A data transmission dynamic scheduling method

Abstract

The application discloses a data transmission dynamic scheduling method, comprising the following steps: modeling the channel quality parameter by using a CIR model, and updating the model parameter in real time during operation; constructing an instant transmission utility function based on the current transmission amount and the current channel quality, and introducing a value function to obtain the optimal transmission amount; using an ALMA moving average signal mechanism, generating a trigger signal by using two ALMA moving averages, and judging whether the current time is the transmission execution time; if it is judged that the transmission task is executed, mapping the strength of the ALMA signal to an execution ratio, and generating a data task transmission control instruction according to the optimal transmission amount and the execution ratio. The application not only improves the comprehensive utility of large-capacity data transmission, but also significantly reduces the system interference and the opportunity cost of channel resources.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication and signal processing technology, specifically relating to a dynamic scheduling method for time-division data transmission that combines a random channel quality prediction model with time-series signal analysis. Background Technology

[0002] In wireless communication systems, there is often a need to perform complex data tasks involving a large volume of data that can be transmitted in packets. Examples include large-scale data packet transmission (such as cloud backup and high-definition video stream buffering). The transmission environment for these tasks is often dynamically changing, and key physical parameters describing the channel state (such as instantaneous channel quality and signal-to-noise ratio SINR) exhibit random fluctuations.

[0003] Currently, widely used scheduling methods mainly include fixed-rate transmission (Fixed Slice) and time-weighted average scheduling. These methods aim to smooth the transmission process and distribute channel resource usage by splitting large data packets into multiple small data frames or allocating transmission volume in a time-weighted manner. However, they are usually based on preset static allocation logic, without modeling or predicting the future trends of key channel quality parameters, nor can they dynamically adjust the transmission rhythm according to real-time changes in parameters. This often leads to the system missing the window of efficient execution when environmental parameters fluctuate drastically, or incurring unnecessary execution overhead during stable environmental periods, resulting in significant waste of spectrum resources or loss of transmission efficiency.

[0004] On the other hand, some advanced intelligent scheduling strategies are beginning to incorporate predictive models to optimize execution decisions. For example, certain random channel models (such as mean-regression process models) can model the mean regression (such as slow fading characteristics) and random fluctuation characteristics (such as fast fading) of instantaneous channel quality, thereby achieving effective prediction of future parameter paths. Meanwhile, timing methods based on moving average and other technical analysis techniques capture trend signals of channel quality changes by filtering and suppressing noise in historical data.

[0005] Although these two technologies are widely used in their respective fields, existing technologies often treat channel prediction and channel trend signal judgment separately, lacking deep integration within the same execution framework. Furthermore, data transmission overhead includes not only the system overhead of executing instructions (such as power consumption and interference costs) but also the opportunity cost caused by inappropriate transmission rhythm. Achieving a dynamic balance between transmission efficiency, system overhead, and (channel) opportunity cost, and constructing an adaptive transmission system that can utilize the advantages of channel prediction while also considering trend signals, has become a pressing technical challenge. Summary of the Invention

[0006] In view of the above, the purpose of this invention is to provide a dynamic scheduling method for data transmission based on the combination of CIR channel model and ALMA signal. This method can calibrate the channel model in real time and accurately optimize the batch transmission volume in a highly dynamic environment. It also combines trend signals for noise filtering to avoid invalid execution, and finally achieves optimal scheduling for large-capacity data tasks, thereby improving the overall transmission efficiency of the system.

[0007] The objective of this invention is achieved through the following technical solution: a dynamic data transmission scheduling method, comprising the following steps:

[0008] (1) The CIR model is used to model the channel quality parameters, and the model parameters are updated in real time during operation. , , );

[0009] (2) Construct an instantaneous transmission utility function based on the current transmission volume and the current channel quality, and introduce a value function to solve for the optimal transmission volume;

[0010] (3) The ALMA moving average signal mechanism is adopted, and the fast and slow ALMA moving averages are used to generate trigger signals to determine whether it is the right time to perform transmission.

[0011] (4) If it is determined that a transmission task is to be executed, the intensity of the ALMA signal is mapped to the execution ratio, and a data task transmission control instruction is generated according to the optimal transmission amount and the execution ratio.

[0012] Furthermore, the channel quality parameters are modeled using the CIR model, specifically as follows:

[0013]

[0014] in, Indicates time The instantaneous channel quality value; Control its rate of reversion to the long-term mean; It is the long-term average channel quality level; Control its sensitivity to random disturbances; It represents standard Brownian motion; N(0,1) is a standard normal distribution with a mean of 0 and a variance of 1.

