Short packet-MEC network transmission optimization method based on information age and time delay

Abstract

The invention relates to the technical field of wireless communication, and discloses a short packet-MEC network transmission optimization method based on information age and time delay, the method comprises the following steps: initializing related parameters of a short packet-MEC network, the short packet-MEC network comprising a local device and a mobile edge server; generating state updating information P according to the state of the mobile edge server; determining a first moment of a state updating information time delay # imgabs0 # and a first moment of a state updating information calculation completion interval # imgabs1 # according to the state updating information P; determining the average time delay and the average peak information age of the state updating information P; establishing a multi-objective optimization function according to the average time delay and the average peak information age; and iterating the multi-objective optimization function, determining an optimal solution of the multi-objective optimization function, and determining optimal parameters of the short packet-MEC network according to the optimal solution of the multi-objective optimization function. According to the method, the multi-objective optimization function is established by considering the time delay and the information age to obtain the optimal solution, and the network performance can be improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and in particular, to a short-packet - MEC network transmission optimization method based on age of information and delay. Background Art

[0002] With the rapid development of Internet of Things technologies, various intelligent devices and applications emerge in an endless stream, leading to an explosive growth in computing demands. Due to its centralized computing mode, the traditional cloud computing architecture is difficult to meet the requirements of low latency, high bandwidth, and high reliability. Therefore, introducing computing capabilities into the network edge, namely mobile edge computing (MEC), has become an important way to address the growth of computing demands. By deploying computing, storage, and network resources at the network edge, MEC can be closer to users, reduce latency, and improve network efficiency and user experience.

[0003] As a low-latency information transfer method, short-packet communication plays a key role in real-time applications. However, from a perception perspective, simply ensuring latency is not sufficient to characterize the freshness of information. Therefore, the age of information (AoI) has been proposed as a timeliness metric, which can better reflect the freshness of information and has received increasing attention.

[0004] Currently, although studying the latency and AoI performance of short-packet MEC (SP-MEC) networks is an active research direction, existing studies have not fully explored their internal correlations. Summary of the Invention

[0005] Based on this, it is necessary to address the above problems and propose a short-packet - MEC network transmission optimization method based on age of information and delay.

[0006] A short-packet - MEC network transmission optimization method based on age of information and delay, the method includes the following steps: Initialize the relevant parameters of the short-packet - MEC network, where the short-packet - MEC network includes a local device and a mobile edge server; Generate status update information P according to the state of the mobile edge server; Determine the first moment of the delay of the status update information according to the status update information P and the first moment of the interval between the completion of the calculation of the status update information; ; ; Determine the average delay and average peak age of information of the status update information P; Establish a multi-objective optimization function according to the average delay and average peak age of information; Iterate the multi-objective optimization function to determine the optimal solution of the multi-objective optimization function; Determine the optimal parameters of the short-packet MEC network according to the optimal solution of the multi-objective optimization function.

[0007] In the above solution, when the mobile edge server is idle, the local device generates new status update information and processes the first part of the information to obtain the processed information. The local device offloads the processed information and the information other than the first part to the mobile edge server via the wireless communication channel. Characterize the overall decoding error probability of the transmission process using the finite blocklength domain theory. If the offloading of the status update information fails, the local device retransmits the processed information and the information other than the first part, and preset the maximum allowable number of retransmissions. 。

[0008] In the above solution, the relevant parameters for initializing the short-packet MEC network specifically include: status update information P, the size of the status update information , the offloading ratio of the update information , the static channel gain , the block length , the block error probability under the finite blocklength regime , the delay of the status update information , the interval between the completion of the calculation of the status update information , the calculation time of the local device , the calculation time of the mobile edge server , the number of CPU cycles required to process 1 nat task , the calculation frequency of the local device , the calculation frequency of the mobile edge server , the maximum allowable number of retransmissions , the iteration index 。

[0009] In the above solution, determining the first moment of the status update information delay and the first moment of the interval between the completion of the calculation of the status update information specifically includes: Determine the first moment of the status update information delay according to the following expression: where is the block transmission time, is the maximum number of transmissions, is the first moment of the status update information delay , is the calculation time of the local device. For the computing time of the mobile edge server, is the block error probability under the finite block length regime, is the transmission of status information The probability of failure in all Determine the completion interval of the status update information calculation according to the following expression The first moment of where, is the computing time of the local device, is the computing time of the mobile edge server, is the block transmission time, is the block error probability under the finite block length regime, is the completion interval of the status update information calculation The first moment of is the transmission of status information The probability of failure in all

