A method for obtaining a threshold distribution of signal-to-interference ratio of a communication device in an electromagnetic environment

Abstract

To solve the problems of the prior art, the application provides a method for obtaining a signal-to-interference ratio threshold distribution of a communication device in an electromagnetic environment, comprising the following steps: determining a communication success rate under each interference power through simulation experiments, and determining an interference device level value analysis interval in which the communication success rate decreases from 100% to 0% or close to 0%. The corresponding signal-to-interference ratio of the receiving end under different interference source device levels and different communication signal transmission powers in the interference device level value analysis interval is solved. The signal-to-interference ratio distribution caused by interference noise is taken as a stress distribution, the device signal-to-interference ratio threshold distribution is taken as a device strength distribution, and a communication reliability analysis model is constructed based on a stress-strength model. The data obtained by solving is brought into the communication reliability analysis model, and the signal-to-interference ratio threshold distribution of the communication device in the electromagnetic environment is solved. The application breaks through the limitation of the traditional fixed threshold model, and more accurately describes the inherent volatility of the anti-interference ability of the device through the probability distribution.

Description

Technical Field

[0001] This invention belongs to the field of communication technology, specifically relating to a method for obtaining the signal-to-interference ratio threshold distribution of communication devices under electromagnetic environment. Background Technology

[0002] With the rapid development of electronic information technology, frequency-using equipment such as communication devices, radars, and drones face increasingly diverse interference patterns in complex electromagnetic environments. This interference can directly lead to increased bit error rates, transmission delays, functional impairment, and even mission failure. Against this backdrop, quantifying the disturbance state of equipment and assessing reliability through the signal-to-interference-ratio (SIR) threshold has become a key research direction. Whether it's estimating the critical interference threshold for satellite communication, measuring the environment of electronic interference signals, or determining the interference power threshold for drone communication, the SIR threshold plays a central role in anti-interference research. Accurately determining the SIR threshold is not only fundamental to quantifying the disturbance state of equipment but also a crucial basis for assessing mission reliability (such as communication success rate and detection probability) and supporting engineering decisions (such as interference power control and equipment deployment schemes).

[0003] Existing studies typically treat the signal-to-interference ratio (SIR) threshold as a fixed constant. However, in real-world scenarios, the SIR threshold is affected by environmental randomness such as interference power fluctuations and atmospheric attenuation changes, as well as by equipment conditions such as component aging and temperature drift. It exhibits significant random fluctuation characteristics and cannot meet the reliability research requirements in dynamic electromagnetic environments. Summary of the Invention

[0004] This invention addresses the problems existing in the prior art by providing a method for obtaining the signal-to-interference ratio (SIR) threshold distribution of communication devices in an electromagnetic environment, comprising the following steps:

[0005] The communication success rate under various interference powers was determined through simulation experiments, and the analysis range of interference device level values ​​where the communication success rate dropped from 100% to 0% or close to 0% was determined.

[0006] The solution yields the receiver signal-to-interference ratio (SIR) for different interference source device levels and different communication signal transmission powers within the analysis interval of the interference device level value analysis.

[0007] Using the signal-to-interference ratio (SIR) distribution caused by interference noise as the stress distribution, and the equipment's own SIR threshold distribution as the equipment strength distribution, a communication reliability analysis model is constructed based on the stress-strength model as follows:

[0008] Assume the signal-to-interference ratio measured under interference conditions is X. L Then when the signal-to-interference ratio threshold X s When X is greater than the signal-to-interference ratio measured under interference conditions L Communication failure is determined at this time, and the cumulative failure probability is given by Equation 1:

[0009] F = P(X) s >X L ) Formula 1

[0010] Where F is the cumulative failure probability and P is the probability function;

[0011] By substituting the obtained interference device level values ​​within the analysis interval, and the corresponding receiver signal-to-interference ratio (SIR) under different interference source device levels and different communication signal transmission powers, into the communication reliability analysis model, the SIR threshold distribution of communication devices under electromagnetic conditions is obtained, specifically including:

[0012] At least two value points are selected within the self-interference device level analysis interval, and the cumulative failure probability F corresponding to these value points is obtained based on experimental data. n n is the natural number sequentially numbered for the selected level values;

[0013] Let the range of the signal-to-interference ratio threshold distribution of communication equipment be [μ]. S -3σ S μ S +3σ S The length of the [] is 6dB. Solving Equation 1 yields the signal-to-interference ratio threshold distribution parameters of the communication device. Where: μ S For the signal-to-interference ratio threshold X s The mean, σ S For the signal-to-interference ratio threshold X s The standard deviation is then used to obtain the distribution of the signal-to-interference ratio threshold of the communication equipment.

[0014] Furthermore, the method for determining the communication success rate under various interference powers through simulation experiments includes:

[0015] Under normal communication conditions of the target communication equipment, the interference source is directed at the receiving end of the communication equipment, and the interference intensity is continuously adjusted.

[0016] Record the total number of signals transmitted and the total number of signals successfully received under different interference intensities, and calculate the communication success rate under that interference intensity.

