Bandwidth estimation method for touch teleoperation service

Abstract

The invention is suitable for the technical field of wireless communication system communication, and provides a touch teleoperation service-oriented bandwidth estimation method, which comprises the following steps of: modeling a touch service arrival process; performing correlation analysis on the arrival process of the two parts; a yoke expression method of the tactile sudden arrival process; predicting a service arrival process; and estimating the bandwidth of the service flow. The tactile service arrival process is modeled, the correlation of the tactile service arrival process from master control to slave control and from slave control to master control is analyzed, the time domain, frequency domain and high-order characteristics of service arrival are considered, a new method for constructing an arrival yoke process is provided, service arrival is predicted, accurate measurement of tactile service burstiness is achieved, and the accuracy of the tactile service burstiness is improved. A new bandwidth estimation method is provided, and dynamic estimation of the haptic service bandwidth demand is realized. According to the method and the system, the abruptness of the tactile service can be efficiently processed, and a foundation is laid for guaranteeing the service quality of tactile communication, optimizing bandwidth resource configuration and improving network performance.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communication systems, and in particular relates to a bandwidth estimation method for tactile teleoperation services. Background Art

[0002] Tactile Internet is one of the potential key technologies of 6G mobile Internet. The IEEE P1918.1 standard working group defines Tactile Internet as a network architecture that allows remote access, perception, and operation of real (or virtual) objects (or processes).

[0003] The delay of a data packet is determined by its arrival and service process. How to model the arrival process of tactile teleoperation services and estimate bandwidth is the premise for ensuring the delay service quality of tactile communication. However, there is a lack of research on the modeling and prediction of tactile teleoperation service arrival, and there are also deficiencies in the research on bandwidth estimation. Unlike classic mobile communication services, tactile teleoperation is a "man-in-the-loop" system. Its service arrival frequency and characteristics are closely related to the operator's real-time operation. Due to the strict delay requirements, it is impossible to rely on conventional cache mechanisms and other strategies to compensate for the delay. Therefore, the tactile communication system needs to pay special attention to the dynamic estimation and adjustment of bandwidth to ensure the real-time and efficient data transmission during high-frequency operations. Specifically, when the operator at the master end performs high-frequency pushing, grabbing, releasing and other actions, the system will generate a large amount of tactile information in a short time. In order to reduce the burden on the communication network, tactile compression coding technology (such as the perceptual dead zone coding method) is essential. However, although compression coding technology can control the average arrival rate of tactile data packets, it aggravates the burstiness of the instantaneous arrival rate and arrival time interval. This bursty characteristic makes traditional bandwidth estimation methods challenging, especially during peak hours or high-frequency operations, when the burstiness and volatility of bandwidth demand may cause tremendous pressure on the network.

[0004] In summary, tactile information interaction has a natural burst characteristic, and compression coding technologies such as perceptual dead zone coding further enhance the burst attribute of tactile services. Since the arrival of tactile service traffic is random, accurately estimating bandwidth becomes a complex task. Therefore, how to accurately estimate the bandwidth of tactile services with burst characteristics is a difficult problem to achieve efficient quality assurance of tactile communication service. To this end, the present invention proposes a bandwidth estimation method for tactile teleoperation services. Summary of the invention

[0005] The purpose of the present invention is to provide a bandwidth estimation method for tactile teleoperation services, aiming to solve the problems raised in the above background technology.