[0015] Furthermore, based on the Euler-Maria method, the continuous CIR process is analyzed at step size... Discretize the model and rewrite it in standard linear regression form. Solve the regression equation and calculate the model parameters. , , ).

[0016] Furthermore, it also includes: using a first-order moment approximation method to recursively predict short-term instantaneous channel quality, specifically: the conditional expectation of instantaneous channel quality in the CIR model has an analytical expression. , When the first-order Taylor approximation holds true... This simplifies to the Euler approximation form. .

[0017] Furthermore, it also includes: introducing the Euler approximation equation into the noise term. Indicates future instantaneous channel quality .

[0018] Furthermore, it also includes: introducing overhead terms related to the amount of data transmitted into the instantaneous transmission utility function. .

[0019] Furthermore, the difference between the fast and slow ALMA moving averages is calculated and normalized. The normalized difference between the fast and slow ALMA moving averages is the strength of the ALMA signal. ;

[0020] like If the value is greater than 0, it indicates that it is time to perform a transmission; otherwise, it is not time to perform a transmission.

[0021] Introducing upper and lower thresholds and ,when Exceed And continuous If the cycle is maintained, it is determined when to execute the transmission; when In When dealing with intervals, it is necessary to combine the amount of data to be transmitted or channel momentum indicators for auxiliary judgment; if Below If it is not the right time to perform the transmission.

[0022] Furthermore, the intensity of the ALMA signal is mapped. For execution ratio Specifically:

[0023]

[0024] in, To achieve the maximum execution ratio, and These are the preset minimum and maximum thresholds, respectively.

[0025] The present invention also provides an electronic device, including a memory and a processor, characterized in that the memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the above-described dynamic data transmission scheduling method.

[0026] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements a data transmission dynamic scheduling method as described above.

[0027] The beneficial effects of this invention are as follows: On the one hand, by utilizing the mean recovery and non-negativity characteristics of the CIR model, accurate prediction and real-time calibration of instantaneous channel quality are achieved, enabling the grasp of the optimal transmission window when channel quality fluctuates drastically; on the other hand, the ALMA (Alternating Current Model) signaling mechanism effectively filters out fast fading noise and captures the inflection point of slow fading trends, avoiding extreme transmission scheduling with frequent small or concentrated bursts of power; by deeply integrating channel quality prediction and transmission decision-making through a dynamic programming framework, a dynamic balance can be achieved between transmission power consumption, system interference, and channel opportunity cost; the adjustable maximum transmission ratio control makes the execution system both flexible and capable of fully exerting its power when the signal is strong; in summary, this invention not only improves the overall utility of large-capacity data transmission (such as total throughput) but also significantly reduces system interference and the opportunity cost of channel resources. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is the overall flowchart of the present invention.

[0030] Figure 2 This is a comparison chart of the simulated path and the actual instantaneous channel quality in the CIR model.

[0031] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0032] The present invention will now be described in detail with reference to the accompanying drawings. Unless otherwise specified, the features of the following embodiments and implementations can be combined with each other.

[0033] This invention discloses a dynamic data transmission scheduling method based on the combination of CIR channel model and ALMA signal, applied to a wireless communication system (hereinafter referred to as the system). See [link to relevant documentation]. Figure 1 This includes the following steps:

[0034] (1) Instantaneous channel quality modeling: The Cox-Ingersoll-Ross (CIR) model is used to model a key channel quality parameter describing the external environment or system state, and its model parameters are updated periodically or triggered during operation. , , After estimating the CIR model parameters, the instantaneous channel quality is predicted.

[0035] (2) Transmission volume decision: Design a dynamic programming (DP) module. This module is based on the current time point. Current transmission volume Current value of instantaneous channel quality The goal of finding the optimal execution path is to maximize a pre-defined cumulative transmission utility function related to transmission volume and instantaneous channel quality, and to introduce a value function to obtain the optimal transmission volume; where , This represents the current amount of data remaining.

[0036] (3) Execution timing judgment: The Arnaud Legoux Moving Average (ALMA) signal mechanism is adopted. Two ALMA moving averages, fast and slow, are used to generate trigger signals to determine whether the current time is a suitable time to execute transmission, so as to filter out unnecessary transmissions caused by short-term noise in channel quality (such as fast fading).

