Data transmission method and device of distributed system, equipment and storage medium

Abstract

The embodiment of the invention provides a data transmission method and device of a distributed system, equipment and a storage medium, and relates to the technical field of communication. The method comprises the steps of obtaining server calculation power related to energy consumption of each terminal device, then obtaining target power according to the server calculation power, signal transmission power and beam forming power of a receiving end, accumulating all the target power to obtain total power, minimizing the total power to generate an optimization objective function, and on the premise that a constraint condition set is met, optimizing the target function. And solving the optimized target function to obtain a target transmission parameter of each terminal device in the corresponding channel. According to the method, the users are layered according to data requirements, and different resources are allocated to the users with different delay requirements, so that the mobile devices of the users can avoid unnecessary calculation burden while meeting own requirements. The overall optimization modeling is performed by combining the energy consumption of all mobile devices in the transmission process, and the total energy consumption of the network is reduced on the basis of ensuring the transmission fluency.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a data transmission method, apparatus, device and storage medium for a distributed system. Background Art

[0002] As an important application of the fifth generation network, wireless virtual reality can provide an immersive experience and is widely used in performances, education, games and other scenarios. The transmission of virtual reality videos requires ultra-high resolution and ultra-low latency tolerance, which is accompanied by a huge amount of data transmission and processing. Therefore, the demand for computing power for virtual reality devices, especially mobile devices, has exploded.

[0003] In related technologies, mobile edge computing is used to assist the transmission of virtual reality videos. By deploying edge servers around users, the computing power of access points is improved to support real-time, large-scale computing-intensive services. However, in this way, mobile devices still need to perform a large number of computing processes, which leads to high energy consumption of mobile devices, thereby reducing the performance of wireless virtual reality networks. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose a data transmission method, apparatus, device and storage medium for a distributed system, reduce the energy consumption of mobile devices and improve network performance.

[0005] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a data transmission method for a distributed system, wherein the distributed system includes at least an edge server and multiple communication layers, each of the communication layers includes at least one terminal device, and different communication layers have different data quality requirements. The edge server establishes a channel with each of the terminal devices through a wireless access point, and the method includes:

[0006] For each of the terminal devices, the signal transmission power and the beamforming power of the receiving end are obtained, and the server calculation power is calculated according to the channel bandwidth, the allocation resource parameter, the signal to interference noise ratio, the calculation resource parameter and the effective capacitance coefficient of the corresponding channel;

[0007] Obtaining a target power corresponding to each of the terminal devices according to the server calculation power, the signal transmission power, and the beamforming power of the receiving end, accumulating all the target powers to obtain a total power, and minimizing the total power to generate an optimization objective function;

[0008] Generate a set of constraint conditions based at least on the amount of user data corresponding to each of the terminal devices, the signal to interference and noise ratio, the computing resource parameter, and the signal transmission power;

[0009] On the premise of satisfying the constraint condition set, the optimization objective function is solved to obtain the target transmission parameters of each terminal device in the corresponding channel, wherein the target transmission parameters include target computing resource parameters, target signal transmission power and target beamforming vector.

[0010] In some embodiments, the calculating server calculating power according to the channel bandwidth of the corresponding channel, the allocated resource parameter, the signal to interference noise ratio, the calculation resource parameter and the effective capacitance coefficient includes:

[0011] Calculating a signal to interference plus noise ratio according to a beamforming vector of a receiving end, channel parameters, and the signal transmission power;

[0012] The communication rate is obtained by adding one to the signal to interference noise ratio and calculating the logarithm, and then multiplying the result by the channel bandwidth. The product of the communication rate, the allocation resource parameter, the computing resource parameter and the effective capacitance coefficient is calculated to obtain the server computing power.