[0006] The purpose of the present invention is achieved through the following technical solutions: A bandwidth estimation method for tactile teleoperation service comprises the following steps: Step 1: Modeling the tactile business arrival process; Analyze the autocorrelation and partial autocorrelation coefficients of the data packet arrival rate from the master to the slave and from the slave to the master. According to the characteristics of the autocorrelation and partial autocorrelation coefficients, select the model for modeling: if it is independent and identically distributed, use the maximum likelihood estimation to fit the candidate probability distribution; if it is the first-order autocorrelation, use the Markov chain model; if it is order autocorrelation and ,use The model is built using an order autoregressive process. The probability distribution of the random variables involved in the autoregressive process adopts a candidate distribution, and the parameters of the candidate distribution are fitted using maximum likelihood estimation. Step 2: Correlation analysis of the two-part arrival process; Calculate the time difference correlation coefficient of the service arrival process from the master control to the slave control and from the slave control to the master control to measure the correlation between the two; Step 3: Martingale expression method of tactile burst arrival process; A martingale representation is constructed for the arrival process of tactile services to reflect their burstiness and time correlation, including: Independent and identically distributed Pareto distribution: construct exponential upper martingale, considering the impact of tail index on reaching the martingale; Order autoregressive process: construct exponential martingale and consider the influence of order on reaching martingale; Step 4: Predict the business arrival process; The properties of the martingale are used to predict the cumulative arrival packet traffic in future time slots, including: Independent and identically distributed Pareto distribution: express predictions via martingale; Order autoregressive process: express predictions through martingale; Step 5: Estimate the bandwidth of business traffic; Define the service rate as bandwidth and build a constant rate service model. Calculate the delay violation probability for the autoregressive arrival model AR(p). Use the dichotomy method to estimate the bandwidth.

[0007] Furthermore, in step 1, the calculation formulas for the autocorrelation coefficient and the partial autocorrelation coefficient are as follows: Assume that the time series data is , Indicates t Observed value of business traffic at the moment; Autocorrelation coefficient To measure the t Data at the moment With hysteresis Data at the moment The correlation between them is calculated as: Formula 1: ; in, It is a time series The mean of is the lag period; n is the sample size of the time series, that is, the total number of observations; the partial autocorrelation coefficient To measure the t Data at the moment With hysteresis Data at the moment The direct relationship between The correlation between the two after the influence of the lag period is calculated as: Formula 2: ; in: Indicates that before it is known Under the condition of lagged data, t Time data With hysteresis Time data The conditional covariance between Indicates t Time data Before it is known The conditional variance under the condition of lag period.

[0008] Furthermore, in step 2, the calculation formula of the time difference correlation coefficient is: Formula 3: ; in: Indicates that the service arrival process from the master to the slave and from the slave to the master is The time difference correlation coefficient of , express Observed values ​​of business traffic from master to slave and from slave to master at all times; express Observed value of business traffic from master control to slave control at all times; Indicates the sample average of the observed value of the service flow from the master to the slave; It represents the sample average of the traffic observation values ​​from the control to the master control; t Indicates time slot; represents the sample size; Indicates the lag period.

[0009] Furthermore, the specific process of step three is as follows: make Represents the arrival process and describes the traffic volume of data packets arriving per unit time; represents the cumulative arrival process, Equal to the initial time to time slot The cumulative number of packets arriving; if The probability distribution function of the Pareto distribution that obeys independent and identical distribution is: Formula 4: ; in: Indicates an event Probability of occurrence; x represents the value of a random variable; Represents a random variable The lower bound of represents the tail index; make Represents the arrival martingale process, constructing the arrival martingale of the tactile business as an exponential upper martingale, considering the tail exponent The impact on the arrival of the martingale, according to the properties of the Pareto distribution, the arrival of the martingale expression corresponding to the Pareto distribution is constructed as follows: Formula 5: ; in: represents the martingale parameter, Represents the Martingale parameter proportional factor; according to the properties of Martingale , solve the martingale expression parameters and ,in Indicates the expectation symbol; express The arrival martingale process of time; Indicates from time slot 0 to time slot Arrival process; proof and hour, It is a martingale process; if belong The order autoregressive process has the form: Formula 6: ; in: Indicates before The autoregressive coefficient at each moment; i represents the index of the lagged term; Represents an autoregressive process Before The value of a moment; Represents the constant expected value, which determines the overall offset of the sequence; represents standard deviation; is an independent and identically distributed Gaussian distribution with a mean of 0 and a variance of 1; Using exponential supermartingale construction The arrival martingale of an autoregressive process of order The influence of, that is, the time domain characteristics of the arrival process, constructs the arrival martingale expression as: Formula 7: ; in: j Indicates a point in time; Represents an autoregressive process Before The value of the moment; represents the martingale expression parameter, which is the service rate; Using the properties of martingale , solve the martingale expression parameters .

