Task allocation method and device based on uplink OFDM mobile edge computing

Abstract

This application provides a task allocation method and apparatus based on uplink OFDM mobile edge computing. The method includes: constructing an information timeliness model for uplink OFDM mobile edge computing, wherein the information timeliness model is used to simulate the changing trend of a first freshness with a first target ratio, a first transmission rate, a first target bandwidth, and a second target ratio; constructing a target optimization model based on the information timeliness model and multiple preset constraints, wherein the first objective function of the target optimization model is to maximize the sum of the first freshness corresponding to each terminal device; solving the target optimization model through the MM iterative algorithm to obtain the control parameters of the MEC system, and controlling the MEC system to perform operations based on the control parameters. This application solves the problem of the lack of task allocation methods for edge computing that consider information timeliness in the prior art.

Description

Technical Field

[0001] This invention relates to the field of data communication technology, and more specifically, to a task allocation method, apparatus, computer-readable storage medium, processor, and mobile edge computing system based on uplink OFDM mobile edge computing. Background Technology

[0002] To address the mutual interference between signals in communication networks, transmission systems must find an effective transmission mechanism to suppress interference. Therefore, researchers proposed the concept of Orthogonal Frequency-Division Multiplexing (OFDM). OFDM is a multi-carrier modulation technique that divides channel resources into multiple orthogonal subcarriers and converts high-speed serial data into parallel low-speed sub-data streams, which are then distributed to each subcarrier for transmission. Because each subcarrier is orthogonal to the others, and the spectral nulls overlap with those of adjacent carriers, OFDM effectively reduces inter-carrier interference (ICI). Due to the partial overlap between carriers, OFDM has higher spectral efficiency compared to traditional communication methods.

[0003] In OFDM transmission, the high-speed data stream is converted from serial to parallel and distributed to lower-rate sub-channels for transmission. The symbol period in each sub-channel is relatively increased, effectively reducing inter-symbol interference (ISI) caused by multipath delay spread. Simultaneously, a guard interval is introduced, allowing OFDM to eliminate inter-symbol interference (ISI) to the maximum extent possible even with a multipath delay spread greater than the guard interval. If a cyclic prefix is ​​used as the guard interval, OFDM can also avoid inter-channel interference caused by multipath. OFDM uses multiple overlapping sub-bands that are orthogonal to each other, so the receiver can receive signals without spectrum separation. A major advantage of OFDM systems is that orthogonal subcarriers can be modulated and demodulated using inverse fast fourier transform (IFFT) and fast fourier transform (FFT). This method fills the OFDM symbol guard interval with a cyclic prefix, ensuring that the number of waveform periods of the delayed replicas of the OFDM symbol within the FFT period is an integer. Therefore, signals with delays less than the guard interval will not generate ISI during demodulation.

[0004] Mobile Edge Computing (MEC) is a network architecture concept designed to leverage wireless access networks to provide telecommunications users with the IT services and cloud computing capabilities they need, thereby creating a high-performance, low-latency, and high-bandwidth carrier-grade service environment. By deeply integrating traditional cellular networks and internet services, MEC significantly reduces end-to-end latency in mobile service delivery, thus enhancing user experience. The inherent capabilities of wireless networks have been successfully unlocked, a concept that not only brings about a complete transformation in the operating models of telecommunications operators but also promotes the establishment of new industry chains and network ecosystems.

[0005] Compared to cloud data centers, MEC servers, although smaller in scale, offer many significant advantages due to their deployment at the network edge:

[0006] High performance and low latency: MEC servers have more powerful computing and storage resources. When there are many service demands, they can greatly reduce the task computing time. For some latency-sensitive and computationally intensive applications, they can increase the possibility of completing the calculation within the maximum tolerable time, thereby improving service quality and user experience.

[0007] Energy saving and extended equipment life: By offloading some tasks to the MEC server, system energy consumption can be reduced, while also relieving pressure on mobile devices and extending battery life.

[0008] Data security and rapid response: MEC servers can provide cloud computing capabilities to users within their coverage area. Because they are closer to users, they have higher data security and can detect changes in user status more quickly.

[0009] Furthermore, the MEC architecture supports various optimization strategies, such as compute migration, edge caching, and service orchestration, to adapt to different application needs and scenarios. The combined use of these strategies can further improve network efficiency and quality of service.

[0010] In summary, combining uplink OFDM with mobile edge computing can improve the response speed and computation speed of computing tasks while reducing interference during transmission. However, existing technologies still lack a method for allocating computing tasks in uplink OFDM mobile edge computing systems with timeliness as the objective. Summary of the Invention

[0011] The main objective of this application is to provide a task allocation method, apparatus, computer-readable storage medium, processor, and mobile edge computing system based on uplink OFDM mobile edge computing, so as to at least solve the problem that there is a lack of task allocation methods for edge computing that take into account the timeliness of information in the prior art.

[0012] To achieve the above objectives, according to one aspect of this application, a task allocation method based on uplink OFDM mobile edge computing is provided. The MEC system includes a communication base station and multiple terminal devices. The method includes: constructing an information timeliness model for uplink OFDM mobile edge computing, wherein the information timeliness model is used to simulate the changing trend of a first freshness with a first target ratio, a first transmission rate, a first target bandwidth, and a second target ratio. The first freshness is the freshness of the computation result of the task to be computed; the first target ratio is the percentage of the task to be computed completed by online computation and local computation; the first transmission rate is the real-time signal transmission rate between the terminal devices and the communication base station; and the first target bandwidth is the data transmission rate from the terminal devices to the communication base station. The transmission spectrum bandwidth, the second target ratio is the computing power ratio of the base station processors allocated to the task to be computed; a target optimization model is constructed according to the information timeliness model and multiple preset constraints, the first objective function of the target optimization model is to maximize the sum of the first freshness corresponding to each terminal device, and the multiple preset constraints include the first target ratio of each terminal device being less than or equal to a first preset value, the sum of the first target bandwidth of each terminal device being less than or equal to a second preset value, and the sum of the second target ratio of each terminal device being less than or equal to a third preset value; the target optimization model is solved by the MM iterative algorithm to obtain the control parameters of the MEC system, and the MEC system is controlled to perform operations based on the control parameters.

[0013] Optionally, constructing an information timeliness model for uplink OFDM mobile edge computing includes: constructing a first target formula, which simulates the process of a first target signal being transmitted through an uplink channel and converted into a second target signal, wherein the first target signal is a signal sent by the terminal device to the communication base station, and the second target signal is a signal received by the communication base station; constructing a second target formula, which simulates the trend of the first target probability changing with the first transmission power, the first transmission rate, and the first target bandwidth, wherein the first target probability is the probability that the communication base station successfully receives the second target signal, and the first transmission power is the signal transmission power of the terminal device; constructing a third target formula, which simulates the trend of the second freshness changing with the first target probability, the first target proportion, the first transmission rate, and the second target proportion, wherein the second freshness is the freshness of the calculation result calculated online; constructing a fourth target formula, which simulates the trend of the third freshness changing with the first target proportion, wherein the third freshness is the freshness of the calculation result calculated locally; and constructing a fifth target formula, which determines the minimum value between the second freshness and the third freshness as the first freshness and outputs the first freshness.

[0014] Optionally, constructing the first target formula includes: acquiring the first target signal and the second target signal, and constructing the first target formula based on the first target signal and the second target signal. Among them, y k h is the second target signal. k The channel response of the uplink channel, p k x is the first transmission power. k Let n be the first target signal. k The uplink channel is the additive Gaussian noise.