[0017] Furthermore, the method for obtaining the receiver signal-to-interference ratio (SIR) under different interference source device levels and different communication signal transmission powers within the analysis interval of interference device level values ​​includes:

[0018] Based on the performance of the jamming equipment, a relationship is established between the jamming equipment level and the jamming signal strength at the receiving end, expressed as Equation 2, to obtain the jamming signal strength J at the receiving end. V :

[0019] J V =20*log(V / K1)-K2 (Equation 2)

[0020] In Equation 2, V is the level value of the interfering device, and K1 and K2 are the performance parameters of the interfering device;

[0021] The signal strength Q at the receiving end is detected;

[0022] The signal-to-interference ratio at the receiver is calculated according to Equation 3.

[0023] SNR = QJ V Formula 3

[0024] SNR stands for Signal-to-Interference Ratio at the receiver.

[0025] Furthermore, based on the conversion formula between dBm and Vpp of the sinusoidal signal under 50 ohms impedance, the interference device level values ​​corresponding to different transmission powers J of the interference device are obtained.

[0026] Furthermore, Equation 1 is solved using the following method:

[0027] Signal interference ratio threshold X L The range of values ​​is Then the signal-to-interference ratio threshold X L The probability of belonging to this value range is:

[0028]

[0029] The probability of communication failure is:

[0030]

[0031] because With (X) S >x L Since these are two independent events, the range of values ​​is... The internal cumulative failure probability is the product of equations four and five, that is:

[0032]

[0033] At this point, the cumulative failure probability F of the entire distribution is:

[0034]

[0035] or,

[0036]

[0037] At this point, let the signal strength be X. L And the threshold X s If both conform to a normal distribution, then the function f1(x) L f2(x) and f2(x) S ) is represented as:

[0038]

[0039] In the formula, μ L For the sake of trust X L The mean, μ S For the signal-to-interference ratio threshold X s The mean, σ L For the sake of trust X L Standard deviation, σ S For the signal-to-interference ratio threshold X s The standard deviation.

[0040] Furthermore, let the random variable Δ = X S -X L Then the cumulative failure probability is the probability of the random variable Δ, since X S and X L Since both follow a normal distribution, the random variable Δ also follows a normal distribution, and the probability density function of the random variable Δ is:

[0041]

[0042] In the formula, μ δ =μ S -μ L , Substituting equation eleven into equation twelve yields:

[0043]

[0044] make At this point, when δ = 0; When δ=∞, u=∞, and dδ=σ δ du, substituting equation 12, yields equation 13:

[0045]

[0046] make Equation thirteen is expressed as:

[0047]

[0048] Since the probability density function curve of a standard normal random variable is symmetric about the ordinate axis, it has the following properties:

[0049]

[0050] Therefore, the cumulative failure probability is expressed as:

[0051]

[0052] At this point, by solving Equation 16 based on the known F, the integration limit Z can be obtained. Then, by solving Equation 17, the signal-to-interference ratio threshold distribution parameters of the communication equipment can be obtained.

[0053]

[0054] Furthermore, the solution method for Equation 17 is as follows:

[0055] Assume that the data at at least two points follow a normal distribution. Furthermore, the signal-to-interference ratio threshold of communication equipment follows a normal distribution. Equation 17 can be written as:

[0056]

[0057] Since the interference patterns and power are similar at each sampling point, the variance of the data distribution at each sampling point does not vary much. Assuming that the variance of the data distribution at each sampling point is equal, we can solve Equation 18 to obtain the signal-to-interference ratio threshold distribution parameters of the communication equipment.

[0058] Furthermore, based on the obtained signal-to-interference ratio threshold distribution parameters... And the signal-to-interference ratio distribution under another interference condition. The cumulative failure probability under the interference condition is obtained by solving Equations 16 and 17, and then the reliability of the communication equipment under the interference condition is obtained.

[0059] Preferably, the value points selected for the self-interference device level analysis interval are the two endpoints (mVpp) of the target analysis interval for the interference device level. min With mVpp max The value point at that location.

[0060] The advantages of this invention are:

[0061] 1. This invention models the signal-to-interference ratio (SIR) threshold of communication equipment as a probability distribution, providing a reliability modeling method for communication equipment considering dynamic threshold distribution under electromagnetic interference. This method overcomes the limitations of traditional fixed threshold models, namely, the inability to consider threshold calculation errors caused by additional noise from environmental factors or other factors. By using probability distribution, it more accurately characterizes the inherent fluctuations in the equipment's anti-interference capability.

[0062] 2. This invention is based on stress-intensity interference theory, takes into account the application methods under electromagnetic environment, and further derives the expression form of standard normal distribution by deriving the probability distribution equation, so that the reliability value can be directly obtained by looking up the table in engineering.