[0010] Furthermore, the specific process of step 4 is as follows: According to forecasting theory, the random variable yes It can be measured if yes Measurable: Formula 8: ; From the definition of conditional mathematical expectation, we know that yes It can be measured if yes It can be measured according to the properties of the martingale Derived , and finally solve , Equal to the initial time to time slot The cumulative number of packets arriving; if Obeying the Pareto distribution of independent and identical distribution, according to the properties of the martingale, we know that the random variable yes It can be measured that, through formula 5, we can know that the martingale Measurable, that is Measurable; Therefore, Convert to ,Right now: Formula 9: ; The final solution is: Formula 10: ; if belong The AR process can be measured as can be measured; from formula 7, we can know that the Martingale Measurable, that is Measurable; Therefore, Convert to ,Right now: Formula 11: ; The final solution is: Formula 12: ; in: Represents an autoregressive process Before The value of a moment.

[0011] Furthermore, the specific process of step five is as follows: Define the service rate as the bandwidth required by the traffic, and represent the service as a constant rate service model; if belong The order autoregressive process is in the form of Equation 6; for the service rate satisfy The autoregressive arrival model AR(p) is: Formula 13: ; Formula 14: ; in: represents the martingale parameter, which is A special value defined in the range of ; represents variance; represents the delay violation probability coefficient; represents the autoregressive coefficient; express The standard deviation of Then the delay violation probability is: Formula 15: ; in: represents probability; Indicates time slot Delay represents the delay bound; consider The special case is: Formula 16: ; Then the variance is: Formula 17: ; in: express The variance of It represents the variance operator; express Gaussian process of time; therefore: Formula 18: ; Formula 19: ; in: Indicates the mean value symbol; The relevant characteristic function representing the arrival process can reflect the correlation between random variables; Represents the relevant characteristic functions of the service process; Then the delay violation probability is derived as: Formula 20: ; Let the target delay violation probability , using the bisection method to estimate the bandwidth C , the specific process is as follows: Initialization parameters: define constants in formulas and set delay limits k The range is , generating multiple k value; Define the binary search interval: For each k value, set the initial bandwidth search range to ; Binary iteration, including: Calculate the bandwidth midpoint : Formula 21: ; Calculate the median value of the delay violation probability according to the formula : Formula 22: ; judge Probability of violating target delay Relationship: if , the bandwidth is too large, narrow the search range: ; if , the bandwidth is too small, increase the search interval: ; Stop range: When The iteration stops when is the accuracy requirement, then the estimated value of the bandwidth is: Formula 23: ; in: Represents the minimum bandwidth and maximum bandwidth respectively.

[0012] Compared with the prior art, the present invention has the following beneficial effects: The present invention models the arrival process of tactile services in a teleoperation system based on the burst characteristics of tactile services. It not only analyzes the correlation of the arrival process of tactile services in the two directions of master control to slave control and slave control to master control, but also considers the time domain, frequency domain and high-order characteristics of service arrival, proposes a new method for constructing the arrival martingale process, and predicts service arrival to achieve accurate measurement of the burstiness of tactile services. It proposes a new bandwidth estimation method to achieve dynamic estimation of the bandwidth demand of tactile services. The present invention can efficiently handle the burstiness of tactile services, laying a foundation for ensuring the service quality of tactile communication, optimizing bandwidth resource allocation and improving network performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The figure is a flow chart of the method of the present invention.

[0014] Figure 2 For the current setting λ= 10Mbps, σ= At 4Mbps, change Value, at different delay boundaries k The estimated bandwidth is C of the changing trend.

[0015] Figure 3 For the current setting =1 e -3, σ= At 4Mbps, change l Value, at different delay boundaries k The estimated bandwidth is C of the changing trend.

[0016] Figure 4 For the current setting =1 e -3, λ= At 10Mbps, change Value, at different delay boundaries k The estimated bandwidth is C of the changing trend.

[0017] Figure 5 For the current setting σ= 4Mbps, k= 20 ms When, change Value, in different l The estimated bandwidth is C of the changing trend.

[0018] Figure 6 For the current setting λ= At 10Mbps, k= 10 ms When, change Values ​​at different standard deviations The estimated bandwidth is C of the changing trend. DETAILED DESCRIPTION

[0019] In order to have a clearer understanding of the technical features, purposes and beneficial effects of the present invention, the technical solution of the present invention is now described in detail below, but it should not be construed as limiting the applicable scope of the present invention.