[0010] In the above solution, determining the average delay and average peak information age of the status update information P specifically includes: Determine the average delay of the status update information P according to the following expression: where, is the computing time of the local device, is the computing time of the mobile edge server, is the block transmission time, is the block error probability under the finite block length regime, is the maximum number of transmissions, is the average delay of the status update information P, is the delay of the status update information The first moment of is the transmission of status information The probability of failure in all Determine the average peak information age of the status update information P according to the following expression: where, is the delay of the status update information The first moment of is the completion interval of the status update information calculation The first moment of is the computing time of the local device, is the computing time of the mobile edge server, is the block transmission time, is the maximum number of transmissions, is the block error probability under the finite block length regime, is the average peak age of information of the status update message P, is the probability that the status message transmission fails for all

[0011] In the above scheme, establishing the multi-objective optimization function according to the average delay and the average peak age of information specifically includes: The multi-objective optimization function is based on the block length , the maximum number of transmissions and the offloading ratio to minimize the weighted sum of the average peak age of information and the average delay: where, is the weight factor, and are the lower and upper bounds of the data packet length respectively, is the maximum allowed number of retransmissions, is the average delay of the status update message P, is the average peak age of information of the status update message P, is the multi-objective optimization function, s.t. indicates that the following expression is a constraint condition, C1 is the first constraint condition, C2 is the second constraint condition, and C3 is the third constraint condition, is the offloading ratio of the update message, is the maximum number of transmissions, is the block length, N + is the set of positive integers.

[0012] In the above scheme, iterating the multi-objective optimization function to determine the optimal solution of the multi-objective optimization function specifically includes: Converting the constraint variables into positive real variables; Dividing the multi-objective optimization function into several sub-problem functions; Based on the maximum number of transmissions iterating the several sub-problem functions to obtain the optimal parameter combination; Determining the parameter combination that minimizes the multi-objective optimization function in the optimal parameter combination as the optimal solution of the multi-objective optimization function.

[0013] In the above scheme, dividing the multi-objective optimization function into several sub-problems specifically includes: Based on the maximum number of transmissions and the offloading ratio of the update message , obtaining the first sub-problem function: wherein, is the first sub - problem function, s.t. C1 indicates that this constraint condition is the first constraint condition, is when a first concave function with respect to the block length, is when a second convex function with respect to the block length, is when a third convex function with respect to the block length, is the size of the state update information, is the update information offloading ratio, is the block transmission time, is the maximum number of transmissions, is the block error probability under the finite block - length regime, is the local device computing time, is the mobile edge server computing time, is the weight factor, is the probability that the state information transmission fails G times; Based on the maximum number of transmissions and the block length , the second sub - problem function is obtained: wherein, is the second sub - problem function, s.t. C2 indicates that this constraint condition is the second constraint condition, is when a first concave function with respect to the update information offloading ratio , is when a second convex function with respect to the update information offloading ratio, is the weight factor, is the block transmission time, is the block error probability under the finite block - length regime, is the maximum number of transmissions, is the size of the state update information, is the update information offloading ratio, is the number of CPU cycles required to process the 1nat task, is the local device computing frequency, For the computing frequency of the mobile edge server, For the transmission of status information The probability of failure in each attempt.