[0015] Optionally, constructing a second target formula includes: obtaining a second transmission rate, a first target bandwidth, and a first transmission power; and constructing a sixth target formula based on the second transmission rate, the first target bandwidth, and the first transmission power. The sixth target formula characterizes the changing trend of the second transmission rate with respect to the first target bandwidth and the first transmission power. The second transmission rate is the achievable rate of the uplink channel: r k =w k ·log2(1+p k |h k | 2 ); where r k For the second transmission rate, w k For the first target bandwidth, pk h is the first transmission power. k The channel response of the uplink channel is determined; a first transmission rate and a preset condition are obtained; if the first transmission rate and the second transmission rate meet the preset condition, it is determined that the communication base station has successfully received the second target signal; the second target formula is constructed based on the preset condition, the first transmission rate, and the second transmission rate, wherein the preset condition is that the first transmission rate is less than or equal to the second transmission rate. Wherein, φ(v k ,w k Let v be the probability of the first target. k For the first transmission rate, γ k These are preset coefficients.

[0016] Optionally, constructing a third target formula includes: obtaining the second freshness, the first target probability, the first target ratio, the first transmission rate, and the second target ratio; and constructing the third target formula based on the second freshness, the first target probability, the first target ratio, the first transmission rate, and the second target ratio. in, For the second freshness, λ k M is the update interval for the task to be computed. k For the task to be computed, v k For the first transmission rate, φ(v) k ,w k Let ) be the probability of the first target, and a k β is the first target ratio. k C is the second target ratio. B This refers to the computational processing speed of the communication base station.

[0017] Optionally, constructing a fourth target formula includes: obtaining the third freshness and the first target ratio, and constructing the fourth target formula based on the third freshness and the first target ratio. in, For the third freshness, a k λ is the first target ratio. k M is the update interval for the task to be computed. k For the task to be computed, C k This refers to the computing and processing speed of the terminal device.

[0018] Optionally, the target optimization model is solved using the MM iterative algorithm to obtain the control parameters of the MEC system, including: a first optimization step, setting the second target ratio, the first target bandwidth, and the first transmission rate to be greater than 0 and unchanged as the first target condition; based on the constraint of the first target condition, the solution of the first objective function is transformed into solving for the first target ratio corresponding to the maximum value of the sum of the first freshnesses, to obtain the second objective function; the difference between the second freshness and the third freshness is determined as the sixth objective formula; based on the sixth objective formula, the closed-form solution of the second objective function is solved to obtain the alternative control parameters corresponding to the first target ratio; a second optimization step, setting the first target ratio, the second target ratio, and the first target bandwidth to be greater than 0 and unchanged as the second target condition; based on the constraint of the second target condition, the solution of the first objective function is transformed into solving for the first transmission rate corresponding to the maximum value of the sum of the first freshnesses, to obtain the third objective function; the third objective function is solved using the bisection method to obtain the alternative control parameters corresponding to the first transmission rate; a third optimization step, setting the first target ratio, the first target bandwidth, and the first transmission rate to be greater than 0 and unchanged. The process involves several steps: First, the first objective ratio is transformed into a third objective condition. Based on this third objective condition, a correlation relationship is constructed between two adjacent iterations of the second objective ratio, resulting in a seventh objective formula. Then, the first objective function is equivalently transformed based on the seventh objective formula and the third objective condition, resulting in a fourth objective function. A closed-form solution is obtained based on the fourth objective function to obtain the candidate control parameters corresponding to the second objective ratio. In the fourth optimization step, the first objective ratio, the second objective ratio, and the first transmission rate are set to be greater than 0 and remain unchanged as the fourth objective condition. Based on this fourth objective condition, a correlation relationship is constructed between two adjacent iterations of the first objective bandwidth, resulting in an eighth objective formula. Based on this eighth objective formula and the fourth objective condition, the first objective function is equivalently transformed to obtain a fifth objective function. A closed-form solution is obtained based on the fifth objective function to obtain the candidate control parameters corresponding to the first objective bandwidth. The first, second, third, and fourth optimization steps are repeated at least once until the error between two adjacent iterations is less than a preset value. Finally, the candidate control parameters corresponding to the first objective ratio, the first transmission rate, the first objective bandwidth, and the second objective ratio are determined as the control parameters.

[0019] Another aspect of this application provides a task allocation device based on uplink OFDM mobile edge computing. The MEC system includes a communication base station and multiple terminal devices. The device includes: a first construction unit for constructing an information timeliness model for uplink OFDM mobile edge computing. The information timeliness model is used to simulate the changing trend of a first freshness with a first target ratio, a first transmission rate, a first target bandwidth, and a second target ratio. The first freshness is the freshness of the computation result of the task to be computed. The first target ratio is the percentage of the task to be computed completed by online computation and local computation. The first transmission rate is the real-time signal transmission rate between the terminal devices and the communication base station. The first target bandwidth is the spectral bandwidth for data transmission from the terminal devices to the communication base station. The second target ratio is the computing power ratio of the base station processors to which the task to be computed is allocated; the second construction unit is used to construct a target optimization model based on the information timeliness model and multiple preset constraints, wherein the first objective function of the target optimization model is to maximize the sum of the first freshness corresponding to each terminal device, and the multiple preset constraints include the first target ratio of each terminal device being less than or equal to a first preset value, the sum of the first target bandwidth of each terminal device being less than or equal to a second preset value, and the sum of the second target ratio of each terminal device being less than or equal to a third preset value; the calculation unit is used to solve the target optimization model through the MM iterative algorithm to obtain the control parameters of the MEC system, and control the MEC system to perform calculations based on the control parameters.

[0020] In another aspect, this application provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform any of the methods described.

[0021] In another aspect, this application provides a mobile edge computing system comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including methods for performing any one of the methods described.

[0022] Applying the technical solution of this application, in the above-mentioned task allocation method based on uplink OFDM mobile edge computing, firstly, an information timeliness model for uplink OFDM mobile edge computing is constructed. This information timeliness model is used to simulate the changing trend of a first freshness with respect to a first target ratio, a first transmission rate, a first target bandwidth, and a second target ratio. The first freshness is the freshness of the computation result of the task to be computed; the first target ratio is the percentage of the task to be computed completed by online computation and local computation; the first transmission rate is the real-time signal transmission rate between the terminal device and the communication base station; the first target bandwidth is the spectral bandwidth for data transmission from the terminal device to the communication base station; and the second target ratio is... The computing power ratio of the base station processors allocated to the aforementioned tasks to be computed is determined. Then, a target optimization model is constructed based on the aforementioned information timeliness model and multiple preset constraints. The first objective function of the target optimization model is to maximize the sum of the aforementioned first freshness corresponding to each of the aforementioned terminal devices. The multiple preset constraints include the aforementioned first target ratio of each of the aforementioned terminal devices being less than or equal to a first preset value, the sum of the aforementioned first target bandwidth of each of the aforementioned terminal devices being less than or equal to a second preset value, and the sum of the aforementioned second target ratio of each of the aforementioned terminal devices being less than or equal to a third preset value. Subsequently, the target optimization model is solved using the MM iterative algorithm to obtain the control parameters of the aforementioned MEC system, and the aforementioned MEC system is controlled to perform calculations based on the aforementioned control parameters.

[0023] This application addresses the challenge of evaluating information freshness in existing edge computing processes, where task execution involves both local and online computation. It establishes an information timeliness model for uplink OFDM mobile edge computing, jointly optimizing the correlation between user task allocation ratios, transmission rates, transmission spectrum resources, base station edge computing resource allocation, and information freshness. The objective function is optimized to achieve the best information timeliness, and this objective function is decomposed into sub-problems for solution. This yields the optimal parameter settings for edge computing, achieving the best information timeliness. This method solves the problem of existing edge computing task allocation methods that lack consideration for information timeliness. Attached Figure Description

[0024] Figure 1 A hardware structure block diagram of a mobile terminal based on a task allocation method for uplink OFDM mobile edge computing provided in an embodiment of this application is shown.