[0037] (4) Joint strategy execution and control instruction generation: The suggested transmission amount output by the DP module. Combined with ALMA signals. A maximum transmission ratio control is introduced, mapping the strength of the ALMA signal to the actual execution ratio. Generate the final data task transmission control command. .

[0038] Specifically, step (1) includes the following sub-steps:

[0039] (1.1) Instantaneous Channel Quality Modeling. To characterize the dynamic behavior of a key channel quality parameter, the Cox–Ingersoll–Ross (CIR) model is used. This model describes the evolution of the channel quality parameter through the following stochastic differential equation:

[0040]

[0041] in, Indicates time The instantaneous channel quality value (e.g., transmission efficiency per unit of resource, which is a non-negative real number). Control its rate of reversion to the long-term mean; This is the long-term average channel quality level of this parameter; Control its sensitivity to random disturbances; This model represents standard Brownian motion. It effectively describes a class of physical processes (such as wireless channel fading) or system state variables exhibiting mean regression and non-negativity. Parameter estimation and updates can be performed using standard discretization and linear regression methods. Based on the estimated parameters, possible future paths for the channel quality parameters can be generated through first-order moment recursion or Monte Carlo simulation, providing input for subsequent dynamic programming.

[0042] (1.2) Discretization and Regression of Parameter Estimation. Based on the Euler-Maria method, for continuous CIR processes at step size... Discretization is then performed. Indicates the first A discrete time point, Indicates the first The random disturbance terms at each time step follow a standard normal distribution N(0,1) with mean 0 and variance 1. We obtain: To eliminate the cause The introduced heteroscedasticity effect makes

[0043] , ,

[0044] The discretized equation described above can then be rewritten in standard linear regression form:

[0045]

[0046] Defined as a standardized instantaneous change in channel quality, it serves as the dependent variable in the regression model; Defined as the first independent variable in the regression model; Defined as the second independent variable in the regression model; where These are the first and second regression coefficients to be estimated. Organize the N sample data into a dependent variable vector. Independent variable parameter matrix The coefficient vector is solved using the least squares method. You can get The estimated value, , The estimated value And then through Calculate the long-run equilibrium level Estimates; regression residuals Then used for calculation The estimated value To achieve real-time parameter updates, the estimation process in step (1.2) can be executed periodically (e.g., every [time period]). (time slots), using the latest A scrolling window consisting of data points is used to resolve the problem. Thus, the updated model parameters are obtained. , , ).

[0047] (1.3) Recursive Prediction Based on First-Order Moments. Given the estimated CIR model parameters, a recursive prediction of the short-term instantaneous channel quality can be made using the first-order moment approximation method. Specifically, the conditional expectation of the instantaneous channel quality in the CIR model has an analytical expression, given a time... The instantaneous channel quality is Its small time step The expected value of the subsequent condition is This formula reflects the mean-recovery characteristic, meaning that the instantaneous channel quality will gradually approach the long-term mean. .when When the value is relatively small (so that the first-order Taylor approximation holds), When ), the expression can be simplified to the Euler approximation form. This facilitates practical calculations. The method can start from the last observation point of historical data and proceed recursively. For example, setting... This method, based on the timing of the forecast, can update the predicted value daily until the desired forecast period ends. Its advantages lie in its low computational cost, clear structure, and suitability as a basic forecasting tool. However, its disadvantage is that it only reflects the expected trend of instantaneous channel quality and fails to encompass its random fluctuations.

[0048] (1.4) Monte Carlo path generation based on discretization simulation. To more comprehensively characterize the uncertainty and random fluctuations of future instantaneous channel quality, the Monte Carlo simulation method can be further introduced. In this method, the CIR model first needs to be discretized to adapt to numerical simulation. A common approach is to use the Euler discretization scheme to represent the future instantaneous channel quality as...

[0049] ,

[0050] in Let be independent and identically distributed standard normal random variables, simulating the random disturbance term of instantaneous channel quality. In each simulation step, adding a noise term generates a future instantaneous channel quality path, iterating continuously until the prediction end. The complete path sequence was then obtained. This process can be repeated several times to generate a sample family. Then, the sample distribution at each time point is statistically analyzed to obtain information such as the predicted mean, variance, and confidence interval of the instantaneous channel quality. This information is used for the numerical calculation of the expected value of the Bellman equation in step (2.4).

[0051] Specifically, step (2) includes the following sub-steps:

[0052] (2.1) Decision-making sequence and state definition: The total amount of data to be transmitted to be executed. (e.g., 1000 Mbits) divided into discrete... The decision-making point is denoted as... ,in Corresponding to the start time. At each point in time. The system observes the current dynamic parameter values. Then based on the remaining amount of data to be transmitted Determine the current transmission volume The decision variable satisfies After the decision is implemented, the remaining data volume is... renew.