[0020] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0021] An embodiment of the present invention provides a bandwidth estimation method for tactile teleoperation services, the flow chart of which is as follows: Figure 1 As shown, the method comprises the following steps: Step 1: Modeling the tactile business arrival process; Existing research work on tactile service modeling assumes that tactile service arrivals are independent and identically distributed. Different from this, the present invention first analyzes the autocorrelation and partial autocorrelation coefficients of the "master → slave" (master to slave) and "slave → master" (slave to master) data packet arrival rates to determine whether the respective data packet arrival processes are independent and identically distributed. According to the characteristics of its autocorrelation and partial autocorrelation coefficients, a suitable model is selected for modeling: if it is independent and identically distributed, the parameters of the candidate probability distribution function are fitted using maximum likelihood estimation. To ensure the rationality of the analysis results of the invention, the present invention will compare the fitting results of multiple candidate probability distributions such as Poisson distribution, gamma distribution, lognormal distribution and generalized Pareto distribution. If it is a first-order autocorrelation, the Markov chain model is used for modeling. If it is order autocorrelation and , then use The model is built using an order autoregressive (AR) process. The probability distribution of the random variables involved in the autoregressive process adopts the candidate distribution, and the parameters of the candidate distribution are fitted using maximum likelihood estimation.

[0022] Assume that the time series data is ,in Indicates t Observed value of business traffic at the moment; Autocorrelation coefficient The measurement is t Data at the moment With hysteresis Data at the moment The correlation between them is calculated as: Formula 1: ; in, It is a time series The mean of is the lag period; n is the sample size of the time series, i.e. the total number of observations; Partial autocorrelation coefficient To measure the t Data at the moment With hysteresis Data at the moment The direct relationship between The correlation between the two after the influence of the lag period is calculated as: Formula 2: ; in: Indicates that before it is known Under the condition of lagged data, t Time data With hysteresis Time data The conditional covariance between Indicates t Time data Before it is known The conditional variance under the condition of lag period; If the autocorrelation coefficients for all lags and partial autocorrelation coefficient If they are all close to 0, that is, there is no significant correlation, then the time series data can be considered to be independent and identically distributed, indicating that there is no dependency between the data at the current moment and the data at any lag period, which conforms to the characteristics of independent and identical distribution.

[0023] Step 2: Correlation analysis of the two-part arrival process; For the correlation of the arrival of the two parts of services, "master control → slave control" and "slave control → master control", their tactile information has an obvious leading-following relationship. The correlation coefficient of their time difference is calculated to measure the degree of correlation between them.

[0024] The time difference correlation coefficient uses the coefficient to test the lag relationship between time series variables, that is, the absolute value of the correlation coefficient is used to determine the time difference relationship between two variables. The calculation formula is: Formula 3: ; in: The service arrival process of "master control → slave control" and "slave control → master control" has a time difference of The time difference correlation coefficient of , express Observed values ​​of business traffic from master to slave and from slave to master at all times; express Observed value of business traffic from master control to slave control at all times; Indicates the sample average of the observed value of the service flow from the master to the slave; It represents the sample average of the traffic observation values ​​from the control to the master control; t Indicates time slot; represents the sample size; Indicates the lag period.

[0025] Step 3: Martingale expression method of tactile burst arrival process; The definition of reaching the martingale is: if for any There are always Martingale parameters , so that the random process is a supermartingale, then a cumulative arrival process Subject to the Martingale; among them: represents the martingale parameter; Indicates time slot The number of packets served; Indicates time slot 0 to time slot Cumulative number of packets served; same as arrival martingale, Represents the relevant characteristic function of the arrival process, reflecting the correlation between random variables. are independent and identically distributed, then ; Represents the arrival martingale parameter, which is used to maintain the supermartingale property of the arrival martingale; Represents the parameters of the arrival martingale.

[0026] make Represents the arrival process and describes the traffic volume of data packets arriving per unit time; represents the cumulative arrival process, Equal to the initial time to time slot Cumulative number of packets arriving.