[0013] In some embodiments, the terminal devices corresponding to the same communication layer are sorted and numbered from high to low according to the channel gain, and the signal to interference noise ratio is calculated according to the beamforming vector of the receiving end, the channel parameter, and the signal transmission power, including:

[0014] Calculate the expected signal power according to the beamforming vector, the channel parameter, and the signal transmission power corresponding to the terminal device;

[0015] Other terminal devices located in the same communication layer as the terminal device and with a number greater than that of the terminal device are regarded as same-layer terminal devices, and terminal devices located in different communication layers from the terminal device are regarded as other-layer terminal devices, and the same-layer signal power corresponding to each of the same-layer terminal devices is obtained, and the other-layer signal power corresponding to each of the other-layer terminal devices is obtained;

[0016] Accumulating the same-layer signal powers to obtain the same-layer interference signal power, accumulating the other-layer signal powers to obtain the other-layer interference signal power, and obtaining the total interference signal power according to the same-layer interference signal power and the other-layer interference signal power;

[0017] The sum of the total interference signal power and the noise power is acquired as the interference noise power, and the ratio of the expected signal power to the interference noise power is calculated to obtain the signal to interference noise ratio.

[0018] In some embodiments, the generating a constraint set based at least on the amount of user data corresponding to each of the terminal devices, the signal to interference and noise ratio, the computing resource parameter, and the signal transmission power includes:

[0019] For each of the terminal devices, generating a delay constraint condition according to the corresponding user data volume, the signal to interference and noise ratio, the computing resource parameter, and the signal transmission power;

[0020] Accumulating the signal transmission power corresponding to each of the terminal devices to obtain a total transmission power, setting the total transmission power to be less than or equal to a maximum power value, and generating a transmission power constraint condition;

[0021] Accumulating the computing resource parameters corresponding to each of the terminal devices to obtain a total computing resource, setting the total computing resource to be less than or equal to a maximum resource value, and generating a computing resource constraint condition;

[0022] The constraint condition set is obtained according to the delay constraint condition, the transmission power constraint condition and the computing resource constraint condition.

[0023] In some embodiments, for each of the terminal devices, generating a delay constraint condition according to the corresponding user data volume, the signal to interference and noise ratio, the computing resource parameter, and the signal transmission power includes:

[0024] For each of the terminal devices, the corresponding communication rate is obtained, and a first delay parameter is obtained according to a ratio of the user data volume to the communication rate;

[0025] Obtaining a second delay parameter according to a ratio of the user data volume to the computing resource parameter, and calculating the sum of the first delay parameter and the second delay parameter to obtain a total delay parameter corresponding to the terminal device;

[0026] The total delay parameter of each terminal device is set to be less than or equal to the delay upper limit value corresponding to the terminal device, and the delay constraint condition is generated.

[0027] In some embodiments, solving the optimization objective function under the premise of satisfying the constraint condition set to obtain the target transmission parameters of each terminal device in the corresponding channel includes:

[0028] Acquire computing resource optimization items related to the allocated resource parameters from the optimization objective function, solve the computing resource optimization items, and obtain the target computing resource parameters;

[0029] Under the constraint of the target computing resource parameters, a transmission optimization item related to the signal transmission power and the beamforming vector is obtained from the optimization objective function, and the transmission optimization item is solved to obtain the target signal transmission power and the target beamforming vector.

[0030] In some embodiments, solving the computing resource optimization item to obtain the target computing resource parameter includes:

[0031] Setting a first Lagrangian multiplier corresponding to the time delay constraint and a second Lagrangian multiplier corresponding to the computing resource constraint;

[0032] Obtaining a computing resource Lagrangian function according to the computing resource optimization item, the first Lagrangian multiplier, and the second Lagrangian multiplier;

[0033] Partial derivatives of the computing resource Lagrangian function are calculated to obtain a first partial derivative function, and the first partial derivative function is solved to obtain the target computing resource parameter.

[0034] In some embodiments, solving the transmission optimization item to obtain the target signal transmission power and the target beamforming vector includes:

[0035] generating a delay reference item corresponding to the signal to interference plus noise ratio according to the delay constraint, generating a convex lower bound corresponding to the signal to interference plus noise ratio, and generating a convex upper bound corresponding to the communication rate;

[0036] Based on the convex upper bound, the transmission optimization item is updated to obtain a transmission optimization update item, and the delay reference item is set to be less than or equal to the convex lower bound to obtain a reference delay constraint condition;

[0037] Based on the reference delay constraint and the transmission power constraint, the transmission optimization update term is solved to obtain the target signal transmission power and the target beamforming vector.