[0063] 3. This invention combines environmental measurement data with threshold distribution, directly deriving the parameters of the threshold distribution by solving a system of equations, and determining the length of the distribution interval based on practical experience to solve for specific distribution parameter values. This modeling method can better quantify the randomness of equipment performance through distribution parameters, providing a statistical basis for anti-interference design, and also pioneering a method for determining the signal-to-interference ratio threshold distribution. Attached Figure Description

[0064] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention and do not constitute an undue limitation thereof. Obviously, those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0065] Figure 1 This is a schematic diagram of the preliminary test of the communication equipment of the present invention;

[0066] Figure 2 This is a schematic diagram of the signal-to-interference ratio modeling and analysis based on the stress-intensity model of the present invention;

[0067] Figure 3 This is an exemplary stress intensity interference region diagram of the present invention;

[0068] Figure 4 The signal-to-interference ratio distribution and its threshold distribution are shown in the embodiments of the present invention. Detailed Implementation

[0069] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0070] This invention provides an exemplary method for obtaining the signal-to-interference ratio (SIR) threshold distribution of communication devices in an electromagnetic environment, comprising the following steps:

[0071] The communication success rate under various interference powers was determined through simulation experiments, and the range of interference device level values ​​where the communication success rate dropped from 100% to 0% or close to 0% was analyzed.

[0072] The solution yields the signal-to-interference ratio (SIR) at the receiver under different interference source device levels and different communication signal transmission powers within the analysis interval.

[0073] The signal-to-interference ratio (SIR) distribution caused by interference noise is used as the stress distribution, and the SIR threshold distribution of the equipment itself is used as the equipment strength distribution. A communication reliability analysis model is constructed based on the stress-strength model.

[0074] By substituting the obtained interference device level values ​​within the analysis interval for different interference source device levels and different communication signal transmission powers into the communication reliability analysis model, the threshold distribution of the communication device signal-to-interference ratio under electromagnetic environment is obtained.

[0075] This invention provides, by way of example, a method for determining the communication success rate under various interference powers through simulation experiments, comprising:

[0076] Under normal communication conditions of the target communication equipment, the interference source is directed at the receiving end of the communication equipment, and the interference intensity is continuously adjusted.

[0077] like Figure 1 As shown, during the communication interference threshold test, the test equipment includes an interference source, a communication equipment transmitter (including a transmitting antenna and a display and control platform), and a communication equipment receiver (including a receiving antenna, a display and control platform, and a data collection platform).

[0078] This step mainly involves pointing the transmitting antenna of the interference source at the receiving antenna of the receiving end of the communication device while the two communication devices are in normal communication status, and continuously adjusting the interference intensity until the communication status of the interfered device reaches a critical value, and then recording the corresponding interference intensity as a level value.

[0079] Record the total number of signals transmitted and the total number of signals successfully received under different interference intensities, and calculate the communication success rate under that interference intensity.

[0080] This step organizes the data corresponding to the level value of each interfering device, analyzes its packet loss rate and time delay changes, treats every six data points as one instruction, calculates the average packet loss rate of every six data points, and uses the communication success criterion: a packet loss rate of less than 10% is considered successful to classify the obtained data, and obtains the number of successes, the total number, and the percentage under each interference power, forming a data statistics table as shown in Table 1.

[0081] Table 1. Success Count, Total Number, and Percentage at Different Interference Powers

[0082]

[0083] When the interference device's voltage level is set within a certain range, such as [mVpp] min ,mVpp max When the success rate of the transmission command decreases from high to low and eventually reaches 0%, the level data in this range can be selected for further analysis.

[0084] This invention provides, by way of example, a method for determining the receiver signal-to-interference ratio (SIR) under different interference source device levels and different communication signal transmission powers within an analysis interval of interference device level values, comprising:

[0085] Based on the performance of the jamming equipment, a relationship is established between the jamming equipment level and the jamming signal strength at the receiving end, expressed as Equation 2, to obtain the jamming signal strength J at the receiving end. V :

[0086] J V =20*log(V) K1 Formula 2 (-K2)

[0087] In Equation 2, V is the level value of the interfering device, and K1 and K2 are the performance parameters of the interfering device.

[0088] For example: Given that the interference signal strength obtained by the receiver through spectrum analysis at 1200mVpp is -35dBm, and that the interference signal power at the receiver decreases proportionally with decreasing interference level, and is linearly related to log(V / 1200), therefore, taking 1200mVpp as the baseline, K1 = 1200, K2 = 35, Equation 2 can be transformed into...

[0089] J V =20*log(V / 1200)-35

[0090] In the formula, V is the level value of the interfering device, and J V To determine the interference signal strength at the receiver, when the interference device's voltage level is adjusted to 1200mVpp, we can obtain a corresponding interference signal strength of -35dBm at the receiver. Similarly, when the interference device's voltage level is adjusted to V, we can obtain a corresponding interference signal strength of J at the receiver. V .

[0091] Therefore, the interference power range at the receiving end can be determined based on the interference intensity level range determined in the previous step.

[0092] The signal strength Q at the receiving end is detected.

[0093] In this step, the signal strength Q at the receiving end corresponding to different transmit powers at the transmitting end of the communication equipment can be determined through environmental signal baseline testing.

[0094] The signal-to-interference ratio at the receiver is calculated according to Equation 3.

[0095] SNR = QJ V Formula 3

[0096] SNR stands for Signal-to-Interference Ratio at the receiver.

[0097] According to the definition of signal-to-interference ratio (SIR), which is equal to the ratio of the signal power of the communication receiving device to the interference signal strength at the communication receiving device, we can obtain Equation 3, and then calculate the SIR at the receiving end of the device.