[0014] Adopting the embodiments of the present invention has the following beneficial effects: By initializing network parameters, generating update information based on the server status, determining relevant first moments, obtaining the average delay and peak age of information, constructing and iterating a multi-objective optimization function to obtain the optimal solution, and finally determining the optimal parameters of the short-packet - MEC network, the comprehensive optimization of network transmission in terms of information freshness (measured by the age of information) and transmission time (i.e., delay) is realized, effectively improving network performance and making the data transmission between the local device and the mobile edge server more efficient and the information more timely. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0016] Among them: Figure 1 It is a schematic flowchart of an optimization method for short-packet - MEC network transmission based on the age of information and delay in one embodiment; Figure 2 It is a system model diagram of the short-packet - MEC network in an example of the present invention; Figure 3 It is a schematic diagram of the change of the age of information over time; Figure 4 It is a schematic diagram of the functional relationship between the average AoI / PAoI and the average delay and the maximum allowable retransmission times; Figure 5 It is a schematic diagram of the relationship between the average PAoI and the average delay; Figure 6 It is a schematic diagram of the relationship between the local computing frequency and the weighted sum of the average delay and the average PAoI; Figure 7 It is a schematic diagram of the relationship between the edge computing frequency and the weighted sum of the average delay and the average PAoI. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0018] In the following description, a large number of specific details are given to provide a more thorough understanding of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some well-known technical features in the art are not described. It should be understood that the present invention can be implemented in different forms and should not be construed as limited to the embodiments presented here. The short-packet - MEC network mentioned in this application refers to a network architecture that combines short-packet transmission technology and Mobile Edge Computing (MEC). The following are the explanations of these two concepts: Short-packet transmission: It is a data transmission method, usually referring to small data packets transmitted in a network. Short-packet transmission is very important in fields such as real-time communication, Internet of Things (IoT), and mobile communication because these applications usually require fast and low-latency data transmission. Short data packets can reduce transmission latency and improve the real-time performance of communication.

[0019] Mobile Edge Computing (MEC): This is a network architecture that transfers computing resources from the cloud center to the edge of the network, that is, close to the data source. MEC aims to reduce the distance of data transmission, reduce latency, improve processing speed, and provide a better service experience for users. In the MEC environment, data processing and analysis are performed at the edge of the network rather than in a remote data center.

[0020] The short-packet - MEC network combining these two in this application can provide extremely low latency and higher bandwidth, thus supporting the high-speed transmission of a large number of short data packets.

[0021] To thoroughly understand the present invention, detailed structures will be presented in the following description to illustrate the technical solutions proposed by the present invention. The optional embodiments of the present invention are described in detail as follows. However, in addition to these detailed descriptions, the present invention can also have other implementation manners.

[0022] As Figure 1 shown, in one embodiment, a short-packet - MEC network transmission optimization method based on age of information and time delay is provided. The short-packet - MEC network transmission optimization method based on age of information and time delay includes steps S101 to S107, which are described in detail as follows: S101. Initialize the relevant parameters of the short-packet MEC network, where the short-packet MEC network includes a local device and a mobile edge server; This step sets the basic configuration of the network, which can ensure that the local device and the mobile edge server can start working under preset conditions and provide a basis for subsequent optimization.

[0023] In some embodiments, when the mobile edge server is idle, the local device generates new status update information and performs computational processing on the first part of the information to obtain the processed information; The local device offloads the processed information and the information other than the first part to the mobile edge server via a wireless communication channel; Use the finite blocklength domain theory to characterize the overall decoding error probability of the transmission process; If the offloading of the status update information fails, the local device retransmits the processed information and the information other than the first part, and preset the maximum allowable number of retransmissions .

[0024] Among them, the maximum allowable number of retransmissions is a limit on the maximum number of transmissions G, that is, the upper limit of the maximum number of transmissions G.

[0025] In this scheme, when the mobile edge server is idle, the local device can generate new status update information, process part of the information and then offload it and the remaining information to the server, and use the finite blocklength domain theory to characterize the overall decoding error probability of the transmission. If the offloading fails, the local device will retransmit according to the preset maximum number of times, so as to ensure the reliability and integrity of information transmission and improve the success rate and efficiency of the transmission of the status update information in the short-packet MEC network.

[0026] Specifically, as Figure 2 shown in the system model diagram of the short-packet MEC network in the embodiment of the present invention, it includes: a local device and a mobile edge server; when the mobile edge server is idle, the local device generates a data packet with a size of nats and divides it into two parts. The data with a size of is computationally processed on the local device, and the remaining data with a size of is encapsulated into a short data packet and offloaded to the mobile edge server via a wireless communication channel for further processing; use the finite blocklength domain theory to characterize the overall decoding error probability of the transmission process. When the decoding of the data packet is incorrect and the data packet cannot be offloaded to the mobile edge server, the local device will retransmit the data packet, and the maximum allowable number of retransmissions is .

[0027] In some embodiments, initializing the relevant parameters of the short-packet - MEC network specifically includes: status update information P, status update information size , update information offloading ratio , static channel gain , block length , block error probability under the finite block length regime , status update information delay , status update information calculation completion interval , local device calculation time , mobile edge server calculation time , number of central processing unit cycles required to process 1 nat task , local device calculation frequency , mobile edge server calculation frequency , maximum allowable number of retransmissions , iteration index .