[0027] if The Pareto distribution is an independent and identically distributed distribution. The Pareto distribution is a special case of the general Pareto distribution. Its probability distribution function is: Formula 4: ; in: Indicates an event Probability of occurrence; x represents the value of a random variable; Represents a random variable The lower bound of Represents the tail index.

[0028] The present invention utilizes tail index Measuring the burstiness of tactile services. The smaller it is, the more severe the heavy-tail burst is. Represents the arrival martingale process. The present invention constructs the arrival martingale of the tactile service as an exponential super-martingale. Heavy tail bursts are the main factor causing delay degradation. On the one hand, the present invention uses the exponential super-martingale structure to assign a higher impact weight to the burst arrival. On the other hand, due to the tail exponential It reflects both the heavy tail burstiness and the high-order characteristics of the arrival process. Impact on the arrival martingale. According to the properties of the Pareto distribution, the present invention constructs the arrival martingale expression corresponding to the Pareto distribution as follows: Formula 5: ; in: represents the martingale parameter, Represents the Martingale parameter proportional factor. According to the properties of Martingale , solve the martingale expression parameters and ,in Indicates the expectation symbol; express The arrival martingale process of time; Indicates from time slot 0 to time slot The arrival process. It can be proved and hour, It is a martingale process; if belong The AR process is of the form: Formula 6: ; in: Indicates before The autoregressive coefficient at each moment; i represents the index of the lagged term; Represents an autoregressive process Before The value of a moment; Represents the constant expected value, which determines the overall offset of the sequence; represents standard deviation; is an independent and identically distributed Gaussian distribution with a mean of 0 and a variance of 1.

[0029] The present invention still uses the exponential martingale structure The arrival martingale of an autoregressive process of order . On the time scale, the higher the time correlation, the greater the impact on the delay. Therefore, The order of the martingale constructed by autoregressive The present invention assigns a higher influence weight to high time correlation and constructs the arrival martingale expression as follows: Formula 7: ; in: j Indicates a point in time; Represents an autoregressive process Before The value of the moment; Represents the Martingale expression parameter, which is the service rate.

[0030] Then, using the properties of the martingale , solve the martingale expression parameters . will with The high-order characteristics are closely related to the frequency domain characteristics.

[0031] In addition, for other heavy-tailed arrival models such as the general ON-OFF process, the corresponding supermartingale process of the general ON-OFF heavy-tailed arrival is constructed according to the definition and properties of the supermartingale. The functional form of this supermartingale process is closely related to the probability distribution characteristics of the ON state and OFF state duration.

[0032] Step 4: Predict the business arrival process; According to forecasting theory, the random variable yes Measurable, among which, express Algebra, is a collection of information; if the random variable yes The measurable ones are: Formula 8: ; From the definition of conditional mathematical expectation, we know that yes Measurable, among which, express Algebra, including ratios More information; if yes Measurable, according to the properties of the martingale Derived , and finally solve , Equal to the initial time to time slot Cumulative number of packets arriving.

[0033] if Obeying the Pareto distribution of independent and identical distribution, according to the properties of the martingale, we know that the random variable yes It can be measured. According to formula 5, we can know that the Martingale Measurable, that is Measurable.

[0034] Therefore, we can Convert to ,Right now: Formula 9: ; The final solution is: Formula 10: ; if belong The AR process is measurable, that is, It can be measured. From formula 7, we can know that the Martingale Measurable, that is Measurable.

[0035] Therefore, we can Convert to ,Right now: Formula 11: ; The final solution is: Formula 12: ; in: Represents an autoregressive process Before The value of a moment.

[0036] Step 5: Estimate the bandwidth of business traffic; The service rate is defined as the bandwidth required by the traffic, and the service is represented as a constant rate service model.