[0025] Figure 2 A flowchart illustrating a task allocation method based on uplink OFDM mobile edge computing according to an embodiment of this application is shown.

[0026] Figure 3A structural block diagram of a task allocation device based on uplink OFDM mobile edge computing provided according to an embodiment of this application is shown.

[0027] The above figures include the following reference numerals: 102, processor; 104, memory; 106, transmission device; 108, input / output device. Detailed Implementation

[0028] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0029] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] Definitions:

[0032] OFDM (Orthogonal Frequency Division Multiplexing) is a multi-carrier modulation technique used in wireless and wired communications. It divides a high-speed data stream into multiple low-speed data streams, which are then transmitted on different frequencies to improve channel utilization and interference resistance. OFDM technology is widely used in Wi-Fi, 4G / 5G mobile communications, digital broadcasting, and other fields.

[0033] MEC (Mobile Edge Computing) is a technology that deploys computing, storage, and networking functions on network edge nodes (such as base stations and routers). By deploying computing resources close to the network edge, MEC systems can achieve lower latency and higher bandwidth utilization, thereby improving network performance and user experience. MEC systems are commonly used to support various application scenarios, including the Internet of Things (IoT), smart cities, and virtual reality.

[0034] As described in the background section, existing technologies that combine uplink OFDM with mobile edge computing can improve the response speed and computation speed of computing tasks while reducing interference during transmission. However, existing technologies still lack a method for allocating computing tasks to the uplink OFDM mobile edge computing system with timeliness as the objective. To address the problem of the lack of a task allocation method for edge computing that considers the timeliness of information in existing technologies, embodiments of this application provide a task allocation method, apparatus, computer-readable storage medium, processor, and mobile edge computing system based on uplink OFDM mobile edge computing.

[0035] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0036] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal based on a task allocation method for uplink OFDM mobile edge computing, according to an embodiment of this application. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0037] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the device information display method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thus implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0038] This embodiment provides a task allocation method based on uplink OFDM mobile edge computing that runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than that shown here.

[0039] Figure 2 This is a flowchart of a task allocation method based on uplink OFDM mobile edge computing according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0040] Step S201: Construct an information timeliness model for uplink OFDM mobile edge computing. The information timeliness model is used to simulate the changing trend of the first freshness with the first target ratio, the first transmission rate, the first target bandwidth, and the second target ratio. The first freshness is the freshness of the calculation result of the task to be calculated. The first target ratio is the percentage of the task to be calculated completed by online computing and local computing. The first transmission rate is the real-time signal transmission rate between the terminal device and the communication base station. The first target bandwidth is the spectrum bandwidth for data transmission from the terminal device to the communication base station. The second target ratio is the computing power ratio of the base station processor to which the task to be calculated is allocated.

[0041] Specifically, the wireless uplink MEC system deployment consists of one communication base station and k single-antenna MEC user terminals, i.e., the aforementioned terminal equipment. This MEC system is an edge computing system, and the communication method is OFDM communication.

[0042] In practical implementation, the aforementioned communication base station is connected to the core network and a computing server is deployed to provide online computing resources for MEC user terminals. Let the first freshness be F. k First target ratio a k First transmission rate v k The first target bandwidth w k Second target proportion β k The first target ratio, the first transmission rate, the first target bandwidth, and the second target ratio are determined as input parameters, and the first freshness is determined as the output parameter. A corresponding simulation model is constructed to output the change of the input parameters, that is, to simulate the freshness of the user's computing task under different online and offline computing scheduling allocations.

[0043] Step S202: Construct a target optimization model based on the above information timeliness model and multiple preset constraints. The first objective function of the target optimization model is to maximize the sum of the first freshness corresponding to each of the above terminal devices. The multiple preset constraints include the first target ratio of each of the above terminal devices being less than or equal to a first preset value, the sum of the first target bandwidth of each of the above terminal devices being less than or equal to a second preset value, and the sum of the second target ratio of each of the above terminal devices being less than or equal to a third preset value.

[0044] Specifically, this application sets an optimization objective based on the aforementioned information timeliness model, namely, maximizing the freshness of the overall computing task, and constructs the aforementioned objective optimization model.

[0045] Step S203: Solve the above target optimization model using the MM iterative algorithm to obtain the control parameters of the above MEC system, and control the above MEC system to perform operations based on the above control parameters.

[0046] Specifically, based on the MM iterative algorithm, the above-mentioned target optimization model is solved under the condition of satisfying the above constraints to obtain the control parameters of the above-mentioned MEC system, including the first target ratio, the first transmission rate, the first target bandwidth and the second target ratio. Then, the MEC system is controlled based on the input parameters of the above-mentioned information timeliness model to ensure the real-time information timeliness of the system.

[0047] In this embodiment, firstly, an information timeliness model for uplink OFDM mobile edge computing is constructed. This model simulates the changing trend of a first freshness with respect to a first target ratio, a first transmission rate, a first target bandwidth, and a second target ratio. The first freshness refers to the freshness of the computation result of the task to be computed. The first target ratio is the percentage of the task to be computed completed by online computation and local computation. The first transmission rate is the real-time signal transmission rate between the terminal device and the communication base station. The first target bandwidth is the spectrum bandwidth for data transmission from the terminal device to the communication base station. The second target ratio is the base station processing capacity allocated to the task to be computed. The computing power ratio of the devices is determined; then, based on the aforementioned information timeliness model and multiple preset constraints, a target optimization model is constructed. The first objective function of the target optimization model is to maximize the sum of the first freshness corresponding to each of the aforementioned terminal devices. The multiple preset constraints include the first target ratio of each of the aforementioned terminal devices being less than or equal to a first preset value, the sum of the first target bandwidth of each of the aforementioned terminal devices being less than or equal to a second preset value, and the sum of the second target ratio of each of the aforementioned terminal devices being less than or equal to a third preset value. Subsequently, the target optimization model is solved using the MM iterative algorithm to obtain the control parameters of the aforementioned MEC system, and the aforementioned MEC system is controlled to perform calculations based on the aforementioned control parameters. This application addresses the challenge of evaluating information freshness in existing edge computing processes, where task execution involves both local and online computation. It establishes an information timeliness model for uplink OFDM mobile edge computing, jointly optimizing the correlation between user task allocation ratio, transmission rate, transmission spectrum resources, base station edge computing resource allocation, and information freshness. The objective function is optimized to achieve the best information timeliness, and this objective function is decomposed into sub-problems for solution. This yields the optimal parameter settings for edge computing, achieving the best information timeliness. This method solves the problem of existing edge computing task allocation methods that lack consideration for information timeliness.

[0048] To construct the aforementioned information timeliness model, in one optional implementation, step S201 includes:

[0049] Step S2011: Construct a first target formula. The first target formula is used to simulate the process of the first target signal being transmitted through the uplink channel and converted into a second target signal. The first target signal is the signal sent by the terminal device to the communication base station, and the second target signal is the signal received by the communication base station.

[0050] Specifically, based on the signal encoding of the computational task by the terminal device during data transmission and the channel interference during transmission, the above-mentioned first objective formula is constructed to characterize the changes in the signal during data transmission.

[0051] It is easy to understand that in the actual process of performing computing tasks, the computing can be divided into three types according to the ratio of online computing to local computing tasks. The first type is completed entirely by online computing, the second type is completed by a combination of online computing and local computing according to the first target ratio mentioned above, and the third type is completed entirely by local computing.

[0052] Step S2012: Construct a second target formula. The second target formula is used to simulate the changing trend of the first target probability with the first transmission power, the first transmission rate and the first target bandwidth. The first target probability is the probability that the communication base station successfully receives the second target signal. The first transmission power is the signal transmission power of the terminal device.