[0028] During the information transmission process from the local device to the mobile edge device, the channel gain plays a key role, where the channel gain has the following expression: , where is the path loss constant, is the distance between the local device and the mobile edge server, is the path loss exponent, is the Rayleigh block fading coefficient with zero mean and unit variance, is the channel gain.

[0029] The approximate characterization of the overall decoding error probability of the transmission process using the finite code length domain theory is: where is the first coefficient, is the transmission power of the mobile edge server, is the noise power, is a constant, is the approximate characterization of the overall decoding error probability, is the block length.

[0030] Specifically, , , , is the size of the data packet encapsulated into a short data packet and offloaded to the mobile edge server for calculation, is the status update information size, is the update information offloading ratio, is the transmission power of the mobile edge server, is the noise power, is the block length, is the distance between the local device and the mobile edge server, is the path loss exponent, is the path loss constant.

[0031] When a decoding error occurs and the data packet cannot be unloaded to the mobile edge server, the local device will resend the data packet until the maximum allowed number of retransmissions is reached , if the unloading is still not successful at this time, the data packet will be discarded, and when the mobile edge server is idle, the local device generates a new data packet.

[0032] As Figure 3 shown is a schematic diagram of the age of information changing with time. The time when the local device generates the th data packet and the mobile edge server completes the process are and respectively. The calculation completion interval is: .

[0033] S102. Generate status update information P according to the status of the mobile edge server; By capturing the status changes of the mobile edge server in real time and generating corresponding status update information, it can provide data support for network adjustment.

[0034] S103. Determine the first moment of the status update information delay and the first moment of the status update information calculation completion interval ; By calculating the first moments of the delay and the calculation completion interval, quantifying the transmission and calculation efficiency of the status update information can provide metrics for evaluating network performance.

[0035] In some embodiments, determining the first moment of the status update information delay and the first moment of the status update information calculation completion interval specifically includes: Determine the first moment of the status update information delay according to the following expression: where, is the block transmission time, is the maximum number of transmissions, is the first moment of the status update information delay , Calculate the time for the local device, calculate the time for the mobile edge server, is the block error probability under the finite block length regime, is the probability that the state information transmission fails G times; Determine the completion interval of the state update information calculation according to the following expression of the first moment: where, is the time calculated by the local device, is the time calculated by the mobile edge server, is the block transmission time, is the block error probability under the finite block length regime, is the completion interval of the state update information calculation of the first moment, is the probability that the state information transmission fails G times.

[0036] Specifically, by using the expressions regarding the block transmission time, the maximum number of transmissions, the calculation times of the local device and the mobile edge server, and the block error probability, the first moment of the delay of the state update information can be accurately calculated, which helps to clearly grasp the time delay characteristics of the state update information during the transmission process. At the same time, according to another expression including factors such as the calculation times of the local device and the mobile edge server, the block transmission time, and the block error probability, the first moment of the completion interval of the state update information calculation can be determined, so as to have a quantitative understanding of the time characteristics of the completion interval of the state update information calculation. The determination of these two first moments provides important data support for in-depth analysis of network performance and optimization of the transmission mechanism.

[0037] S104. Determine the average delay and the average peak age of information of the state update information P; By calculating the average delay and the average peak age of information, the timeliness of network transmission and the freshness of information can be further evaluated, providing a quantitative basis for the optimization goal.

[0038] In some embodiments, determining the average delay and the average peak age of information of the state update information P specifically includes: Determine the average delay of the state update information P according to the following expression: where, is the time calculated by the local device, is the time calculated by the mobile edge server, is the block transmission time, is the block error probability under the finite block length regime, is the maximum number of transmissions, is the average delay of the state update information P, Status update information delay The first moment of Transmitting status information The probability of failure every time; The average peak information age of the state update information P is determined according to the following expression: in, Status update information delay The first moment of Calculate completion interval for status update information The first moment of Calculates time for the local device, Calculate time for mobile edge servers, is the block transfer time, is the maximum number of transmissions, is the block error probability under the finite block length system, is the average peak information age of the state update information P, Transmitting status information The probability of failure each time.