[0038] In some embodiments, generating a convex lower bound corresponding to the signal to interference plus noise ratio includes:

[0039] Obtaining an auxiliary variable according to the inverse of the signal transmission power, adjusting the expected signal power based on the auxiliary variable to obtain a first non-convex function, and adjusting the interference noise power according to the auxiliary variable to obtain a first convex function, and updating the signal to interference plus noise ratio using the first convex function and the first non-convex function to obtain a signal to interference plus noise ratio function;

[0040] Obtaining a first iteration value corresponding to each iteration according to the first non-convex function, and obtaining a second iteration value corresponding to each iteration according to the first convex function;

[0041] Introducing a first auxiliary convex function, and performing a first-order Taylor expansion on the first auxiliary convex function at a position where the first iteration value is greater than zero and the second iteration value is greater than zero to obtain a first inequality;

[0042] Performing a first-order Taylor expansion on the first non-convex function to obtain a second inequality, substituting the first inequality into the second inequality to obtain the convex lower bound corresponding to the signal to interference noise ratio function in each iteration process.

[0043] In some embodiments, generating a convex upper bound corresponding to the communication rate includes:

[0044] Obtaining a second convex function according to the first non-convex function and the first convex function, and obtaining a second non-convex function according to the inverse of the first convex function, and updating the communication rate using the second convex function and the second non-convex function to obtain a communication rate function;

[0045] Obtaining a third iteration value corresponding to each iteration according to the second non-convex function, and obtaining a fourth iteration value corresponding to each iteration according to the second convex function;

[0046] Introducing a second auxiliary convex function, and performing a first-order Taylor expansion on the second auxiliary convex function at a position where the third iteration value is greater than zero and the fourth iteration value is greater than zero to obtain a third inequality;

[0047] After applying the third inequality to the communication rate function, substituting the third iteration value and the fourth iteration value into the third iteration value, the convex upper bound corresponding to the communication rate function in each iteration process is obtained.

[0048] In some embodiments, solving the transmission optimization update term based on the reference delay constraint and the transmission power constraint to obtain the target signal transmission power and the target beamforming vector includes:

[0049] Based on the constraint set, all variables are initialized, and the target computing resource parameters are obtained;

[0050] Initializing the auxiliary variables to obtain the initial values ​​of the auxiliary variables corresponding to the first iteration process;

[0051] Entering multiple iterative solution processes, solving the transmission optimization update item according to the auxiliary variable value, beamforming vector value and the target computing resource parameter of the current iterative process, obtaining iterative auxiliary variable value and iterative beamforming vector value, using the iterative auxiliary variable value as the auxiliary variable value of the next iterative process, and using the iterative beamforming vector value as the beamforming vector value of the next iterative process, and continuing the iterative process until the iteration stop condition is reached;

[0052] The target signal transmission power is obtained according to the iterative auxiliary variable value obtained in the last iterative process, and the target beamforming vector is obtained according to the iterative beamforming vector value obtained in the last iterative process.

[0053] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application proposes a data transmission device of a distributed system, wherein the distributed system comprises at least an edge server and multiple communication layers, each of the communication layers comprises at least one terminal device, and different communication layers have different data quality requirements. The edge server establishes a channel with each of the terminal devices through a wireless access point, and the device comprises:

[0054] Power calculation module: used to obtain the signal transmission power and the beamforming power of the receiving end for each of the terminal devices, and calculate the server calculation power according to the channel bandwidth, allocation resource parameters, signal to interference noise ratio, calculation resource parameters and effective capacitance coefficient of the corresponding channel;

[0055] An optimization target construction module is used to obtain a target power corresponding to each of the terminal devices according to the server calculation power, the signal transmission power and the beamforming power of the receiving end, accumulate all the target powers to obtain a total power, and minimize the total power to generate an optimization target function;

[0056] A constraint condition building module: used to generate a constraint condition set based on at least the amount of user data corresponding to each of the terminal devices, the signal to interference and noise ratio, the computing resource parameter and the signal transmission power;

[0057] Optimization solution module: used to solve the optimization objective function on the premise of satisfying the constraint condition set, and obtain the target transmission parameters of each terminal device in the corresponding channel, wherein the target transmission parameters include target computing resource parameters, target signal transmission power and target beamforming vector.