[0053] Specifically, based on the above three calculation methods, it can be seen that when the allocation method is the third calculation method, the success rate of the calculation depends on the probability of local calculation success. The first and second calculation methods require that the task to be calculated be successfully transmitted and that both online and local calculations are successful. Therefore, based on the above derivation, the first calculation method is taken as a special case of the second calculation method. The relationship between the calculation success rate and the second transmission rate, the first transmission power, the first transmission rate and the first target bandwidth are fitted to construct the above second target formula.

[0054] Step S2013: Construct a third target formula. The third target formula is used to simulate the changing trend of the second freshness with the first target probability, the first target proportion, the first transmission rate and the second target proportion. The second freshness is the freshness of the calculation result calculated online.

[0055] Specifically, based on the first target probability mentioned above, the calculation results are divided into the following scenarios: successful transmission, successful transmission but failed calculation, and both transmission and calculation failed. Based on the above scenarios, the freshness of the task to be calculated is fitted, and the third target formula mentioned above is constructed to simulate the correlation between the second freshness and the first target probability, the first target ratio, the first transmission rate, and the second target ratio.

[0056] Step S2014: Construct a fourth objective formula. The fourth objective formula is used to simulate the changing trend of the third freshness with the proportion of the first objective. The third freshness is the freshness of the calculation result calculated locally.

[0057] Specifically, based on local computation, the computation results are divided into the following scenarios: computation success and computation failure. Then, based on the above scenarios, the freshness of the computation task is fitted to construct the above fourth objective formula, which is used to simulate the correlation between the third freshness and the above first objective ratio.

[0058] Step S2015: Construct a fifth objective formula, which is used to determine the minimum value between the second freshness and the third freshness as the first freshness, and output the first freshness.

[0059] Specifically, in hybrid computing, when both online and local computations are completed, the computational task is finally computed, and then the formula is constructed. The minimum value between the second freshness and the third freshness mentioned above is taken as the first freshness, where F k For the aforementioned first freshness, For the second freshness mentioned above, This is the third level of freshness mentioned above.

[0060] In order to construct the first target formula described above, in one optional implementation, step S2011 includes:

[0061] Step S20111: Obtain the first target signal and the second target signal, and construct the first target formula based on the first target signal and the second target signal.

[0062]

[0063] Among them, y k h is the second target signal. k The channel response of the uplink channel, p k x is the first transmission power. k Let n be the first target signal. k The uplink channel is the additive Gaussian noise.

[0064] Specifically, when the user-selected computation method involves online computation, the terminal device transmits the computation task to the base station via wireless channel bandwidth W. To avoid interference between users, the network adopts an OFDM transmission scheme, i.e., the aforementioned first target bandwidth W... k The signal is assigned to terminal device k for wireless transmission. Therefore, the signal received by the base station based on the uplink channel can be represented as shown in the above formula.

[0065] In specific implementations, the uplink channel response follows a Rayleigh distribution, and the ambient noise of the uplink channel is assumed to be additive white Gaussian noise. To ensure generality, this application sets the noise variance of the aforementioned noise to 1.

[0066] In order to construct the second objective formula described above, in one optional implementation, step S2012 includes:

[0067] Step S20121: Obtain the second transmission rate, the first target bandwidth, and the first transmission power; construct a sixth target formula based on the second transmission rate, the first target bandwidth, and the first transmission power. The sixth target formula is used to characterize the changing trend of the second transmission rate with the first target bandwidth and the first transmission power. The second transmission rate is the achievable rate of the uplink channel.

[0068] r k =w k ·log2(1+p k |h k 2 );

[0069] Where, r k For the second transmission rate, w k For the first target bandwidth, p k h is the first transmission power. k This refers to the channel response of the uplink channel;

[0070] Specifically, based on the aforementioned uplink channel modeling, the transmission rate of terminal device k in this application can be expressed as r. k That is, the second transmission rate mentioned above, the specific expression of which is as shown in the above formula, which is the achievable transmission rate of terminal device k.

[0071] Step S20122: Obtain a first transmission rate and a preset condition. If the first transmission rate and the second transmission rate meet the preset condition, determine that the communication base station has successfully received the second target signal. Construct the second target formula based on the preset condition, the first transmission rate, and the second transmission rate. The preset condition is that the first transmission rate is less than or equal to the second transmission rate.

[0072]

[0073] Wherein, φ(v k ,w k Let v be the probability of the first target. k For the first transmission rate, γ k These are preset coefficients.

[0074] It is easy to understand that when the achievable transmission rate is greater than or equal to the actual transmission rate, the uplink channel can meet the transmission requirements of the task to be calculated. That is, when the first transmission rate is less than or equal to the second transmission rate, the transmission of the task to be calculated is determined to be successful, and when the first transmission rate is greater than the second transmission rate, the transmission of the task to be calculated is determined to be interrupted.

[0075] Furthermore, set v k ≤r k =w k ·log2(1+p k |h k | 2 If the transmission is successful, further processing yields the following results: Based on this, due to |h k | 2 For parameter γ k If the vector is an exponentially distributed random vector, then the transmission success rate of terminal device k is expressed as φ(v). k ,w k ), φ(v k ,w k The specific modeling is as shown in the above formula.

[0076] In order to construct the aforementioned third objective formula, in one optional implementation, step S2013 includes:

[0077] Step S20131: Obtain the second freshness, the first target probability, the first target ratio, the first transmission rate, and the second target ratio; and construct the third target formula based on the second freshness, the first target probability, the first target ratio, the first transmission rate, and the second target ratio.

[0078]

[0079] in, For the second freshness mentioned above, λ k M represents the update interval for the aforementioned task to be calculated. k For the above-mentioned task to be calculated, v k For the first transmission rate mentioned above, φ(v) k,w k Let ) represent the probability of the first objective mentioned above, and a k For the aforementioned first target ratio, β k For the aforementioned second target ratio, C B This refers to the computational processing speed of the aforementioned communication base station.

[0080] Specifically, the size of the task to be computed is set to M. k Therefore, we define task M. k Information freshness is:

[0081]

[0082] Therefore, the long-term average information freshness of the task to be calculated can be expressed as:

[0083]

[0084] Where T is the duration, and N is further defined as the number of update cycles within the duration T. The total duration of the nth calculation task is I. k (n), task M to be computed k The time from completion to the next update is T. k (n), then the average freshness is expressed as Among them, E[I k ] = E[W k ]+E[T k ], E[I k [E[T] represents the average time interval.] k [E[W] represents the average freshness time.] k [This represents the average waiting time.]

[0085] Based on the above formula, for terminal device k, online computing task a k M k The nth update cycle I k (n) There are three possible scenarios: both transmission and computation are completed, transmission is completed but computation is not completed, and neither transmission nor computation is completed. The probabilities of these scenarios are expressed as follows:

[0086]

[0087]

[0088]

[0089] Based on the above probabilities, calculate the corresponding online calculations. and The corresponding formula is:

[0090]

[0091]

[0092]

[0093] According to the definition, the second freshness mentioned above is expressed as:

[0094]

[0095] In order to construct the fourth objective formula described above, in one optional implementation, step S2014 includes:

[0096] Step S20141: Obtain the aforementioned third freshness and the aforementioned first target ratio, and construct the aforementioned fourth target formula based on the aforementioned third freshness and the aforementioned first target ratio:

[0097]

[0098] in, For the third freshness mentioned above, a k For the aforementioned first target ratio, λ k M represents the update interval for the aforementioned task to be calculated. k For the above-mentioned task to be calculated, C k This refers to the computing and processing speed of the aforementioned terminal devices.