[0039] Specifically, the average delay and average peak information age of the status update information can be accurately calculated with the help of the given expressions. By combining factors such as the local device calculation time, the mobile edge server calculation time, the block transmission time, the block error probability under the finite block length system, and the maximum number of transmissions, the average delay of the status update information can be accurately determined, which allows us to grasp the average delay of the status update information during the transmission process as a whole. When calculating the average peak information age, not only the above factors are considered, but also the first-order moment of the status update information delay and the first-order moment of the status update information calculation completion interval are introduced, so as to measure the freshness of the information and understand the average aging degree of the information when it reaches the peak in the whole process. The determination of these two indicators provides a key basis for evaluating and optimizing the transmission performance of short packet-MEC networks, and helps to take more effective measures in practical applications to improve the real-time performance of the network and the effectiveness of information.

[0040] S105, establishing a multi-objective optimization function according to the average delay and the average peak information age; A multi-objective optimization function including average delay and average peak information age is constructed to quantify network performance and provide a mathematical model for finding the optimal solution.

[0041] In some embodiments, a multi-objective optimization function is established based on the average delay and the average peak information age, specifically including: The multi-objective optimization function is based on the block length , the maximum number of transmissions and the update information offloading ratio Minimize the weighted sum of the average peak age of information and the average delay: where, is the weight factor, and are the lower and upper bounds of the packet length respectively, is the maximum allowed number of retransmissions, is the average delay of the status update information P, is the average peak age of information of the status update information P, is the multi-objective optimization function, s.t. indicates that the following expression is a constraint condition, C1 is the first constraint condition, C2 is the second constraint condition, and C3 is the third constraint condition, is the update information offloading ratio, is the maximum number of transmissions, is the block length, N + is the set of positive integers.

[0042] where, .

[0043] The multi-objective optimization function constructed in this application is based on the block length, the maximum number of transmissions, and the update information offloading ratio, aiming to minimize the weighted sum of the average peak age of information and the average delay. By introducing a weight factor, this function can flexibly adjust the relative importance of the average peak age of information and the average delay in the optimization objective. At the same time, considering the upper and lower bounds of the packet length and the maximum number of transmissions and other limiting conditions, the optimization is more in line with the actual network scenario. The average delay and the average peak age of information of the status update information, as key components of the function, reflect the performance of the network in terms of information transmission time and information freshness. By solving and analyzing this multi-objective optimization function, it can provide guidance for the short-packet - MEC network in terms of parameter selection (such as block length, maximum number of transmissions, and update information offloading ratio), so as to optimize and improve the network performance under the comprehensive consideration of the age of information and delay, and help meet the requirements of application scenarios with high real-time and information timeliness requirements.

[0044] S106. Iterate the multi-objective optimization function to determine the optimal solution of the multi-objective optimization function; S107. Determine the optimal parameters of the short-packet - MEC network according to the optimal solution of the multi-objective optimization function.

[0045] By using an iterative algorithm for step - by - step search, a set of solutions that can achieve relatively optimal levels for both the average delay and the average peak age of information can be found, providing theoretical guidance for optimizing network performance and applying the theoretically optimal solution to the actual network, adjusting the short - packet - MEC network parameters, and realizing the transmission optimization of the network in terms of the age of information and delay.

[0046] In some embodiments, iterating on the multi - objective optimization function to determine the optimal solution of the multi - objective optimization function specifically includes: Converting the constraint variables into positive real variables; Dividing the multi - objective optimization function into several sub - problem functions; Based on the maximum number of transmissions Iterating on several sub - problem functions to obtain the optimal parameter combination; Determining the parameter combination that minimizes the multi - objective optimization function among the optimal parameter combinations as the optimal solution of the multi - objective optimization function.

[0047] Converting the constraint variables into positive real variables simplifies the nature of the variables, which can facilitate subsequent calculations and analyses, enabling the optimization process to be carried out in a more concise mathematical environment. Then, splitting the multi - objective optimization function into several sub - problem functions decomposes the complex multi - objective problem into relatively simple sub - problems in this way, reducing the difficulty of problem - solving. Next, based on the maximum number of transmissions, iterating on these sub - problem functions continuously adjusts the parameters during the iteration process, gradually approaching the optimal parameter combination. This process fully utilizes the key factor of the maximum number of transmissions to explore the optimal solution space of the function. Finally, among the obtained optimal parameter combinations, screening out the parameter combination that minimizes the value of the multi - objective optimization function and determining it as the optimal solution of the multi - objective optimization function. This optimal solution comprehensively considers the minimization of the weighted sum of the average peak age of information and the average delay, and can provide a practical parameter configuration scheme for optimizing the short - packet - MEC network, thereby effectively improving the comprehensive performance of the network in terms of the age of information and delay.