[0037] if belong The order autoregressive (AR) process is as follows: satisfy The autoregressive arrival model AR(p) is: Formula 13: ; Formula 14: ; in: represents the martingale parameter, which is A special value defined in the range of ; represents variance; represents the delay violation probability coefficient; represents the autoregressive coefficient; express The standard deviation of Then the delay violation probability is: Formula 15: ; in: represents probability; Indicates time slot Delay represents the delay bound; consider The special case is: Formula 16: ; Then the variance is: Formula 17: ; in: express The variance of It represents the variance operator; express Gaussian process of time; therefore: Formula 18: ; Formula 19: ; in: Indicates the mean value symbol; The relevant characteristic function representing the arrival process can reflect the correlation between random variables; Represents the relevant characteristic functions of the service process; Then the delay violation probability is derived as: Formula 20: ; Let the target delay violation probability , using the bisection method to estimate the bandwidth C , the specific process is as follows: (1) Initialization parameters; Define constants in formulas and set delay bounds k The range is , generating multiple k value.

[0038] (2) Define the binary search interval; For each k value, set the initial bandwidth search range to . .

[0039] (3) Binary iteration; Calculate the bandwidth midpoint : Formula 21: ; Calculate the median value of the delay violation probability according to the formula : Formula 22: ; judge Probability of violating target delay Relationship: if , the bandwidth is too large, narrow the search range: ; if , the bandwidth is too small, increase the search interval: .

[0040] (4) Stop interval; when The iteration stops when is the accuracy requirement, then the estimated value of the bandwidth is: Formula 23: ; in: Represents the minimum bandwidth and maximum bandwidth respectively.

[0041] Example 1: Set the parameter values ​​as shown in Table 1;

[0042] When setting λ= 10Mbps, σ= At 4Mbps, change Value, observe at different delay boundaries k The estimated bandwidth is C The simulation results are as follows: Figure 2 Delay Bound k The delay tolerance is set. As increases, the system's tolerance for latency increases and bandwidth requirements decrease. That is, the system can tolerate more requests with latency violations. Therefore, in order to meet looser latency requirements, the system does not need high bandwidth. =1 e -3, the system can tolerate a higher proportion of delay fluctuations, so the bandwidth requirement does not need to be as =1 e -5, the system still meets the requirements at a lower bandwidth. In the bandwidth estimation process, the binary search is used. The binary method continuously adjusts the bandwidth according to the relationship between the current delay bound and the delay violation probability until the accuracy requirement is met. As the delay bound increases, the bandwidth adjustment becomes slower and eventually the bandwidth approaches the expected value of the system. l .

[0043] When setting =1 e -3, σ= At 4Mbps, change l Value, observe at different delay boundaries k The estimated bandwidth is C The simulation results are as follows: Figure 3 As shown. λ=At 5Mbps, the arrival volume per unit time is small, the system load is light, and the system processing capacity is relatively strong. Therefore, at the same delay boundary k The system can handle more requests with lower bandwidth. l This means that the system has a high arrival volume and a heavy load. The system needs a larger bandwidth to maintain a low probability of delay violation. k When the delay bound increases, the system needs a larger bandwidth to cope with the higher probability of delay violation, so the bandwidth decreases faster. As the delay bound increases, the system gradually stabilizes and the rate of bandwidth decrease slows down.

[0044] When setting =1 e -3, λ= At 10Mbps, change Value, observe at different delay boundaries k The estimated bandwidth is C The simulation results are as follows: Figure 4 Standard deviation σ= At 2Mbps, the system has less volatility, which means that the arrival process changes more smoothly, the system load is more stable, the curve drops more slowly and tends to the mean earlier. When the standard deviation is large, the system is more volatile, which means that the uncertainty of the arrival process is high, and the system needs a higher bandwidth to cope with these fluctuations. The curve drops rapidly at the beginning because the noise has a greater impact, resulting in a violent response of the system. The larger the standard deviation, the stronger the initial response of the system. This shows that the system is highly sensitive to external disturbances and adjusts quickly at the beginning, but the system gradually stabilizes over time.

[0045] When setting σ= 4Mbps, k= 20 ms When, change Values, observed in different l The estimated bandwidth is C The simulation results are as follows: Figure 5 As shown in the figure, as the expected value increases, the bandwidth also shows an upward trend. This shows that as the arrival rate increases, the system needs more bandwidth to handle higher traffic. This is because a higher packet arrival rate requires more bandwidth resources to avoid system delays or packet loss due to insufficient bandwidth. =1 e The -5 curve is at the top because at a lower latency violation probability, the system must handle a larger request flow with a higher bandwidth to ensure that the latency does not exceed the set threshold.

