: After determining the membership function of each indicator, the value of the membership function is used to replace the indicator itself, that is, the values ​​of all samples corresponding to each indicator are compressed to the range of [0, 1] according to their appropriateness, and the standard data matrix B is obtained, B = {x′ kl } n×m ; 53) Establishment of demand-oriented standard data matrix: According to m characteristic indicators, the demand is parameterized and recorded as T = {t1′ l } 1×m , add it to the above standard data matrix B, and get the demand-oriented standard data matrix B′, B′={x i ' j ′} (n+1)×m ={x′ kl ; t1′ j } (n+1)×m ; 53) Fuzzy similarity matrix establishment: The standard data matrix sample object x i ″ and x′ j The similarity relationship between ′ is expressed by the number r ij ∈[0,1] to represent, and establish similarity relationship clustering matrix R = {r ij } (n+1)×m , use the minimum and maximum method to determine, then 54) Establishment of fuzzy equivalent matrix: Use the transitive closure method to transform the matrix R into t(R), that is, calculate When t(R) satisfies the transitivity, the fuzzy similarity matrix becomes a fuzzy equivalence matrix. 55) Fuzzy cluster analysis: Select different thresholds α and obtain the matrix t(R) α The classification relationship is obtained. When α=1, each object is a class in its own right. As the α value decreases, the objects gradually merge from fine to coarse, and finally a dynamic clustering pedigree diagram is obtained, thereby determining the samples that are classified into the same category as the demand indicators and obtaining the optimal results of the equipment parameters.

10. A system for optimizing the parameters of ultra-short wave link establishment under complex terrain conditions, characterized in that: For implementing any of the methods described in claims 1 to 9, the system comprises: Evaluation system building module: Combine the environment of device communication and the capability parameters of the communication device itself to determine the evaluation index system that needs to be considered for the communication between the sender and the receiver; Communication link model matching module: matches the radio wave propagation model of the link according to the location of the communication sender and receiver and the terrain environment; Evaluation system indicator parameter calculation module: based on the communication link radio wave propagation model and equipment parameters, calculate the various indicator parameters in the evaluation system; Membership function determination and calculation module: for each indicator in the evaluation system, based on its impact on the communication process, determine the respective membership function and calculate the membership value of each indicator; Fuzzy clustering parameter optimization module: Based on the needs, various parameter configurations are classified and optimized through clustering to obtain the communication configuration parameters that best match the needs.

Images