[0048] In some embodiments, dividing the multi - objective optimization function into several sub - problems specifically includes: Based on the maximum number of transmissions and the updated information offloading ratio , obtaining the first sub - problem function: where is the first sub - problem function, s.t. C1 indicates that this constraint condition is the first constraint condition, When is the first concave function with respect to the block length, When is the second convex function with respect to the block length, When is the third convex function with respect to the block length, is the size of the status update information, is the update information offloading ratio, is the block transmission time, is the maximum number of transmissions, is the block error probability under the finite block length regime, is the local device computing time, is the mobile edge server computing time, is the weight factor, is the probability that the status information transmission fails G times.

[0049] Among them, when , is the first concave function with respect to the block length, is the second convex function with respect to the block length, is the third convex function with respect to the block length. Therefore, the first sub - problem can be solved as follows: When , the sub - problem can be transformed into a convex difference programming problem, and the convex - concave process (CCP) can be used to solve this sub - problem. Among them, the core idea of CCP is to iteratively solve a series of convex surrogate problems, and each convex surrogate problem is constructed by linearizing the concave term. When , the optimal block length of this sub - problem can be found through a line search.

[0050] Based on the maximum number of transmissions and the block length , the second sub - problem function is obtained: Among them, is the second sub - problem function, s.t. C2 indicates that this constraint condition is the second constraint condition, When , is the first concave function with respect to the update information offloading ratio When is the second convex function with respect to the update information offloading ratio, is the weight factor, is the block transmission time, is the block error probability under the finite blocklength regime, is the maximum number of transmissions, is the size of the status update information, is the update information offloading ratio, is the number of CPU cycles required to process 1 nat task, is the local device computing frequency, is the mobile edge server computing frequency, is the probability that the transmission of the status information fails for all

[0051] where, when is is the first concave function with respect to the update information offloading ratio, is the second convex function with respect to the update information offloading ratio. Similar to the solution method of the first sub-problem, when is is

[0052] By decomposing the multi-objective optimization function into these two sub-problem functions as above, the originally complex multi-factor optimization problem is reasonably split, making the research on each factor more detailed and in-depth, providing a clearer idea and a more effective method for solving the optimal solution of the multi-objective optimization function subsequently, and helping to achieve more accurate performance optimization to meet the strict requirements of information age and latency in different application scenarios.

[0053] In some embodiments, by performing a one-dimensional search on the maximum number of transmissions the optimization problem can be iteratively solved, specifically: First, perform a one-dimensional search on the maximum number of transmissions initialize the iteration index r = 0, and the iteration range is from 1 to the maximum allowed retransmission times randomly generate an initial point where and represent the block length and the update information offloading ratio respectively; If then update and respectively using the linear search method for the first sub-problem function and the second sub-problem function, where is the size of the status update information, is the product of the r-th update information offloading ratio and the size of the status update information, is the pi, is the block length for the (r + 1)-th time, is the update information offloading ratio for the (r + 1)-th time; If , update the first sub-problem using the linear search method , update the second sub-problem using the concave-convex process , where is the state update information, is the product of the update information offloading ratio for the r-th time and the size of the state update information, is the pi, is the block length for the (r + 1)-th time, is the update information offloading ratio for the (r + 1)-th time; If , then update the first sub-problem and the second sub-problem respectively using the concave-convex process and , where is the size of the state update information, is the product of the update information offloading ratio for the r-th time and the size of the state update information, is the pi, is the block length for the (r + 1)-th time, is the update information offloading ratio for the (r + 1)-th time.

[0054] After each iteration is completed, update the iteration index to . When the convergence criterion is met, stop the iteration process under the current maximum transmission number value, and record the current maximum transmission number, the current block length and the current update information offloading ratio , and use the obtained current maximum transmission number, the current block length and the current update information offloading ratio to calculate the value of the objective function ; where is the weight factor, is the average peak age of information of the state update information P, is the average delay of the state update information P, is the objective function.

[0055] When all the maximum transmission number values are iterated, at this time, the objective function has been calculated for each maximum transmission number value. Select the first parameter combination that minimizes the objective function within this range as the preliminary optimal parameter combination.

[0056] After obtaining the first parameter combination , further in the block length Within a preset range, find the second parameter combination that minimizes the objective function to obtain the final optimal solution.