[0099] Specifically, based on the above formula, for terminal device k, the local computing task (1-a) k M k There are two scenarios: the terminal completes the calculation, and the terminal does not complete the calculation. The probabilities of these two scenarios are as follows:

[0100]

[0101]

[0102] Therefore, the corresponding local calculation and The corresponding formula is:

[0103]

[0104]

[0105]

[0106] Therefore, according to the definition, the third freshness mentioned above is expressed as:

[0107]

[0108] In the above embodiments, This holds true in purely online computing, purely local computing, and hybrid computing; the only difference is that when a... k If the value is 1, then it represents a purely online environment; calculate... F is 1. k for on the contrary, F is 1. k for Therefore, the above-mentioned third objective formula, the above-mentioned fourth objective formula, and the formula It applies to all three calculation methods.

[0109] Furthermore, the MEC system needs to maximize the total information freshness of k users, that is, the maximum of the first information freshness mentioned above, which can be controlled by adjusting the first target bandwidth, the first transmission rate, the first target ratio, and the second target ratio.

[0110] Therefore, the above objective optimization model can be expressed as, where K is the set of terminal devices:

[0111]

[0112]

[0113]

[0114]

[0115] In order to solve for the control parameters based on the above objective function, in an optional implementation, step S203 includes:

[0116] Step S2031, the first optimization step, sets the above-mentioned second target ratio, the above-mentioned first target bandwidth and the above-mentioned first transmission rate to be greater than 0 and remain unchanged as the first target conditions. Based on the constraints of the above-mentioned first target conditions, the solution of the above-mentioned first objective function is transformed into the solution of the above-mentioned first freshness to obtain the first target ratio corresponding to the maximum value of the sum of the above-mentioned first freshness, and the second objective function is obtained. The difference between the above-mentioned second freshness and the above-mentioned third freshness is determined as the sixth objective formula. Based on the above-mentioned sixth objective formula, the closed-form solution of the above-mentioned second objective function is solved to obtain the alternative control parameters corresponding to the first target ratio.

[0117] Specifically, firstly, the aforementioned first target ratio is optimized. The aforementioned second target ratio, the aforementioned first target bandwidth, and the aforementioned first transmission rate are all greater than 0 and defined as arbitrary values ​​(the deviation of the initial defined values ​​can be eliminated through iteration during the actual iteration process). Then, the optimization of the aforementioned first target ratio is transformed as follows:

[0118]

[0119] stα k ∈[0,1].

[0120] Based on the modeling formulas for second and third freshness, it can be seen that the aforementioned first target ratio only depends on the denominator, and and All are greater than 0, set Based on this, the optimization problem can be rewritten as the second objective function mentioned above:

[0121]

[0122] stα k ∈[0,1].

[0123] Furthermore, construct the aforementioned sixth objective formula. Since the second objective function is a convex problem with respect to the proportion of the first objective, It is an increasing function. Since the function is decreasing, the sixth objective formula is an increasing function of the proportion of the first objective, and its closed-form solution is:

[0124]

[0125] Among them, a` k It can be determined using the dichotomy method. This is the closed-form solution mentioned above.

[0126] Step S2032, the second optimization step, sets the first target ratio, the second target ratio, and the first target bandwidth to be greater than 0 and remain unchanged as the second target conditions. Based on the constraints of the second target conditions, the solution of the first objective function is transformed into the solution of the first transmission rate corresponding to the maximum value of the sum of the first freshness, and a third objective function is obtained. The third objective function is solved based on the bisection method to obtain the alternative control parameters corresponding to the first transmission rate.

[0127] Specifically, given the optimized first objective ratio, the second objective ratio, and the first objective bandwidth, it is easy to understand that the third objective function is a logarithmically concave problem with respect to the first transmission rate, which has an optimal point and can be optimized using a bisection method.

[0128] Step S2033, the third optimization step, sets the first target ratio, the first target bandwidth, and the first transmission rate to be greater than 0 and remain unchanged as the third target conditions, constructs the correlation relationship between the second target ratio between two adjacent iterations based on the third target conditions, and obtains the seventh target formula, performs an equivalent transformation on the first target function based on the seventh target formula and the third target conditions, and obtains the fourth target function, solves the corresponding closed-form solution based on the fourth target function, and obtains the above-mentioned alternative control parameters corresponding to the second target ratio;

[0129] Specifically, based on the optimized first target ratio, the first transmission rate, and the first target bandwidth, when the first target ratio is greater than 0, i.e., online computation exists, the base station needs to allocate computing resources β. k C B Therefore, the first objective function mentioned above can be rewritten as follows:

[0130]

[0131]

[0132] The fourth objective function mentioned above takes the value of 0 when the first objective ratio is 0. When the first objective ratio is greater than 0, it can be seen that the solution for the second freshness will include the second objective ratio. Taking the first derivative of the second objective ratio with respect to the second freshness, it can be seen that it is a monotonically increasing function. Therefore, optimization through the MM iterative algorithm requires rewriting it using a substitution function and then iterating. The substitution function needs to satisfy the following constraints:

[0133]

[0134]

[0135]

[0136]

[0137] Define, any make The substitution function is:

[0138]

[0139] in,

[0140] For about β k The monotonically increasing concave function, further rewritten to address the above optimization problem, yields the fourth objective function:

[0141]

[0142]

[0143] The fourth objective function described above is a convex problem. Based on the Karush-Kuhn-Tucker (KKT) conditions, the Langran function can be expressed as:

[0144]

[0145] Find L with respect to β k The first derivative is equal to 0, and using the KKT conditions, β k The closed expression can be represented as:

[0146]

[0147] In the above embodiments, This is the optimized value from the previous round of optimization. In the above formula, only η is unknown, based on the water injection algorithm satisfying... After multiple iterations, when the substitution function approaches the original function, β k It converges and obtains the optimal value.

[0148] Step S2034, the fourth optimization step, sets the first target ratio, the second target ratio, and the first transmission rate to be greater than 0 and remain unchanged as the fourth target condition, constructs the correlation relationship between the first target bandwidth between two adjacent iterations based on the fourth target condition, and obtains the eighth target formula, performs an equivalent transformation on the first target function based on the eighth target formula and the fourth target condition, and obtains the fifth target function, solves the corresponding closed-form solution based on the fifth target function, and obtains the above-mentioned alternative control parameters corresponding to the first target bandwidth;

[0149] Specifically, similarly, the first target bandwidth mentioned above is optimized, and defined as follows: The replacement function is rewritten as follows:

[0150]

[0151] For about w k A monotonically increasing concave function;

[0152] The optimization problem above can be further rewritten to obtain the fifth objective function:

[0153]

[0154]

[0155] in,

[0156] The fifth objective function described above is a convex problem. Based on the Karush-Kuhn-Tucker (KKT) conditions, the Langran function can be expressed as:

[0157]

[0158] Seeking L regarding w k The first derivative is equal to 0, and using the KKT conditions, w k The closed expression can be represented as:

[0159]

[0160] Step S2035: Repeat the first optimization step, the second optimization step, the third optimization step and the fourth optimization step at least once in sequence until the error between two adjacent iterations is less than a preset value, and determine the candidate control parameters corresponding to the first target ratio, the first transmission rate, the first target bandwidth and the second target ratio as the control parameters.

[0161] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0162] This application also provides a task allocation device based on uplink OFDM mobile edge computing. It should be noted that the task allocation device based on uplink OFDM mobile edge computing in this application can be used to execute the task allocation method for uplink OFDM mobile edge computing provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0163] The following describes the task allocation device based on uplink OFDM mobile edge computing provided in the embodiments of this application.