[0058] To achieve the above objectives, a third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in the first aspect is implemented.

[0059] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a storage medium, which is a storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0060] The data transmission method, device, equipment and storage medium of the distributed system proposed in the embodiment of the present application obtain the signal transmission power and the beamforming power of the receiving end for each terminal device, and calculate the server calculation power according to the channel bandwidth, allocation resource parameter, signal to noise ratio, calculation resource parameter and effective capacitance coefficient of the corresponding channel, and then obtain the target power corresponding to each terminal device according to the server calculation power, signal transmission power and beamforming power of the receiving end, accumulate all target powers to obtain the total power, minimize the total power to generate the optimization objective function, generate a constraint condition set based on at least the user data volume, signal to noise ratio, calculation resource parameter and signal transmission power corresponding to each terminal device, and finally solve the optimization objective function on the premise of satisfying the constraint condition set to obtain the target transmission parameters of each terminal device in the corresponding channel, and the target transmission parameters include target calculation resource parameters, target signal transmission power and target beamforming vector. The embodiment of the present application first stratifies users according to their data quality requirements. Since different communication layers correspond to different data quality requirements, different resources can be allocated to users with different delay requirements according to the stratification of users. This method can adapt to user needs and, through reasonable stratification and resource allocation, enable users' mobile devices to avoid unnecessary computing burdens while meeting their own needs. In addition, the effective capacitance coefficient is introduced in the process of optimizing transmission parameters to reflect the energy consumption characteristics of mobile devices under different working conditions, so as to more accurately describe the energy consumption of mobile devices during transmission. At the same time, the energy consumption of all mobile devices during the transmission process is combined for overall optimization modeling, so as to minimize the total network energy consumption while ensuring the smooth transmission of virtual reality content. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a schematic diagram of a distributed system provided in an embodiment of the present application.

[0062] Figure 2 It is a flow chart of the data transmission method of the distributed system provided in an embodiment of the present application.

[0063] Figure 3 It is a flowchart provided in an embodiment of the present application for calculating the server calculation power based on the channel bandwidth, allocation resource parameters, signal-to-interference-noise ratio, calculation resource parameters and effective capacitance coefficient of the corresponding channel.

[0064] Figure 4 This is a flowchart for calculating the signal to interference plus noise ratio based on the beamforming vector, channel parameters, and signal transmission power of the receiving end provided in an embodiment of the present application.

[0065] Figure 5 It is a flowchart provided by an embodiment of the present application for generating a set of constraint conditions based at least on the user data volume, signal-to-interference-noise ratio, computing resource parameters and signal transmission power corresponding to each terminal device.

[0066] Figure 6 This is a flowchart provided by an embodiment of the present application for generating delay constraints for each terminal device based on the corresponding user data volume, signal-to-interference-noise ratio, computing resource parameters and signal transmission power.

[0067] Figure 7 It is a flowchart provided by an embodiment of the present application for solving the optimization objective function under the premise of satisfying a set of constraint conditions to obtain the target transmission parameters of each terminal device in the corresponding channel.

[0068] Figure 8 It is a flowchart for solving computing resource optimization items and obtaining target computing resource parameters provided by an embodiment of the present application.

[0069] Fig. 9 It is a flowchart for solving the transmission optimization items provided in an embodiment of the present application to obtain the target signal transmission power and the target beamforming vector.

[0070] Fig.10 This is a flowchart of generating a convex lower bound corresponding to a signal to interference noise ratio provided by an embodiment of the present application.

[0071] Fig.11 It is a flowchart for generating a convex upper bound corresponding to the communication rate provided by an embodiment of the present application.

[0072] Fig.12 It is a flowchart provided in an embodiment of the present application for solving the transmission optimization update item based on the reference delay constraint condition and the transmission power constraint condition to obtain the target signal transmission power and the target beamforming vector.