[0053] (2.2) Construction of the instantaneous transmission utility function. Assume the transmission volume per period... In the current channel quality An immediate utility can be obtained, the magnitude of which is defined by a linear function:

[0054]

[0055] This linear form can be interpreted as "total utility of instantaneous transmission". For example, if The number of resource blocks allocated. Given the current unit resource block transmission efficiency (bits / block), then This represents the total number of bits transmitted in the current period. In more complex scenarios, system overhead related to the transmission volume can be subtracted from this. (e.g., transmission power consumption or interference with adjacent channels), which can be rewritten as:

[0056]

[0057] (2.3) Setting the value function based on the Bellman optimality principle: In order to maximize the cumulative transmission utility over multiple periods, a value function is introduced. Its meaning is: at a certain point in time The remaining data volume is And the instantaneous channel quality is Under the given conditions, the maximum expected cumulative transfer utility that can be obtained from the current state to the end. The value function satisfies the following recursive relation (Bellman equation):

[0058]

[0059] in, Discount factor. Optimal transmission volume. That is to make The solution to the expression that yields the maximum value.

[0060] (2.4) Calculation of conditional expectation. The value function in step (2.3) Expectations For the next moment The randomness is taken as the expectation. This expectation can be calculated using one of the two methods described in step (1):

[0061] (a) Using the first-moment approximation from step (1.3): the expectation Approximately ,in Depend on The analysis is provided. This method is computationally fast, but it sacrifices information about fluctuations.

[0062] (b) Monte Carlo simulation using step (1.4): This is a direct application of the sample family information obtained in step (1.4). By generating... Channel quality path This expectation can be approximated as This method more accurately captures the random fluctuations in channel quality.

[0063] (2.5) By solving the equation in reverse iteration, the optimal transmission volume strategy under different states can be obtained.

[0064] Specifically, step (3) includes the following sub-steps:

[0065] (3.1) Parameter preset and variable definition. First, set the ALMA sliding window length according to different strategy cycles. Offset and smoothness parameters Specifically, window length The number of historical samples covered by the moving average and the offset are determined. Control the center position of the weight distribution, smoothness This affects the flatness or steepness of the weight curve. The two ALMA lines are... and subscript These represent the parameters for the fast and slow lines, respectively.

[0066] (3.2) Signal Construction and Normalization. At each time step... Calculate the difference between the fast line and the slow line. To eliminate the influence of amplitude scale differences across different varieties or cycles, a normalization factor is introduced. The normalized ALMA signal is defined as follows:

[0067]

[0068] Among them, when When this occurs, it indicates that the current parameter state is unsuitable for executing the task (e.g., system inefficiency), and is judged as an "invalid signal," so execution is not triggered. When the parameter change trend is favorable for execution, an execution trigger signal is issued.

[0069] (3.3) Threshold setting and signal filtering. To avoid high-frequency error signals caused by small fluctuations, upper and lower thresholds are introduced. and And in conjunction with a delayed confirmation mechanism.

[0070] (a) when Exceed And continuous If the execution rate remains constant for a given period, it is considered a "strong signal" (corresponding to the highest execution ratio in the mapping function).

[0071] (b) When If it is not, it is considered an "invalid signal" (corresponding to the lowest execution ratio in the mapping function, such as 0).

[0072] (c) When In When the interval is reached, an auxiliary judgment mechanism is activated:

[0073] (c1) Data volume to be transmitted indicator: Defines "transmission urgency" like Exceeding a certain urgency threshold (Indicating a heavy workload), then it is judged as a "medium-strong signal".

[0074] (c2) Channel momentum index: for example, calculating First-order difference like (Indicating that the signal momentum is increasing), then it is determined to be a "medium-strong signal".

[0075] (d) If the auxiliary judgment is "medium-strong signal", then in the mapping function of step (4.1), the following is adopted: arrive The linear interpolation ratio between them; if the auxiliary judgment fails (e.g. Not high If the signal is not clear, it is considered an "invalid signal". This filtering strategy can suppress noise while retaining key secondary trend inflection points based on task urgency and channel trends.