[0164] Figure 3 This is a structural block diagram of a task allocation device based on uplink OFDM mobile edge computing according to an embodiment of this application. Figure 3 As shown, the device includes:

[0165] The first construction unit 10 is used to construct an information timeliness model for uplink OFDM mobile edge computing. The information timeliness model is used to simulate the changing trend of the first freshness with the first target ratio, the first transmission rate, the first target bandwidth, and the second target ratio. The first freshness is the freshness of the calculation result of the task to be calculated. The first target ratio is the percentage of the task to be calculated completed by online computing and local computing. The first transmission rate is the real-time signal transmission rate between the terminal device and the communication base station. The first target bandwidth is the spectrum bandwidth for data transmission from the terminal device to the communication base station. The second target ratio is the computing power ratio of the base station processor to which the task to be calculated is allocated.

[0166] Specifically, the wireless uplink MEC system deployment consists of one communication base station and k single-antenna MEC user terminals, i.e., the aforementioned terminal equipment. This MEC system is an edge computing system, and the communication method is OFDM communication.

[0167] In practical implementation, the aforementioned communication base station is connected to the core network and a computing server is deployed to provide online computing resources for MEC user terminals. Let the first freshness be F. k First target ratio a k First transmission rate v k The first target bandwidth w k Second target proportion β k The first target ratio, the first transmission rate, the first target bandwidth, and the second target ratio are determined as input parameters, and the first freshness is determined as the output parameter. A corresponding simulation model is constructed to output the change of the input parameters, that is, to simulate the freshness of the user's computing task under different online and offline computing scheduling allocations.

[0168] The second construction unit 20 is used to construct a target optimization model based on the above-mentioned information timeliness model and multiple preset constraints. The first objective function of the target optimization model is to maximize the sum of the first freshness corresponding to each of the above-mentioned terminal devices. The multiple preset constraints include the first target ratio of each of the above-mentioned terminal devices being less than or equal to a first preset value, the sum of the first target bandwidth of each of the above-mentioned terminal devices being less than or equal to a second preset value, and the sum of the second target ratio of each of the above-mentioned terminal devices being less than or equal to a third preset value.

[0169] Specifically, this application sets an optimization objective based on the aforementioned information timeliness model, namely, maximizing the freshness of the overall computing task, and constructs the aforementioned objective optimization model.

[0170] The computing unit 30 is used to solve the above-mentioned target optimization model through the MM iterative algorithm to obtain the control parameters of the above-mentioned MEC system, and to control the above-mentioned MEC system to perform operations based on the above-mentioned control parameters.

[0171] Specifically, based on the MM iterative algorithm, the above-mentioned target optimization model is solved under the condition of satisfying the above constraints to obtain the control parameters of the above-mentioned MEC system, including the first target ratio, the first transmission rate, the first target bandwidth and the second target ratio. Then, the MEC system is controlled based on the input parameters of the above-mentioned information timeliness model to ensure the real-time information timeliness of the system.

[0172] In this embodiment, the first construction unit constructs an information timeliness model for uplink OFDM mobile edge computing. This information timeliness model simulates the changing trend of a first freshness with respect to a first target ratio, a first transmission rate, a first target bandwidth, and a second target ratio. The first freshness refers to the freshness of the computation result of the task to be computed; the first target ratio is the percentage of the task to be computed completed by online computation and local computation; the first transmission rate is the real-time signal transmission rate between the terminal device and the communication base station; the first target bandwidth is the spectral bandwidth for data transmission from the terminal device to the communication base station; and the second target ratio is the base station processor allocated to the task to be computed. The computing power ratio; the second construction unit constructs a target optimization model based on the above information timeliness model and multiple preset constraints. The first objective function of the target optimization model is to maximize the sum of the first freshness corresponding to each of the above terminal devices. The multiple preset constraints include the first target ratio of each of the above terminal devices being less than or equal to a first preset value, the sum of the first target bandwidth of each of the above terminal devices being less than or equal to a second preset value, and the sum of the second target ratio of each of the above terminal devices being less than or equal to a third preset value; the computing unit solves the target optimization model through the MM iterative algorithm to obtain the control parameters of the above MEC system, and controls the above MEC system to perform calculations based on the above control parameters. This application addresses the challenge of evaluating information freshness in existing edge computing processes, where task execution involves both local and online computation. It establishes an information timeliness model for uplink OFDM mobile edge computing, jointly optimizing the correlation between user task allocation ratio, transmission rate, transmission spectrum resources, base station edge computing resource allocation, and information freshness. The objective function is optimized to achieve the best information timeliness, and this objective function is decomposed into sub-problems for solution. This yields the optimal parameter settings for edge computing, achieving the best information timeliness. This method solves the problem of existing edge computing task allocation methods that lack consideration for information timeliness.

[0173] The aforementioned task allocation device based on uplink OFDM mobile edge computing includes a processor and a memory. The first building unit, the second building unit, and the computing unit are all stored as program units in the memory, and the processor executes the program units stored in the memory to achieve the corresponding functions. All of the above modules are located in the same processor; alternatively, the modules may be located in different processors in any combination.

[0174] The processor contains a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured, and adjusting kernel parameters can improve the timeliness of edge computing results.

[0175] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0176] This application provides a computer-readable storage medium that includes a stored program, wherein when the program is executed, it controls the device where the computer-readable storage medium is located to perform the task allocation method based on uplink OFDM mobile edge computing.

[0177] Specifically, the task allocation method based on uplink OFDM mobile edge computing includes:

[0178] Step S201: Construct an information timeliness model for uplink OFDM mobile edge computing. The information timeliness model is used to simulate the changing trend of the first freshness with the first target ratio, the first transmission rate, the first target bandwidth, and the second target ratio. The first freshness is the freshness of the calculation result of the task to be calculated. The first target ratio is the percentage of the task to be calculated completed by online computing and local computing. The first transmission rate is the real-time signal transmission rate between the terminal device and the communication base station. The first target bandwidth is the spectrum bandwidth for data transmission from the terminal device to the communication base station. The second target ratio is the computing power ratio of the base station processor to which the task to be calculated is allocated.

[0179] Step S202: Construct a target optimization model based on the above information timeliness model and multiple preset constraints. The first objective function of the target optimization model is to maximize the sum of the first freshness corresponding to each of the above terminal devices. The multiple preset constraints include the first target ratio of each of the above terminal devices being less than or equal to a first preset value, the sum of the first target bandwidth of each of the above terminal devices being less than or equal to a second preset value, and the sum of the second target ratio of each of the above terminal devices being less than or equal to a third preset value.

[0180] Step S203: Solve the above target optimization model using the MM iterative algorithm to obtain the control parameters of the above MEC system, and control the above MEC system to perform operations based on the above control parameters.

[0181] This application provides a processor for running a program, wherein the program executes the task allocation method based on uplink OFDM mobile edge computing.

[0182] Specifically, the task allocation method based on uplink OFDM mobile edge computing includes:

[0183] Step S201: Construct an information timeliness model for uplink OFDM mobile edge computing. The information timeliness model is used to simulate the changing trend of the first freshness with the first target ratio, the first transmission rate, the first target bandwidth, and the second target ratio. The first freshness is the freshness of the calculation result of the task to be calculated. The first target ratio is the percentage of the task to be calculated completed by online computing and local computing. The first transmission rate is the real-time signal transmission rate between the terminal device and the communication base station. The first target bandwidth is the spectrum bandwidth for data transmission from the terminal device to the communication base station. The second target ratio is the computing power ratio of the base station processor to which the task to be calculated is allocated.

[0184] Step S202: Construct a target optimization model based on the above information timeliness model and multiple preset constraints. The first objective function of the target optimization model is to maximize the sum of the first freshness corresponding to each of the above terminal devices. The multiple preset constraints include the first target ratio of each of the above terminal devices being less than or equal to a first preset value, the sum of the first target bandwidth of each of the above terminal devices being less than or equal to a second preset value, and the sum of the second target ratio of each of the above terminal devices being less than or equal to a third preset value.