[0073] Fig.13 It is a flowchart of the algorithm for solving the transmission optimization update item in the embodiment of the present application.

[0074] Fig.14 It is a schematic diagram of the convergence performance curve of the data transmission method of the distributed system in the embodiment of the present application.

[0075] Fig.15 A schematic diagram showing how the power consumption of a data transmission method in a distributed system provided in an embodiment of the present application varies with the number of antennas.

[0076] Fig.16 This is a structural block diagram of a data transmission device of a distributed system provided by another embodiment of the present application.

[0077] Fig.17 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0079] It should be noted that although the functional modules are divided in the device schematic and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart.

[0080] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0081] First, some nouns involved in this application are analyzed:

[0082] Convex function: For a function f(x), if for any two points x1 and x2 in the domain of definition, and any λ∈[0,1], f(λx1+(1-λ)x2)≤λf(x1)+(1-λ)f(x2), then the function f(x) is a convex function.

[0083] Convex lower bound: For a function f(x), if there exists another convex function g(x) such that for all x in the domain, g(x) ≤ f(x), then g(x) is a convex lower bound of f(x).

[0084] As wireless networks continue to evolve from the fifth generation to the sixth generation, they are increasingly able to meet the low energy consumption and low latency requirements of real-time mobile applications on massive devices. Virtual reality is a typical application example of this trend. As one of the important applications of the fifth generation network, wireless virtual reality can provide users with an immersive experience and is widely used in many scenarios such as performances, education, and games. The transmission of virtual reality videos requires ultra-high resolution and ultra-low latency tolerance, which is inevitably accompanied by a huge amount of data transmission and processing. Therefore, the demand for computing power for virtual reality devices, especially mobile devices, is showing an explosive growth trend.

[0085] In addition, in VR applications, essential display devices such as head-mounted displays frequently move with the user's line of sight and movement. In order to provide users with a good experience, VR content needs to track the user's field of view or camera position in real time and update frequently. This undoubtedly further increases the burden of data transmission and also exacerbates the latency and energy consumption problems of VR networks. Given that mobile devices have limited resources and VR applications are extremely sensitive to latency, it is obviously unrealistic to completely hand over the heavy computing work to the user's VR device and require it to support VR video transmission with low latency and high performance.

[0086] The applicant found that in the relevant technology, mobile edge computing is usually used to assist the transmission of virtual reality videos. Specifically, edge servers are deployed around users to improve the computing power of access points, thereby supporting real-time, large-scale computing-intensive services. Since virtual reality applications have almost stringent requirements for fluency and extremely low tolerance for delays, only about 20 milliseconds. Therefore, in the relevant technology, more emphasis is often placed on the delay problem of virtual reality video transmission, while energy consumption is relatively insufficient. However, even if mobile edge computing can bear most of the computing power requirements, in this way, mobile devices still need to perform part of the computing process, which causes the mobile devices to generate more energy consumption. Especially when applied to wireless communications in specific environments, such as ultra-large-scale Internet of Things scenarios, the increase in data offloading energy consumption will cause network delays to increase significantly, thereby reducing the performance of wireless virtual reality networks.

[0087] In virtual reality application scenarios, the energy consumption of user's mobile device has a more direct impact on user experience. Because virtual reality rendering is a computationally intensive operation, it requires both powerful computing power and consumes a lot of energy. However, most mobile devices have limited battery capacity, and excessive energy consumption will make it difficult for mobile devices to support long-term virtual reality experience, which will significantly reduce user experience.

[0088] Based on this, the embodiments of the present application provide a data transmission method, apparatus, device and storage medium for a distributed system. First, users are stratified according to their data quality requirements. Since different communication layers correspond to different data quality requirements, different resources can be allocated to users with different delay requirements according to their stratification. This method can adapt to user needs and, through reasonable stratification and resource allocation, enable users' mobile devices to avoid unnecessary computing burdens while meeting their own needs. In addition, the effective capacitance coefficient is introduced in the process of optimizing transmission parameters to reflect the energy consumption characteristics of mobile devices under different working conditions, so as to more accurately describe the energy consumption of mobile devices during transmission. At the same time, the energy consumption of all mobile devices during the transmission process is combined to perform overall optimization modeling, and the total network energy consumption is minimized on the basis of ensuring the smoothness of virtual reality content transmission.