[0057] As Figure 4 describes the functional relationship between the average AoI / PAoI and the average delay and the maximum allowable number of retransmissions. The results show that the average AoI / PAoI can be reduced by increasing the maximum allowable number of retransmissions. However, the benefit brought by the age of information is offset by the loss of the average delay, thus demonstrating the necessity of achieving a delay trade-off. Among them, the average AoI (Average Age of Information) refers to the average value of the time from the generation of information to its reception by the user. Specifically, it is the expected value of the time difference between the arrival time of the information and the generation time of the information. AoI can be used to measure the freshness of information. The smaller the AoI, the fresher the information. The average PAoI (Peak Age of Information) refers to the average of the maximum value reached by AoI before the arrival of a new information update. It measures the worst-case delay between information updates.

[0058] Figure 5 describes the trade-off between the average PAoI and the average delay. It can be seen from the figure that the performance trade-off of the proposed algorithm is consistent with the Pareto front, and the Pareto optimality can be achieved.

[0059] Figure 6 studies the influence of the local computing frequency on the optimized weighted sum of the average delay and the average PAoI. The optimized results of the exhaustive search, the no-offloading scheme, and the offloading scheme with ARQ (Automatic Repeat Request) retransmission are benchmarked using the proposed algorithm. It can be obtained that the proposed algorithm achieves an optimality close to that of the exhaustive search. From Figure 6 we can see that in the case of insufficient local computing power, the local device tends to offload all updated information to the MES (Mobile Edge Computing Server). In this case, compared with non-offloading and ARQ offloading, the advantages of this application are very prominent. As the local computing frequency increases, the performance of the non-offloading scheme and the offloading scheme with ARQ is still poor, and this difference gradually disappears due to the contraction of the offloading demand. As the edge computing frequency increases, the weighted sum of other schemes except the non-offloading scheme can be reduced, as Figure 7 shown. The proposed scheme of this application is superior to other comparison baselines, thus clarifying the benefits of jointly considering the block length, the offloading ratio, and the maximum allowable number of retransmissions in the SP-MEC network.

[0060] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories.

[0061] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0062] The above-described embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several deformations and improvements can still be made, and these all belong to the protection scope of the present application. What is disclosed above is only the preferred embodiments of the present invention, and of course, the scope of the rights of the present invention cannot be limited thereby. Therefore, equivalent changes made according to the claims of the present invention still fall within the scope covered by the present invention.

Claims

1. A transmission optimization method for short-packet - MEC network based on age of information and time delay, characterized in that, The method includes: Initializing the relevant parameters of the short-packet MEC network, where the short-packet MEC network includes a local device and a mobile edge server; Generating status update information P according to the status of the mobile edge server; Determine the time delay of the status update information based on the status update information P and the first moment of the completion interval of the status update information calculation of the first moment; Determining the average delay and the average peak age of information of the status update information P; Establishing a multi-objective optimization function according to the average delay and the average peak age of information; Iterating the multi-objective optimization function to determine the optimal solution of the multi-objective optimization function; Determining the optimal parameters of the short-packet MEC network according to the optimal solution of the multi-objective optimization function.

2. The short packet - MEC network transmission optimization method based on information age and time delay according to claim 1, wherein, When the mobile edge server is idle, the local device generates new status update information and performs computational processing on the first part of the information to obtain the processed information; The local device unloads the processed information and the information other than the first part to the mobile edge server via a wireless communication channel; Characterizing the overall decoding error probability of the transmission process using the finite blocklength domain theory; If the unloading of the status update information fails, the local device re-transmits the processed information and the information other than the first part, and preset the maximum allowable number of re-transmissions .

3. The short packet - MEC network transmission optimization method based on information age and time delay according to claim 1, characterized in that, The relevant parameters for initializing the short packet-MEC network specifically include: status update information P, size of the status update information , offloading ratio of the update information , static channel gain , block length , block error probability under the finite block length regime , latency of the status update information , completion interval of the status update information calculation , local device calculation time , mobile edge server calculation time , number of CPU cycles required to process 1 nat task , local device calculation frequency , mobile edge server calculation frequency , maximum allowed number of retransmissions , iteration index .