[0098] This invention provides an exemplary method for obtaining the level value of an interfering device, which involves obtaining the level value of the interfering device corresponding to different transmission powers J of the interfering device based on the conversion formula between dBm and Vpp of a sinusoidal signal under a 50-ohm impedance.

[0099] Vpp represents peak-to-peak value, measured in volts (V). Peak-to-peak value refers to the difference between the highest and lowest values ​​of a signal within one period; it represents the range between the maximum and minimum. It describes the magnitude of the signal value's variation range. If the input is a sine wave signal, the peak value is √2 times the effective value, and the peak-to-peak value is twice the peak value. The formulas for converting between dBm and Vpp for a sine wave signal at a 50-ohm impedance are as follows:

[0100] dbm = 10 + 20 * log(0.5 * Vpp)

[0101] This formula can be used to calculate the power corresponding to the level values ​​of the useful signal and the interference signal.

[0102] This invention provides, by way of example, a method for constructing a communication reliability analysis model based on a stress-intensity model, comprising:

[0103] Assume the signal-to-interference ratio measured under interference conditions is X. L Then when the signal-to-interference ratio threshold X s When X is greater than the signal-to-interference ratio measured under interference conditions L Communication failure is determined at this time, and the cumulative failure probability is given by Equation 1:

[0104] F = P(X) s >X L ) Formula 1

[0105] Where F is the cumulative failure probability and P is the probability function.

[0106] In the stress-strength model for mechanical reliability, "stress" refers to the external load on the system (such as tension or compression), which is a random variable leading to system failure. "Strength" refers to the system's own resistance (such as material strength), which is a random variable resisting failure. The "failure condition" refers to the system failing when the stress exceeds the strength, from which the system's reliability can be derived. This is the consideration of the stress-strength model in mechanical reliability.

[0107] While applying stress-intensity interference theory to the study of signal-to-interference ratio (SIR) thresholds for frequency-using equipment such as communication devices in electromagnetic environments can assess the equipment's electromagnetic interference resistance and determine the threshold distribution, the following two problems exist:

[0108] 1. How to consider the interference between stress distribution and intensity distribution, and ensure the compatibility of their physical mechanisms.

[0109] 2. In traditional applications of stress-strength models, reliability can only be assessed based on known distributions. It is impossible to extract the distribution parameters of strength and stress from measured communication success and failure data, making it difficult for the model to adapt to dynamically changing electromagnetic environments and equipment conditions.

[0110] This has resulted in the absence of reports in existing technologies regarding the analysis of the signal-to-interference ratio threshold of communication equipment using stress-intensity interference theory.

[0111] This invention addresses the problems of existing technologies by creatively proposing, based on considerations of the reliability of communication equipment in corresponding electromagnetic environments, to treat the signal-to-interference ratio (SIR) distribution caused by interference noise as a stress distribution, and the equipment's own SIR threshold distribution as a device strength distribution. Figure 2 As shown, the probability of communication success is taken as the probability that the signal-to-interference ratio distribution is less than the signal-to-interference ratio threshold distribution, and the success or failure probability of communication is determined accordingly, which successfully adapts to the failure decision condition that the model requires S>R.

[0112] Furthermore, after extensive experimentation, this invention innovatively provides constraints to determine the length of the distribution interval by combining the statistical characteristics of the normal distribution with the signal power attenuation law in communication scenarios. This allows for the solution of specific parameters of the signal-to-interference ratio threshold distribution from measured data, solving the problem that existing technologies can only evaluate reliability based on known distributions and cannot inversely solve for the distribution parameters of strength and stress from measured communication success and failure data.

[0113] At this point, communication is successful when the signal-to-interference ratio (SIR) measured under interference conditions is greater than the device's SIR threshold. In other words, the probability that the SIR measured under interference conditions is greater than the SIR threshold is taken as the reliability of successful communication.

[0114] Assume the signal-to-interference ratio measured under interference conditions is X. L Then when the signal-to-interference ratio threshold X s The signal-to-interference ratio X measured under conditions greater than interference L When communication fails, the signal-to-interference ratio (SIR) X measured under interference conditions is... L Less than the signal-to-interference ratio threshold X s The probability of the situation is the cumulative failure probability of successful communication. The cumulative failure probability is expressed as Equation 1 of this invention. Solving Equation 1 will yield the cumulative failure probability of communication.

[0115] This invention provides an exemplary method for solving Equation 1, such as... Figure 3 As shown, it includes:

[0116] Signal interference ratio threshold X L The range of values ​​is Then the area A1 represents the signal-to-interference ratio threshold X. L The probability of falling within this range is:

[0117]

[0118] Signal-to-interference ratio threshold X s Greater than x L The probability of communication failure is represented by the area A2 in the diagram, and is:

[0119]

[0120] because With (X) S >x L These are two independent events, and both events will occur as long as the state of the communication equipment does not change significantly. According to the probability multiplication theorem, the probability of two independent events occurring simultaneously is equal to the product of the probabilities of each event occurring individually. Therefore, within the range of values... The internal cumulative failure probability is the product of equations four and five, that is:

[0121]

[0122] At this point, the cumulative failure probability F of the entire distribution is:

[0123]

[0124] According to the properties of integrals, equation 7 can also be written as equation 8.