[0046] When setting λ= At 10Mbps,k= 10 ms When, change values, observed at different standard deviations The estimated bandwidth is C The simulation results are as follows: Figure 6 shown. =1 e The -5 curve is at the top, which means that at a lower probability of delay violation, the system must provide higher bandwidth to cope with the delay risk caused by increased noise or volatility. When the standard deviation increases, the bandwidth demand grows faster because the system must ensure a low probability of delay violation to ensure that the delay limit is not exceeded even with large fluctuations.

[0047] The above are only preferred embodiments of the present invention. It should be pointed out that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention. These should also be regarded as the protection scope of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent.

Claims

1. A bandwidth estimation method for tactile teleoperation service, characterized in that: The following steps are involved: Step 1: Modeling the tactile business arrival process; Analyze the autocorrelation and partial autocorrelation coefficients of the data packet arrival rate from the master to the slave and from the slave to the master. According to the characteristics of the autocorrelation and partial autocorrelation coefficients, select the model for modeling: if it is independent and identically distributed, use the maximum likelihood estimation to fit the candidate probability distribution; if it is the first-order autocorrelation, use the Markov chain model; if it is order autocorrelation and ,use The model is built using an order autoregressive process. The probability distribution of the random variables involved in the autoregressive process adopts a candidate distribution, and the parameters of the candidate distribution are fitted using maximum likelihood estimation. Step 2: Correlation analysis of the two-part arrival process; Calculate the time difference correlation coefficient of the service arrival process from the master control to the slave control and from the slave control to the master control to measure the correlation between the two; Step 3: Martingale expression method of tactile burst arrival process; A martingale representation is constructed for the tactile service arrival process to reflect its burstiness and time correlation, including: Independent and identically distributed Pareto distribution: construct exponential upper martingale, considering the impact of tail index on reaching the martingale; Order autoregressive process: construct exponential martingale and consider the influence of order on reaching martingale; Step 4: Predict the business arrival process; The properties of the martingale are used to predict the cumulative arrival packet traffic in future time slots, including: Independent and identically distributed Pareto distribution: express predictions via martingale; Order autoregressive process: express predictions through martingale; Step 5: Estimate the bandwidth of business traffic; Define the service rate as bandwidth and build a constant rate service model. Calculate the delay violation probability for the autoregressive arrival model AR(p). Use the dichotomy method to estimate the bandwidth.

2. According to the bandwidth estimation method for tactile teleoperation service according to claim 1, in the step 1, the calculation formula of the autocorrelation coefficient and the partial autocorrelation coefficient is as follows: Assume that the time series data is , Indicates t Observed value of business traffic at the moment; Autocorrelation coefficient To measure the t Data at the moment With hysteresis Data at the moment The correlation between them is calculated as: Formula 1: ; in, It is a time series The mean of is the lag period; n is the sample size of the time series, i.e. the total number of observations; Partial autocorrelation coefficient To measure the t Data at the moment With hysteresis Data at the moment The direct relationship between The correlation between the two after the influence of the lag period is calculated as: Formula 2: ; in: Indicates that before it is known Under the condition of lagged data, t Time data With hysteresis Time data The conditional covariance between Indicates t Time data Before known The conditional variance under the condition of lag period.

3. According to the bandwidth estimation method for tactile teleoperation service of claim 1, in the step 2, the calculation formula of the time difference correlation coefficient is: Formula 3: ; in: Indicates that the service arrival process from the master to the slave and from the slave to the master is The time difference correlation coefficient of , express Observed values ​​of business traffic from master to slave and from slave to master at all times; express Observed value of business traffic from master control to slave control at all times; Indicates the sample average of the observed value of the service flow from the master to the slave; It represents the sample average of the traffic observation values ​​from the control to the master control; t Indicates time slot; represents the sample size; Indicates the lag period.