[0076] Specifically, step (4) includes the following sub-steps:

[0077] (4.1) Introduce maximum transmission ratio control: make the dynamic programming module control the transmission ratio at time 1. Output suggested transfer amount Define a maximum transmission ratio control parameter. This parameter can be preset by the system operator based on actual transmission needs, risk preferences, or historical data backtesting. It controls the maximum adjustment range of the ALMA signal to the DP recommendation. This is combined with the ALMA signal strength. Design a mapping function to convert signal strength into actual execution ratio. ,For example:

[0078]

[0079] Convert signal strength to actual execution ratio in a linear or nonlinear manner. This allows for smooth adjustment of the force.

[0080] (4.2) Determination of final execution volume: based on the actual execution ratio and recommended transfer volume The final generated (data) transmission control command contains the following transmission amount:

[0081]

[0082] Update remaining data volume Repeat steps (1) to (4) until the remaining data is processed.

[0083] In summary, this invention first performs real-time modeling and parameter updates of instantaneous channel quality (such as SINR) based on the Cox–Ingersoll–Ross (CIR) model, and obtains the expected and uncertainty distribution of future channel quality paths through discretized regression and first-order moment prediction or Monte Carlo simulation. Then, based on this, a multi-period dynamic programming (DP) module is designed to couple the remaining data volume with the predicted channel quality evolution, and solve for the optimal batch transmission volume that maximizes the discounted cumulative transmission utility at each discrete time point. Next, a dual ALMA (Arnaud-Legoux Moving Average) signal mechanism is introduced to normalize, double-threshold filter, and hysteresis acknowledgment of the difference between the fast and slow lines, so as to make the optimal timing judgment between (channel) fast fading noise and (channel) slow fading trend. Finally, the optimal transmission volume suggestion output by the DP module is combined with the ALMA signal triggering mechanism, and the signal strength is mapped to the actual execution ratio through an adjustable maximum order ratio control parameter, so as to realize adaptive dynamic time-sharing scheduling for large-capacity data tasks.

[0084] Table 1: Record Table of Quantitative and Time Parameters

[0085] The following is a specific example illustrating how the method of the present invention is implemented according to the above steps within a complete scheduling cycle. Assume the scheduling cycle is... Corresponding to five discrete time periods Initial total data volume Mbits; CIR model parameters are set to Step length daily Day; ALMA moving average parameters set to fast line window. Slow line window Offset Smoothness Signal threshold selection Maximum transmission ratio .

[0086] As shown in Table 1, the time points are... Initial channel quality (b / s / Hz), according to the conditional expectation formula Dynamic programming recommendations Mbits; No ALMA signal, execute as is, remaining Mbits; time point Actual measurement ,predict ;suggestion Mbits; ALMA calculation No threshold exceeded, no adjustment required; time point Actual measurement ,predict ;suggestion Mbits; ALMA signal Strong downward trend, press implement Mbits; time point Actual measurement ,predict ;suggestion Mbits; ALMA signal Neutral, execute the original value; point in time (Final Stage): Remaining Data Volume Mbits, execute all under boundary conditions Mbits. This table centrally presents the closed-loop process of "CIR model prediction → dynamic programming suggestion → ALMA signal → scaling adjustment → execution," and illustrates how this invention increases transmission volume during downlink trends and remains cautious during neutral or uplink periods by comparing changes in signal and execution quantities at different stages, thereby optimizing overall transmission efficiency. In practical applications, parameters and mapping functions can be further calibrated within this framework to improve strategy robustness and flexibility.

[0087] See Figure 2 This figure directly supports and demonstrates the effect of step (1) of this invention, "Instantaneous Channel Quality Modeling: CIR Model and Parameter Update". This invention proposes using a CIR model to characterize and predict the dynamic behavior of instantaneous channel quality. Its core is based on the following stochastic differential equation:

[0088]

[0089] Figure 2By comparing the simulated path generated by the model with the real historical data of the actual channel quality, it is proved that the CIR model can effectively capture the mean regression and random fluctuation characteristics of the channel quality parameter. The high fit between the two curves in the figure provides a reliable input basis for the subsequent step (2) "Transmission Volume Decision: Dynamic Programming", which proves that the channel quality prediction based on the CIR model is effective and reliable, thereby enhancing the overall feasibility and persuasiveness of the patent technical solution. The horizontal axis in the figure is time, and the vertical axis is the channel quality level. The orange curve represents the real historical trend of the target channel quality parameter over a period of time. The blue curve is the simulated path generated using the CIR model. Visually, the fluctuation pattern, mean level, and fluctuation range of the blue curve can well reproduce the dynamic characteristics of the real channel quality (orange curve). The contents described in the embodiments of this specification are merely an enumeration of the implementation forms of the inventive concept. The scope of protection of this invention should not be regarded as limited to the specific forms stated in the embodiments. The scope of protection of this invention also extends to the equivalent technical means that can be conceived by those skilled in the art based on the inventive concept.