[0089] The embodiments of the present application provide a data transmission method, apparatus, device and storage medium for a distributed system, which are specifically illustrated by the following embodiments. First, the data transmission method for a distributed system in the embodiments of the present application is described.

[0090] The data transmission method of the distributed system provided in the embodiment of the present application relates to the field of communication technology. The data transmission method of the distributed system provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server side, and can also be a computer program running in a terminal or a server side. For example, the computer program can be a native program or software module in an operating system; it can be a local (Native) application (Application, APP), that is, a program that needs to be installed in the operating system to run, such as a client that supports data transmission of a distributed system, that is, a program that can be run only by downloading it to a browser environment; it can also be a small program that can be embedded in any APP. In short, the above-mentioned computer program can be any form of application, module or plug-in. Among them, the terminal communicates with the server via a network. The data transmission method of the distributed system can be executed by a terminal or a server, or by a terminal and a server in collaboration.

[0091] In some embodiments, the terminal may be a smart phone, a tablet computer, a laptop computer, a desktop computer or a smart watch, etc. In addition, the terminal may also be an intelligent vehicle-mounted device. The intelligent vehicle-mounted device applies the data transmission method of the distributed system of this embodiment to provide related services and enhance the driving experience. The server may be an independent server, or it may be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms; it may also be a service node in a blockchain system, and each service node in the blockchain system forms a peer-to-peer (Peer To Peer, P2P) network, and the P2P protocol is an application layer protocol running on the Transmission Control Protocol (Transmission Control Protocol, TCP) protocol. The terminal and the server may be connected via Bluetooth, Universal Serial Bus (Universal Serial Bus, USB) or network communication connection methods, and this embodiment is not limited here.

[0092] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0093] The following first introduces the distributed system of an embodiment of the present application.

[0094] Reference Figure 1 , Figure 1 Schematic diagram of a distributed system provided by an embodiment of the present application. Figure 1The distributed system shown in the figure includes at least an edge server, a wireless access point, and multiple communication layers. Each communication layer includes at least one user, and each user may correspond to one or more terminal devices. At this time, the edge server establishes a channel with each terminal device with the help of the wireless access point. The channel here refers to the transmission medium connecting the sending end (wireless access point) and the receiving end communication device (terminal device), that is, the channel for signal transmission.

[0095] The edge server is used to provide additional computing power support for the terminal device within a short range, thereby providing virtual reality content to the terminal device with low latency. The wireless access point is responsible for providing an interface for different terminal devices to access the network. Usually, the wireless access point is equipped with multiple antennas. In the following embodiments, N antennas will be used as an example for explanation.

[0096] In one embodiment, users at different communication layers have different requirements for data quality, and the data quality requirements here can be specifically reflected in the requirements for delay. By dividing users into different communication layers, the distributed system can allocate differentiated resources to users with different delay requirements according to the user's stratification status.

[0097] For example, for latency-sensitive users, the system will allocate more network bandwidth, better communication links and other resources to ensure that they can get a smooth experience. For those users with relatively low latency requirements, the system will allocate relatively fewer resources, which can effectively improve the utilization rate of the entire network resources. This resource allocation method can not only avoid waste of resources, but also ensure that each user can get services that meet their needs.

[0098] In addition, given that the computing resources of mobile devices are usually limited, if the device is required to perform a large number of additional computing tasks in order to meet excessively high data quality requirements, it is very likely to cause energy consumption problems such as device heating and shortened battery life, and even affect the normal operation of the device. In this embodiment, through reasonable stratification and resource allocation strategies, the user's mobile device can avoid unnecessary computing burdens while meeting its own needs, thereby improving the stability and service life of the device.

[0099] In one embodiment, the number of communication layers may be L, and the number of users corresponding to the lth communication layer may be K. l Generally, the number of mobile terminals in a communication layer with high delay requirements is less than the number of mobile terminals in a communication layer with low delay requirements.