[0125]

[0126] At this time, when the function f1(x) L f2(x) and f2(x) S When the information is known, the communication reliability can be obtained by solving equation 7 or equation 8.

[0127] This invention provides an exemplary method for solving the cumulative failure probability F, including: assuming a confidence-to-interference ratio X. L And the threshold X s If both conform to a normal distribution, then the function f1(x) L f2(x) and f2(x) S ) is represented as:

[0128]

[0129] In the formula, μ L For the sake of trust X L The mean, μ S For the signal-to-interference ratio threshold X s The mean, σ L For the sake of trust X L Standard deviation, σ S For the signal-to-interference ratio threshold X s The standard deviation.

[0130] Since the cumulative failure probability of successful communication refers to the random variable X S Greater than random variable X L Let the probability be Δ = X. S -X L Then the cumulative failure probability is the probability of the random variable Δ, since X S and X L Since both follow a normal distribution, the random variable Δ also follows a normal distribution, and the probability density function of the random variable Δ is:

[0131]

[0132] According to probability theory, in the formula: μ δ =μ S -μ L , Substituting equation eleven into equation twelve yields:

[0133]

[0134] make At this point, when δ = 0. When δ=∞, u=∞, and dδ=σ δ du, substituting equation 12, yields equation 13:

[0135]

[0136] make Equation thirteen is expressed as:

[0137]

[0138] Since the probability density function curve of a standard normal random variable is symmetric about the ordinate axis, it has the following properties:

[0139]

[0140] Therefore, the cumulative failure probability is expressed as:

[0141]

[0142] At this point, by solving Equation 16 based on the known F, the integration limit Z can be obtained. Then, by solving Equation 17, the signal-to-interference ratio threshold distribution parameters of the communication equipment can be obtained.

[0143]

[0144] This invention employs a method to determine the signal-to-interference ratio (SIR) threshold distribution parameters of a device under the assumption of a normal distribution, thereby clarifying the SIR threshold distribution of the target communication device under simulated communication interference conditions and solving the problem that the SIR threshold distribution is difficult to determine when considering dynamic thresholds.

[0145] Furthermore, the present invention employs a method that uses the signal-to-interference ratio (SIR) threshold distribution based on experimental results to obtain the SIR threshold distribution through data back-calculation. This method can fully consider the special characteristics of the conditions during the experiment, simulate experimental results in specific environments and locations, and perform data back-calculation. This can reduce errors caused by environmental influences in subsequent reliability analysis work and improve the accuracy of the analysis.

[0146] Furthermore, this invention cleverly utilizes the moment equivalence method to derive the interval related to the distribution parameters, and based on the distribution characteristics, cleverly determines the length of the interval related to the distribution parameters. Technically, it achieves the effect of inversely calculating the inherent dynamic threshold distribution of the device in the interference model based on the success rate of communication tasks under a normal distribution.

[0147] Furthermore, this invention derives the standard normal distribution expression by further deducing the probability distribution equation, allowing for direct table lookup of reliability values ​​in engineering applications. This also facilitates the rapid calculation of the signal-to-interference ratio threshold distribution parameters based on known experimental data. On the other hand, it can be based on the determined signal-to-interference ratio threshold distribution parameters. and the known signal-to-interference ratio distribution The cumulative failure probability under this interference condition can be quickly calculated, thereby obtaining the reliability of the communication equipment under this interference condition.

[0148] This invention provides an exemplary solution method for Equation 17: assuming that the data at at least two points follow a normal distribution. Furthermore, the signal-to-interference ratio threshold of communication equipment follows a normal distribution. Equation 17 can be written as:

[0149]

[0150] Since the interference patterns and power are similar at each sampling point, the variance of the data distribution at each sampling point does not vary much. Assuming that the variance of the data distribution at each sampling point is equal, we can solve Equation 18 to obtain the signal-to-interference ratio threshold distribution parameters of the communication equipment.

[0151] This invention provides an exemplary method for selecting the value points for the analysis interval of the self-interference device's level value, which are: the two endpoints (mVpp) of the target analysis interval of the interference device's level value. min With mVpp max The value point at that location.

[0152] This invention provides an exemplary method for obtaining the reliability of communication equipment under different interference conditions, comprising: based on the obtained signal-to-interference ratio threshold distribution parameters... And the signal-to-interference ratio distribution under another interference condition. The cumulative failure probability under the interference condition is obtained by solving Equations 16 and 17, and then the reliability of the communication equipment under the interference condition is obtained.

[0153] This invention treats the signal-to-interference ratio (SIR) threshold of communication equipment under electromagnetic interference as a random variable, following a certain distribution. The SIR threshold of the equipment itself can be considered as an intensity distribution, and the SIR under external force can be considered as a stress distribution. Therefore, a stress intensity model can be used to determine the SIR threshold distribution. Thus, this invention first conducts a data baseline test on the tested communication equipment, calculates the transmission power based on the interference transmission level, and then calculates the interference and signal power at the receiving end. Based on this, the distribution of the SIR data is considered as a normal distribution. Combining this with the intensity stress model, the SIR under interference is considered as stress, and the equipment's own SIR threshold is considered as intensity. By combining this with the equipment's task reliability distribution, a new task success / failure probability model is derived. Based on this, a method for calculating the equipment's SIR threshold based on measured success / failure data is provided.