[0090] Figure 2 The horizontal axis represents time, and the vertical axis represents the transmission value level. The orange curve represents the actual historical trend of the target channel over a period of time. The blue curve is a simulated path generated using the CIR model. Visually, the fluctuation pattern, mean level, and fluctuation range of the blue curve can well reproduce the dynamic characteristics of the real instantaneous channel quality (orange curve).

[0091] Figure 3 This is a schematic diagram of an electronic device structure provided in an embodiment of the present invention. Please refer to... Figure 3 The electronic device provided in this embodiment includes a memory and a processor. The memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions. When the program instructions are loaded and executed by the processor, a data transmission dynamic scheduling method of the present invention is implemented.

[0092] It should be noted that, in addition to Figure 3 In addition to the memory and processor shown, electronic devices may include other hardware depending on their actual functions, which will not be elaborated further.

[0093] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements a data transmission dynamic scheduling method as described above.

[0094] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0095] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0096] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0097] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0098] The embodiments described in this specification are merely examples of implementations of the inventive concept. The scope of protection of this invention should not be considered as limited to the specific forms stated in the embodiments. The scope of protection of this invention also extends to equivalent technical means that can be conceived by those skilled in the art based on the inventive concept.

Claims

1. A dynamic data transmission scheduling method, characterized in that, Includes the following steps: (1) The CIR model is used to model the channel quality parameters, and the model parameters are updated in real time during operation. , , ); Specifically, the CIR model is used to model the channel quality parameters, as follows: in, Indicates time The instantaneous channel quality value; Control its rate of reversion to the long-term mean; It is the long-term average channel quality level; Control its sensitivity to random disturbances; This represents standard Brownian motion; N(0,1) is a standard normal distribution with a mean of 0 and a variance of 1. (2) Construct an instantaneous transmission utility function based on the current transmission volume and the current channel quality, and introduce a value function to solve for the optimal transmission volume; (3) The ALMA moving average signal mechanism is adopted, and the fast and slow ALMA moving averages are used to generate trigger signals to determine whether it is the right time to perform transmission. (4) If it is determined that a transmission task is to be executed, the intensity of the ALMA signal is mapped to the execution ratio, and a data task transmission control instruction is generated according to the optimal transmission amount and the execution ratio.

2. The data transmission dynamic scheduling method according to claim 1, characterized in that, Based on the Euler-Maria method, for continuous CIR processes at step size Discretize the model and rewrite it in standard linear regression form. Solve the regression equation and calculate the model parameters. , , ).

3. The data transmission dynamic scheduling method according to claim 1, characterized in that, Also includes: A first-order moment approximation method is used to recursively predict short-term instantaneous channel quality. Specifically, the conditional expectation of instantaneous channel quality in the CIR model has an analytical expression. , When the first-order Taylor approximation holds true... This simplifies to the Euler approximation form. .

4. The data transmission dynamic scheduling method according to claim 3, characterized in that, Also includes: Introducing the Euler approximation equation into the noise term Indicates future instantaneous channel quality .

5. The data transmission dynamic scheduling method according to claim 1, characterized in that, This also includes: introducing overhead related to the amount of data transmitted in the instantaneous transmission utility function. .

6. The data transmission dynamic scheduling method according to claim 1, characterized in that, Calculate the difference between the fast and slow ALMA moving averages and normalize it. The normalized difference between the fast and slow ALMA moving averages is the strength of the ALMA signal. ; like If the value is greater than 0, it indicates that it is time to perform the transmission; otherwise, it is not time to perform the transmission. or Introducing upper and lower thresholds and ,when Exceed And continuous If the cycle is maintained, it is determined when to execute the transmission; when In When dealing with intervals, it is necessary to combine the amount of data to be transmitted or channel momentum indicators for auxiliary judgment; if Below If it is not the right time to perform the transmission, then it is not the right time.

7. The data transmission dynamic scheduling method according to claim 1, characterized in that, Intensity mapping of ALMA signals For execution ratio Specifically: in, To achieve the maximum execution ratio, and These are the preset minimum and maximum thresholds, respectively.

8. An electronic device comprising a memory and a processor, characterized in that, The memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the data transmission dynamic scheduling method according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements a data transmission dynamic scheduling method as described in any one of claims 1-7.

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