4. The short packet-MEC network transmission optimization method based on age of information and time delay according to claim 3, characterized in that Determining the time delay of the status update information based on the status update information P The first moment of and the interval between the completion of calculations of the status update information The first moment, specifically including: Determine the time delay of the status update information according to the following expression The first moment of: Among them, is the block transfer time, is the maximum number of transmissions, is the first moment of the delay of the status update information ; is the local device calculation time, is the mobile edge server calculation time, is the block error probability under the finite block length regime, is the status information transmission times the probability of all failures; Determine the completion interval of the status update information calculation according to the following expression The first moment of: Among them, is the computing time for the local device, is the computing time for the mobile edge server, is the block transmission time, is the block error probability under the finite blocklength regime, is the completion interval of the state update information calculation of the first moment, is the state information transmission the probability of failure for all [[number of times]] times. It should be noted that there seems to be a missing specific number in " the probability of failure for all [[number of times]] times. ", which might affect the full understanding and accuracy of the translation. You may want to double-check and provide the complete and correct information if possible.

5. The short packet - MEC network transmission optimization method based on age of information and time delay according to claim 4, characterized in that, The determining the average delay and the average peak age of information of the status update information P specifically includes: Determining the average delay of the status update information P according to the following expression: wherein, is the computing time of the local device, is the computing time of the mobile edge server, is the block transmission time, is the block error probability under the finite block length regime, is the maximum number of transmissions, is the average delay of the status update information P, is the delay of the status update information of the first moment, is the probability that the status information transmission fails G times; Determining the average peak age of information of the status update information P according to the following expression: wherein, is the first moment of the time delay of the status update information ; is the first moment of the interval when the status update information calculation is completed ; is the local device calculation time is the mobile edge server calculation time is the block transmission time is the maximum number of transmissions is the block error probability under the finite block length regime is the average peak age of information of the status update information P is the probability that the status information transmission fails G times 6. The short-packet - MEC network transmission optimization method based on age of information and delay according to claim 5, characterized in that The establishing a multi-objective optimization function according to the average delay and the average peak age of information specifically includes: The multi-objective optimization function is based on the block length , the maximum number of transmissions and the update information offloading ratio to minimize the weighted sum of the average peak age of information and the average delay: Among them, is the weight factor, and are the lower and upper limits of the data packet length respectively, is the maximum allowable number of retransmissions, is the maximum number of transmissions, is the average delay of the status update information P, is the average peak information age of the status update information P, is the multi-objective optimization function, s.t. indicates that the following expression is a constraint condition, C1 is the first constraint condition, C2 is the second constraint condition, and C3 is the third constraint condition, is the update information offloading ratio, is the block length, N + is the set of positive integers.

7. The short packet-MEC network transmission optimization method based on age of information and time delay according to claim 6, wherein The iterating the multi-objective optimization function to determine the optimal solution of the multi-objective optimization function specifically includes: Converting the constraint variables into positive real variables; Dividing the multi-objective optimization function into several sub-problem functions; Based on the maximum number of transmissions Iterate on the several sub-problem functions to obtain an optimal parameter combination; Determining the parameter combination that minimizes the multi-objective optimization function among the optimal parameter combinations as the optimal solution of the multi-objective optimization function.

8. The short packet-MEC network transmission optimization method based on age of information and time delay according to claim 7, characterized in that The dividing the multi-objective optimization function into several sub-problems specifically includes: Based on the maximum number of transmissions and the update information offloading ratio , the first sub-problem function is obtained: Among them, is the first sub-problem function, s.t. C1 indicates that this constraint condition is the first constraint condition, is when a first concave function with respect to the block length, is when a second convex function with respect to the block length, is when a third convex function with respect to the block length, is the size of the status update information, is the update information offloading ratio, is the block transmission time, is the maximum number of transmissions, is the block error probability under the finite block length regime, is the local device computing time, is the mobile edge server computing time, is the weight factor, is the probability that the status information transmission fails G times; Based on the maximum number of transmissions and the block length , a second sub-problem function is obtained: Among them, is the second sub-problem function, where C2 indicates that this constraint condition is the second constraint condition. When , it is the first concave function with respect to the update information offloading ratio . When , it is the second convex function with respect to the update information offloading ratio. is the weight factor. is the block transfer time. is the block error probability under the finite block length regime. is the maximum number of transmission times. is the size of the state update information. is the update information offloading ratio. is the number of CPU cycles required to process 1 nat task. is the local device computing frequency. is the mobile edge server computing frequency. is the state information transmission times the probability of failure.

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