[0185] Step S203: Solve the above target optimization model using the MM iterative algorithm to obtain the control parameters of the above MEC system, and control the above MEC system to perform operations based on the above control parameters.

[0186] This application provides a communication system, which includes a primary communication domain, a secondary communication domain processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps:

[0187] Step S201: Construct an information timeliness model for uplink OFDM mobile edge computing. The information timeliness model is used to simulate the changing trend of the first freshness with the first target ratio, the first transmission rate, the first target bandwidth, and the second target ratio. The first freshness is the freshness of the calculation result of the task to be calculated. The first target ratio is the percentage of the task to be calculated completed by online computing and local computing. The first transmission rate is the real-time signal transmission rate between the terminal device and the communication base station. The first target bandwidth is the spectrum bandwidth for data transmission from the terminal device to the communication base station. The second target ratio is the computing power ratio of the base station processor to which the task to be calculated is allocated.

[0188] Step S202: Construct a target optimization model based on the above information timeliness model and multiple preset constraints. The first objective function of the target optimization model is to maximize the sum of the first freshness corresponding to each of the above terminal devices. The multiple preset constraints include the first target ratio of each of the above terminal devices being less than or equal to a first preset value, the sum of the first target bandwidth of each of the above terminal devices being less than or equal to a second preset value, and the sum of the second target ratio of each of the above terminal devices being less than or equal to a third preset value.

[0189] Step S203: Solve the above target optimization model using the MM iterative algorithm to obtain the control parameters of the above MEC system, and control the above MEC system to perform operations based on the above control parameters.

[0190] This application also provides a computer program product that, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps:

[0191] Step S201: Construct an information timeliness model for uplink OFDM mobile edge computing. The information timeliness model is used to simulate the changing trend of the first freshness with the first target ratio, the first transmission rate, the first target bandwidth, and the second target ratio. The first freshness is the freshness of the calculation result of the task to be calculated. The first target ratio is the percentage of the task to be calculated completed by online computing and local computing. The first transmission rate is the real-time signal transmission rate between the terminal device and the communication base station. The first target bandwidth is the spectrum bandwidth for data transmission from the terminal device to the communication base station. The second target ratio is the computing power ratio of the base station processor to which the task to be calculated is allocated.

[0192] Step S202: Construct a target optimization model based on the above information timeliness model and multiple preset constraints. The first objective function of the target optimization model is to maximize the sum of the first freshness corresponding to each of the above terminal devices. The multiple preset constraints include the first target ratio of each of the above terminal devices being less than or equal to a first preset value, the sum of the first target bandwidth of each of the above terminal devices being less than or equal to a second preset value, and the sum of the second target ratio of each of the above terminal devices being less than or equal to a third preset value.

[0193] Step S203: Solve the above target optimization model using the MM iterative algorithm to obtain the control parameters of the above MEC system, and control the above MEC system to perform operations based on the above control parameters.

[0194] This application provides a processor for running a program, wherein the program executes the task allocation method based on uplink OFDM mobile edge computing.

[0195] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0201] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0202] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0203] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0204] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0205] 1) The task allocation method based on uplink OFDM mobile edge computing of this application firstly constructs an information timeliness model for uplink OFDM mobile edge computing. This information timeliness model is used to simulate the changing trend of a first freshness with respect to a first target ratio, a first transmission rate, a first target bandwidth, and a second target ratio. The first freshness is the freshness of the computation result of the task to be computed; the first target ratio is the percentage of the task to be computed completed by online computation and local computation; the first transmission rate is the real-time signal transmission rate between the terminal device and the communication base station; the first target bandwidth is the spectrum bandwidth for data transmission from the terminal device to the communication base station; and the second target ratio is the percentage of the task to be computed that is completed by online computation and local computation. The calculation process involves determining the computing power ratio of the base station processors to which the task is assigned. Then, based on the aforementioned information timeliness model and multiple preset constraints, a target optimization model is constructed. The first objective function of the target optimization model is to maximize the sum of the first freshness values ​​corresponding to each of the aforementioned terminal devices. The multiple preset constraints include the first target ratio of each of the aforementioned terminal devices being less than or equal to a first preset value, the sum of the first target bandwidths of each of the aforementioned terminal devices being less than or equal to a second preset value, and the sum of the second target ratios of each of the aforementioned terminal devices being less than or equal to a third preset value. Subsequently, the target optimization model is solved using the MM iterative algorithm to obtain the control parameters of the aforementioned MEC system. Based on the aforementioned control parameters, the aforementioned MEC system is controlled to perform calculations. This application addresses the challenge of evaluating information freshness in existing edge computing processes, where task execution involves both local and online computation. It establishes an information timeliness model for uplink OFDM mobile edge computing, jointly optimizing the correlation between user task allocation ratio, transmission rate, transmission spectrum resources, base station edge computing resource allocation, and information freshness. The objective function is optimized to achieve the best information timeliness, and this objective function is decomposed into sub-problems for solution. This yields the optimal parameter settings for edge computing, achieving the best information timeliness. This method solves the problem of existing edge computing task allocation methods that lack consideration for information timeliness.

[0206] 2) The task allocation device based on uplink OFDM mobile edge computing of this application, wherein the first building unit constructs an information timeliness model for uplink OFDM mobile edge computing. The information timeliness model is used to simulate the changing trend of the first freshness with the first target ratio, the first transmission rate, the first target bandwidth, and the second target ratio. The first freshness is the freshness of the calculation result of the task to be calculated. The first target ratio is the percentage of the task to be calculated completed by online calculation and local calculation. The first transmission rate is the real-time signal transmission rate between the terminal device and the communication base station. The first target bandwidth is the spectrum bandwidth for data transmission from the terminal device to the communication base station. The second target ratio is the percentage of the task to be calculated. The computing power ratio of the base station processors to which the task is assigned; the second construction unit constructs a target optimization model based on the above information timeliness model and multiple preset constraints. The first objective function of the target optimization model is to maximize the sum of the first freshness corresponding to each of the above terminal devices. The multiple preset constraints include the first target ratio of each of the above terminal devices being less than or equal to a first preset value, the sum of the first target bandwidth of each of the above terminal devices being less than or equal to a second preset value, and the sum of the second target ratio of each of the above terminal devices being less than or equal to a third preset value; the calculation unit solves the target optimization model through the MM iterative algorithm to obtain the control parameters of the above MEC system, and controls the above MEC system to perform calculations based on the above control parameters. This application addresses the challenge of evaluating information freshness in existing edge computing processes, where task execution involves both local and online computation. It establishes an information timeliness model for uplink OFDM mobile edge computing, jointly optimizing the correlation between user task allocation ratio, transmission rate, transmission spectrum resources, base station edge computing resource allocation, and information freshness. The objective function is optimized to achieve the best information timeliness, and this objective function is decomposed into sub-problems for solution. This yields the optimal parameter settings for edge computing, achieving the best information timeliness. This method solves the problem of existing edge computing task allocation methods that lack consideration for information timeliness.

[0207] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A task allocation method based on uplink OFDM mobile edge computing, characterized in that, The MEC system includes a communication base station and multiple terminal devices, including: An information timeliness model for uplink OFDM mobile edge computing is constructed. This model is used to simulate the changing trend of a first freshness with a first target ratio, a first transmission rate, a first target bandwidth, and a second target ratio. The first freshness is the freshness of the computation result of the task to be computed. The first target ratio is the percentage of the task to be computed that is completed by online computation and local computation. The first transmission rate is the real-time signal transmission rate between the terminal device and the communication base station. The first target bandwidth is the spectrum bandwidth for data transmission from the terminal device to the communication base station. The second target ratio is the computing power ratio of the base station processors allocated to the task to be computed. A target optimization model is constructed based on the information timeliness model and multiple preset constraints. The first objective function of the target optimization model is to maximize the sum of the first freshness corresponding to each terminal device. The multiple preset constraints include the first target ratio of each terminal device being less than or equal to a first preset value, the sum of the first target bandwidth of each terminal device being less than or equal to a second preset value, and the sum of the second target ratio of each terminal device being less than or equal to a third preset value. The target optimization model is solved by the MM iterative algorithm to obtain the control parameters of the MEC system, and the MEC system is controlled to perform operations based on the control parameters.