[0154] This invention adopts a modeling approach that uses the device's own signal-to-interference ratio (SIR) threshold as the intensity distribution and measured SIR data as the stress distribution. By combining preliminary experimental data with the inverse solution of the equation set, it achieves accurate determination of the SIR threshold distribution under electromagnetic conditions, providing a quantitative basis for reliability assessment and anti-interference design of frequency-using equipment.

[0155] Therefore, on the one hand, this invention treats the signal-to-interference ratio (SIR) threshold of the communication device itself in an electromagnetic environment as an intensity distribution, and the SIR data distribution measured in the aforementioned level range as a stress distribution, thereby realizing the application of the stress-intensity model in the reliability assessment of frequency-using equipment, especially communication equipment, in an electromagnetic environment. When the SIR threshold distribution parameter and the SIR distribution parameter are known, the reliability of the communication task of this communication device can be directly obtained by looking up a table.

[0156] On the other hand, this invention directly derives the success or failure result by combining measured data, and then uses equation derivation to obtain the parameter interval distribution range corresponding to the test result. Then, based on empirical constraints, the length of the distribution interval is determined, and by combining the parameter interval calculated from the measured data, the specific value of the signal-to-interference-ratio (SIR) threshold distribution parameter is obtained, thus determining the SIR threshold distribution.

[0157] The technical solution of the present invention will be further described below with reference to specific embodiments.

[0158] The experiment set the communication transmission power to 6dBm, the distance between communication devices to 10m, the antenna gain of the communication transmitter to 6dBi, and the interference source to 5m from the communication receiver. The interference antenna was a directional antenna, designed to interfere only with the receiver, with an antenna gain of 10dB. The interference pattern used by the interference source was a single-tone continuous wave at a frequency of 1430MHz.

[0159] The data corresponding to the level values ​​of each interfering device were organized and analyzed to determine the packet loss rate and delay changes. The collected data were treated as one instruction for every six data points, and the average packet loss rate of every six data points was calculated. The data were then classified using the criterion of successful communication: a packet loss rate of less than 10% is considered successful. The number of successful communications, the total number of communications, and the percentage of communications under each interference power are shown in Table 2.

[0160] Table 2. Success Count, Total Number, and Percentage at Different Interference Powers

[0161]

[0162] As shown in the table above, the success rate of transmitting commands is 100% when the interference device's voltage level is set to less than 199 mVpp, and close to 0% when the interference device's voltage level is set to greater than 500 mVpp. Therefore, the threshold for judging successful communication is between 199 mVpp and 500 mVpp for the interference device's voltage level. We further analyzed the data collected when the interference device's voltage level was set to 250 mVpp and 354 mVpp.

[0163] (1) Calculate the transmission power based on the level value

[0164] When the interference source device's voltage level is adjusted to 250mVpp, the corresponding transmit power J is...

[0165] J=10+20*log(0.5*250*10^-3)=-8.06dBm

[0166] When the interference source device's voltage level is adjusted to 354mVpp, the corresponding transmit power J is...

[0167] J=10+20*log(0.5*354*10^-3)=-5.04dBm

[0168] (2) Calculate the interference signal power at the receiver.

[0169] Given that the interference signal strength obtained by the receiver through a spectrum analyzer at 1200mVpp is -35dBm, then:

[0170] When the interference source device's voltage level is adjusted to 250mVpp, the corresponding receiver power is:

[0171] J250 =20*log(250 / 1200)-35=-48.62dBm

[0172] When the interference source device's voltage level is adjusted to 354mVpp, the corresponding receiver power is:

[0173] J 354 =20*log(354 / 1200)-35=-45.60dBm

[0174] The interference signal strengths at 354mVpp and 250mVpp are -45.60dBm and -48.62dBm, respectively.

[0175] (3) Calculate the communication signal power at the receiving end.

[0176] It is known that when the communication device transmits at a power of 6 dBm, the signal strength P obtained by the receiver through a spectrum analyzer is -37.89 dBm; when the communication device transmits at a power of 16 dBm, the signal strength P obtained by the receiver through a spectrum analyzer is -31.84 dBm; and when the communication device transmits at a power of 26 dBm, the signal strength P obtained by the receiver through a spectrum analyzer is -21.08 dBm.

[0177] (4) Calculate the signal-to-interference ratio at the receiver.