4. According to the bandwidth estimation method for tactile teleoperation service of claim 1, the specific process of step 3 is as follows: make Represents the arrival process and describes the traffic volume of data packets arriving per unit time; represents the cumulative arrival process, Equal to the initial time to time slot The cumulative number of packets arriving; if The probability distribution function of the Pareto distribution that obeys independent and identical distribution is: Formula 4: ; in: Indicates an event Probability of occurrence; x represents the value of a random variable; Represents a random variable The lower bound of represents the tail index; make Represents the arrival martingale process, constructing the arrival martingale of the tactile business as an exponential upper martingale, considering the tail exponent The impact on the arrival of the martingale, according to the properties of the Pareto distribution, the arrival of the martingale expression corresponding to the Pareto distribution is constructed as follows: Formula 5: ; in: represents the martingale parameter, Represents the Martingale parameter proportional factor; according to the properties of Martingale , solve the parameters of the martingale expression and ,in Indicates the expectation symbol; express The arrival martingale process of time; Indicates from time slot 0 to time slot Arrival process; proof and hour, It is a martingale process; if belong The form of the autoregressive process is: Formula 6: ; in: Indicates before The autoregressive coefficient at each moment; i represents the index of the lagged term; Represents an autoregressive process Before The value of a moment; represents the expected value of a constant; represents standard deviation; is an independent and identically distributed Gaussian distribution with a mean of 0 and a variance of 1; Using exponential supermartingale construction The arrival martingale of an autoregressive process of order The influence of, that is, the time domain characteristics of the arrival process, constructs the arrival martingale expression as: Formula 7: ; in: j Indicates a point in time; Represents an autoregressive process Before The value of the moment; represents the martingale expression parameter, which is the service rate; Using the properties of martingale , solve the parameters of the martingale expression .

5. According to the bandwidth estimation method for tactile teleoperation service of claim 1, the specific process of step 4 is as follows: According to forecasting theory, the random variable yes It can be measured if yes Measurable: Formula 8: ; From the definition of conditional mathematical expectation, we know that yes It can be measured if yes It can be measured according to the properties of the martingale Derived , and finally solve , Equal to the initial time to time slot The cumulative number of packets arriving; if Obeying the Pareto distribution of independent and identical distribution, according to the properties of the martingale, we know that the random variable yes It can be measured that, through formula 5, we can know that the martingale Measurable, that is Measurable; Therefore, Convert to ,Right now: Formula 9: ; The final solution is: Formula 10: ; if belong The order autoregressive (AR) process can be measured, that is, can be measured; from formula 7, we can know that the Martingale Measurable, that is Measurable; Therefore, Convert to ,Right now: Formula 11: ; The final solution is: Formula 12: ; in: Represents an autoregressive process Before The value of a moment.

6. The bandwidth estimation method for tactile teleoperation service according to claim 1, wherein the specific process of step 5 is as follows: Define the service rate as the bandwidth required by the traffic, and represent the service as a constant rate service model; if belong The order autoregressive process is in the form of Equation 6; for the service rate satisfy The autoregressive arrival model AR(p) is: Formula 13: ; Formula 14: ; in: represents the martingale parameter, which is A special value defined in the range of ; represents variance; represents the delay violation probability coefficient; represents the autoregressive coefficient; express The standard deviation of Then the delay violation probability is: Formula 15: ; in: represents probability; Indicates time slot Delay represents the delay bound; consider The special case is: Formula 16: ; Then the variance is: Formula 17: ; in: express The variance of It represents the variance operator; express Gaussian process of time; therefore: Formula 18: ; Formula 19: ; in: Indicates the mean value symbol; Represents the relevant characteristic function of the arrival process; Represents the relevant characteristic functions of the service process; Then the delay violation probability is derived as: Formula 20: ; Let the target delay violation probability , using the bisection method to estimate the bandwidth C , the specific process is as follows: Initialization parameters: define constants in formulas and set delay limits k The range is , generating multiple k value; Define the binary search interval: For each k value, set the initial bandwidth search range to ; Binary iteration, including: Calculate the bandwidth midpoint : Formula 21: ; Calculate the median value of the delay violation probability according to the formula : Formula 22: ; judge Probability of violating target delay Relationship: if , the bandwidth is too large, narrow the search range: ; if , the bandwidth is too small, increase the search interval: ; Stop range: When The iteration stops when is the accuracy requirement, then the estimated value of the bandwidth is: Formula 23: ; in: Represents the minimum bandwidth and maximum bandwidth respectively.

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