2. The method according to claim 1, characterized in that, Constructing an information timeliness model for uplink OFDM mobile edge computing, including: A first target formula is constructed to simulate the process of a first target signal being converted into a second target signal through uplink channel transmission. The first target signal is the signal sent by the terminal device to the communication base station, and the second target signal is the signal received by the communication base station. A second target formula is constructed to simulate the changing trend of the first target probability with the first transmission power, the first transmission rate and the first target bandwidth. The first target probability is the probability that the communication base station successfully receives the second target signal, and the first transmission power is the signal transmission power of the terminal device. A third objective formula is constructed to simulate the changing trend of the second freshness with the first objective probability, the first objective proportion, the first transmission rate, and the second objective proportion. The second freshness is the freshness of the calculation result calculated online. A fourth objective formula is constructed to simulate the changing trend of the third freshness with the proportion of the first objective, wherein the third freshness is the freshness of the calculation result calculated locally; A fifth objective formula is constructed, which is used to determine the minimum value between the second freshness and the third freshness as the first freshness, and the first freshness is output.

3. The method according to claim 2, characterized in that, Construct the first objective formula, including: Obtain the first target signal and the second target signal, and construct the first target formula based on the first target signal and the second target signal: Among them, y k h is the second target signal. k The channel response of the uplink channel, p k x is the first transmission power. k Let n be the first target signal. k The uplink channel is the additive Gaussian noise.

4. The method according to claim 2, characterized in that, Constructing the second objective formula includes: Obtain the second transmission rate, the first target bandwidth, and the first transmission power. Construct a sixth target formula based on the second transmission rate, the first target bandwidth, and the first transmission power. The sixth target formula characterizes the changing trend of the second transmission rate with respect to the first target bandwidth and the first transmission power. The second transmission rate is the achievable rate of the uplink channel. r k =w k ·log2(1+p k |h k 2 ); Where, r k For the second transmission rate, w k For the first target bandwidth, p k h is the first transmission power. k This refers to the channel response of the uplink channel; A first transmission rate and a preset condition are obtained. If the first transmission rate and the second transmission rate meet the preset condition, it is determined that the communication base station has successfully received the second target signal. A second target formula is constructed based on the preset condition, the first transmission rate, and the second transmission rate. The preset condition is that the first transmission rate is less than or equal to the second transmission rate. Wherein, φ(v k ,w k ) represents the first target probability, vk represents the first transmission rate, and γk represents a preset coefficient.

5. The method according to claim 2, characterized in that, Constructing the third objective formula includes: Obtain the second freshness, the first target probability, the first target ratio, the first transmission rate, and the second target ratio; construct the third target formula based on the second freshness, the first target probability, the first target ratio, the first transmission rate, and the second target ratio: in, For the second freshness, λ k M is the update interval for the task to be computed. k For the task to be computed, vk is the first transmission rate, φ(v k ,w k ) represents the probability of the first target, ak represents the proportion of the first target, and β k C is the second target ratio. B This refers to the computational processing speed of the communication base station.

6. The method according to claim 2, characterized in that, Constructing the fourth objective formula includes: Obtain the third freshness and the first target ratio, and construct the fourth target formula based on the third freshness and the first target ratio: in, For the third freshness, a k λ is the first target ratio. k M is the update interval for the task to be computed. k For the task to be computed, C k This refers to the computing and processing speed of the terminal device.

7. The method according to claim 2, characterized in that, The control parameters of the MEC system are obtained by solving the target optimization model using the MM iterative algorithm, including: The first optimization step involves setting the second target ratio, the first target bandwidth, and the first transmission rate to be greater than 0 and remain unchanged as the first target conditions. Based on the constraints of the first target conditions, the solution of the first objective function is transformed into solving the first target ratio corresponding to the maximum value of the sum of the first freshnesses, thus obtaining the second objective function. The difference between the second freshness and the third freshness is determined as the sixth objective formula. Based on the sixth objective formula, the closed-form solution of the second objective function is solved to obtain the alternative control parameters corresponding to the first target ratio. The second optimization step involves setting the first target ratio, the second target ratio, and the first target bandwidth to be greater than 0 and remain unchanged as the second target conditions. Based on the constraints of the second target conditions, the solution of the first objective function is transformed into solving the first transmission rate corresponding to the maximum value of the sum of the first freshnesses, thus obtaining the third objective function. The third objective function is then solved using the bisection method to obtain the alternative control parameters corresponding to the first transmission rate. The third optimization step involves setting the first target ratio, the first target bandwidth, and the first transmission rate to be greater than 0 and remain unchanged as the third target conditions. Based on the third target conditions, a correlation relationship between the second target ratio between two adjacent iterations is constructed to obtain the seventh target formula. Based on the seventh target formula and the third target conditions, the first objective function is equivalently transformed to obtain the fourth objective function. Based on the fourth objective function, the corresponding closed-form solution is solved to obtain the alternative control parameters corresponding to the second target ratio. The fourth optimization step involves setting the first target ratio, the second target ratio, and the first transmission rate to be greater than 0 and remain unchanged as the fourth target conditions. Based on the fourth target conditions, a correlation relationship between the first target bandwidth between two adjacent iterations is constructed to obtain the eighth target formula. Based on the eighth target formula and the fourth target conditions, the first objective function is equivalently transformed to obtain the fifth objective function. Based on the fifth objective function, the corresponding closed-form solution is solved to obtain the alternative control parameters corresponding to the first target bandwidth. Repeat the first optimization step, the second optimization step, the third optimization step, and the fourth optimization step at least once in sequence until the error between two adjacent iterations is less than a preset value. Then, determine the alternative control parameters corresponding to the first target ratio, the first transmission rate, the first target bandwidth, and the second target ratio as the control parameters.

8. A task allocation device based on uplink OFDM mobile edge computing, characterized in that, The MEC system includes a communication base station and multiple terminal devices, the device comprising: The first construction unit is used to construct an information timeliness model for uplink OFDM mobile edge computing. The information timeliness model is used to simulate the changing trend of a first freshness with a first target ratio, a first transmission rate, a first target bandwidth, and a second target ratio. The first freshness is the freshness of the calculation result of the task to be calculated. The first target ratio is the percentage of the task to be calculated completed by online computing and local computing. The first transmission rate is the real-time signal transmission rate between the terminal device and the communication base station. The first target bandwidth is the spectrum bandwidth for data transmission from the terminal device to the communication base station. The second target ratio is the computing power ratio of the base station processor allocated to the task to be calculated. The second construction unit is used to construct a target optimization model based on the information timeliness model and multiple preset constraints. The first objective function of the target optimization model is to maximize the sum of the first freshness corresponding to each terminal device. The multiple preset constraints include the first target ratio of each terminal device being less than or equal to a first preset value, the sum of the first target bandwidth of each terminal device being less than or equal to a second preset value, and the sum of the second target ratio of each terminal device being less than or equal to a third preset value. The computing unit is used to solve the target optimization model using the MM iterative algorithm to obtain the control parameters of the MEC system, and to control the MEC system to perform calculations based on the control parameters.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 7.

10. A mobile edge computing system, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising methods for performing any one of claims 1 to 7.

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