[0178] When the interference source device's voltage level is adjusted to 250mVpp and the communication signal transmit power is 6dBm, the corresponding signal-to-interference ratio at the receiver is:

[0179] SNR 250 =PJ 250 =10.73dB

[0180] When the interference source device's voltage level is adjusted to 354mVpp and the communication signal transmit power is 6dBm, the corresponding signal-to-interference ratio at the receiver is:

[0181] SNR 354 =PJ 354 =7.71dB

[0182] When the interference source device's voltage level is adjusted to 250mVpp and the communication signal transmit power is 16dBm, the corresponding signal-to-interference ratio at the receiver is:

[0183] SNR 250 =PJ 250 =16.78dB

[0184] When the interference source device's voltage level is adjusted to 354mVpp and the communication signal transmit power is 16dBm, the corresponding signal-to-interference ratio at the receiver is:

[0185] SNR354 =PJ 354 =13.76dB

[0186] When the interference source device's voltage level is adjusted to 250mVpp and the communication signal transmit power is 26dBm, the corresponding signal-to-interference ratio at the receiver is:

[0187] SNR 250 =PJ 250 =27.54dB

[0188] When the interference source device's voltage level is adjusted to 354mVpp and the communication signal transmit power is 26dBm, the corresponding signal-to-interference ratio at the receiver is:

[0189] SNR 354 =PJ 354 =24.52dB

[0190] (5) Distribution Construction and Computation

[0191] We know that communication will succeed when the signal-to-interference ratio (SIR) of the receiving device is greater than its SIR threshold. It is known that the SIRs of the receiving communication device measured at 250mVpp and 354mVpp are 10.73dB and 7.71dB, respectively. Assume that the SIR measured at 250mVpp follows... The normal distribution f2(x) L2 ), its mean μ L2 That is, 10.73, and the receiver signal-to-interference ratio (SIR) measured under 354 mVpp conditions follows... The normal distribution f1(x) L1 ), its mean μ L1 That is, 7.71.

[0192] The stress intensity model is analyzed, and the signal-to-interference ratio (SIR) threshold characteristic of the device itself is regarded as the intensity distribution. The SIR data distribution measured at 250mVpp and 354mVpp is the stress distribution. When the SIR measured under interference conditions is greater than its SIR threshold, communication is successful. That is, the probability that the SIR measured under interference conditions is greater than the SIR threshold is regarded as the reliability of its communication success.

[0193] The communication device's new-to-interference ratio threshold distribution can then be derived using the algorithm provided in the method derivation. Table 2 shows that the success rates at 354 mVpp and 250 mVpp are 0.46% and 0.79%, respectively, meaning the failure rates are 0.54% and 0.21%. Looking up the table, we obtain Z1 = 0.1004 and Z2 = -0.8064. It is known that the data measured at 250 mVpp follows a certain pattern. It follows a normal distribution with a mean μ.L2 The value is 10.73, and the data measured at 354 mVpp follows the formula. It follows a normal distribution with a mean μ. L1 The value is 7.71, assuming the threshold follows a certain normal distribution. The system of equations can then be obtained as follows.

[0194]

[0195] Since their interference patterns and powers are similar, and the variances of the two distributions do not change significantly, assuming that the variances of the two distributions are equal, we can obtain... Substitute it into the model.

[0196] Solving the equation yields

[0197]

[0198] Furthermore, based on practical experience, the interval [μ] of the signal-to-interference ratio threshold distribution can be obtained. S -3σ S μ S +3σ S If the length is 6dB, then σ can be obtained. S =1,σ L1 =σ L2 =3.1767, which allows us to draw a schematic diagram of the probability density function of the signal-to-interference ratio (SIR) distribution under interference conditions and the SIR threshold distribution, as shown below. Figure 4 As shown.

[0199] The distribution of the signal-to-interference ratio threshold can now be determined. The value is (8.0443, 1). Similarly, the distribution of the signal-to-interference ratio under a certain interference condition can be determined. The value is (9.6, 16.8). From Equations 16 and 17, we can solve for Z = -0.3687, which means the cumulative communication failure probability F = 0.35618. Therefore, the probability of successful communication is 0.64382.

[0200] Based on this, under these conditions, the signal-to-interference ratio threshold distribution parameters of communication equipment under the influence of electromagnetic interference can be obtained using this method. By looking up the table, the cumulative failure probability of communication success under different conditions can be obtained, thus completing the modeling and verification of equipment reliability.

[0201] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method of obtaining a signal-to-interference ratio threshold distribution for a communication device in an electromagnetic environment, characterized by, Includes the following steps: The communication success rate under various interference powers was determined through simulation experiments, and the analysis range of interference device level values ​​where the communication success rate dropped from 100% to 0% or close to 0% was determined. The solution yields the receiver signal-to-interference ratio (SIR) for different interference source device levels and different communication signal transmit powers within the analysis interval, specifically including: According to the performance of the interference device, a relationship between the interference device level value and the received end interference signal strength is established, and the received end interference signal strength is obtained by the following formula two : Formula Two In formula two K1 and K2 are performance parameters of the interference device; The signal strength Q at the receiving end was detected. The signal-to-interference ratio at the receiver is calculated according to Equation 3. Formula Three Formula Three wherein, S is the received end signal to interference ratio; Using the signal-to-interference ratio (SIR) distribution caused by interference noise as the stress distribution, and the equipment's own SIR threshold distribution as the equipment strength distribution, a communication reliability analysis model is constructed based on the stress-strength model as follows: Assuming the signal-to-interference ratio measured under interference conditions is Then when the signal-to-interference ratio threshold When the signal-to-interference ratio is greater than that measured under interference conditions Communication failure is determined at this time, and the cumulative failure probability is given by Equation 1: Formula One Formula One wherein is the cumulative failure probability, function; By substituting the obtained interference device level values ​​within the analysis interval, and the corresponding receiver signal-to-interference ratio (SIR) under different interference source device levels and different communication signal transmission powers, into the communication reliability analysis model, the SIR threshold distribution of communication devices under electromagnetic conditions is obtained, specifically including: The self-interference equipment level value analysis interval selects at least two value points, and obtains the corresponding cumulative failure probability F of the value points based on experiments n , n is the natural number sequential number of the taken level value; Let the range of the signal-to-interference ratio threshold distribution of communication equipment be [ -3 , +3 The length of ] is 6dB. Solving Equation 1 yields the signal-to-interference ratio threshold distribution parameters of the communication equipment. ， ),in: For the signal-to-interference ratio threshold The mean, For the signal-to-interference ratio threshold The standard deviation is then used to obtain the distribution of the signal-to-interference ratio threshold of the communication equipment.

2. The method of claim 1, wherein the threshold distribution of the signal-to-interference ratio of the communication device in the electromagnetic environment is obtained by, The method for determining the communication success rate under various interference powers through simulation experiments includes: Under normal communication conditions of the target communication equipment, the interference source is directed at the receiving end of the communication equipment, and the interference intensity is continuously adjusted. Record the total number of signals transmitted and the total number of signals successfully received under different interference intensities, and calculate the communication success rate under that interference intensity.

3. The method of claim 1, wherein the threshold distribution is obtained by: Based on the formula for converting between dBm and Vpp of a sinusoidal signal under a 50-ohm impedance, the interference device level values ​​corresponding to different transmission powers J of the interference device are obtained.

4. The method of claim 1, wherein the threshold distribution of the signal-to-interference ratio of the communication device in the electromagnetic environment is obtained by: Equation 1 is solved using the following method: The signal-to-interference ratio threshold value The value interval of the signal-to-interference ratio threshold value The probability that the signal-to-interference ratio threshold value belongs to the value interval is: Formula 4 The probability of communication failure is: Formula Five Formula Five because( )and( Since these are two independent events, the range of values ​​is... The internal cumulative failure probability is the product of equations four and five, that is: Formula Six At this point, the cumulative failure probability F of the entire distribution is: Formula 7 or, Formula 8 At this time, let the signal-to-interference ratio and the signal-to-interference ratio threshold both conform to normal distribution, the function and is expressed as: Formula Nine Formula Nine Formula Ten In the formula, For the sake of faith The mean, For the signal-to-interference ratio threshold The mean, For the sake of faith standard deviation For the signal-to-interference ratio threshold The standard deviation.

5. The method for obtaining the signal-to-interference ratio threshold distribution of communication equipment under electromagnetic environment according to claim 4, characterized in that, make Then the cumulative failure probability is a random variable. The probability, due to and Both follow a normal distribution, therefore random variables It also conforms to a normal distribution; random variable The probability density function is: Formula Eleven wherein = - , , and substituting equation eleven into equation twelve gives equation thirteen: Formula Twelve make At this time =0; ,when = hour, ,and Substituting equation 12 into equation 13, we get equation 13: Formula Thirteen Let Z Then Equation XIII is expressed as: Formula Fourteen Since the probability density function curve of a standard normal random variable is symmetric about the ordinate axis, it has the following properties: = Formula Fifteen Therefore, the cumulative failure probability is expressed as: Formula Sixteen At this time, the integral limit Z can be obtained by solving equation 16 according to the known F, and the communication device signal-to-interference ratio threshold distribution parameter can be obtained by solving equation 17 ， ): Formula Seventeen.

6. The method of claim 5, wherein the threshold distribution of the signal-to-interference ratio of the communication device in the electromagnetic environment is obtained by, The solution method for Equation 17 is as follows: Suppose that the data at at least two points follow a normal distribution. ， ), and the signal-to-interference ratio threshold of communication equipment follows a normal distribution ( ， Then equation seventeen can be written as: Formula 18 Because the interference patterns and power of each value point are similar, the variance of the value point data distribution does not change much. Assuming that the variances of the data distributions of each value point are equal, the communication device signal-to-interference ratio threshold distribution parameter (S) can be obtained by solving equation eighteen. ， ).

7. The method of claim 5, wherein the threshold distribution of the signal-to-interference ratio of the communication device in the electromagnetic environment is obtained by, According to the obtained signal-to-interference ratio threshold distribution parameters (S ， ), and the signal-to-interference ratio distribution under another interference condition (S ， ), the cumulative failure probability under the interference condition is obtained by formula sixteen and formula seventeen, and then the reliability of the communication device under the interference condition is obtained.

8. The method of claim 1, wherein the threshold distribution of the signal-to-interference ratio of the communication device in the electromagnetic environment is obtained by, The value point of the self-interference equipment level value analysis interval is two end values mVpp of the interference equipment level value target analysis interval min and the value point at mVpp max .

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