[0100] For example, Figure 1The communication layer shown in the figure is divided into a basic layer and an enhanced layer. The basic layer users can correspond to K1 mobile virtual reality terminal devices. Such users only have basic requirements for the quality of service implementation, that is, low latency requirements. Among them, the i-th terminal device of the basic layer can be represented as u 1,i The enhanced layer users can correspond to K2 mobile virtual reality terminal devices. Such users have high service quality, that is, high latency requirements. The i-th terminal device of the enhanced layer can be represented as u 2,i Generally speaking, the number of terminal devices in the enhanced layer of the network is much less than the number of terminal devices in the basic layer, that is, K2 <K1。

[0101] It is understandable that although Figure 1 The following embodiments are all illustrated with two communication layers, which does not mean that the number of communication layers is limited to two.

[0102] Combine the following Figure 1 The distributed system in the embodiment of the present application describes the data transmission method of the distributed system.

[0103] Figure 2 is an optional flowchart of a data transmission method of a distributed system provided in an embodiment of the present application. Figure 2 The method may include but is not limited to steps 110 to 140. It can also be understood that this embodiment Figure 2 The order of step 110 to step 140 is not specifically limited, and the order of steps can be adjusted or some steps can be reduced or added according to actual needs.

[0104] Step 110: For each terminal device, obtain the signal transmission power and the beamforming power of the receiving end, and calculate the server calculation power according to the channel bandwidth, allocation resource parameters, signal to interference and noise ratio, calculation resource parameters and effective capacitance coefficient of the corresponding channel.

[0105] In one embodiment, for the kth terminal device u in the lth layer l,k For example, the channel parameter corresponding to the channel is defined as h l,k , the channel parameter can be channel gain or channel frequency response, etc. The signal transmission power is p l,k , the beamforming vector at the receiving end is w l,k Assume that the terminal device transmits virtual reality related signals in the network using complex numbers s l,k Indicates that the signal has unit energy, that is,

[0106] Therefore, the signal received by the edge server about the kth terminal device in the lth layer can be expressed as:

[0107]

[0108] in, Indicates terminal device u l,k The total signal of the signal related to virtual reality after channel, beamforming and power adjustment.

[0109] and Indicates the other terminal devices in layer l to terminal device u l,k The interference signal generated is calculated from k+1 onwards because the Non-Orthogonal Multiple Access (NOMA) technology is used in the transmission process. Under this technology, the terminal devices corresponding to the same communication layer are sorted and numbered from high to low according to the channel gain. For the edge server, the detection process is as follows: first detect the terminal device with a strong signal strength, then subtract the signal of the detected terminal device from the received signal, and then detect the next terminal device. Therefore, after detecting a terminal device and subtracting its signal, the detection environment of the subsequent terminal devices has changed. At this time, the remaining interference mainly comes from the terminal devices that have not been detected. This means that after completing the processing of a terminal device, the terminal device will no longer interfere with subsequent detections. Therefore, only the terminal devices that are ranked behind will interfere with the terminal device being detected. Based on this detection and cancellation order, for the kth terminal device, the terminal devices ranked after k will generate corresponding interference.

[0110] and Indicates the interference caused by the corresponding signals of the terminal devices of other communication layers. This embodiment takes two communication layers as an example for illustration, so when l=1, 3-l=2 corresponds to the terminal devices of the enhancement layer, and when l=2, 3-l=1 corresponds to the terminal devices of the base layer.

[0111] and represents channel noise, n represents additive white Gaussian noise, which obeys complex Gaussian distribution σ 2 represents the noise energy, I N represents the identity matrix corresponding to the number of antennas N.

[0112] In one embodiment, for the terminal device u l,k For example, the beamforming power at the receiving end can be calculated based on the beamforming vector w at the receiving end. l,k Therefore, the beamforming power is expressed as:

[0113] ||w l,k || 2

[0114] In one embodiment, after obtaining the terminal device ul,k Before the corresponding server calculates the power, it needs to obtain the following relevant communication parameters, such as the channel bandwidth B, the amount of user data to be transmitted U l,k , calculation resource parameter f l,k 、Effective capacitance coefficient α l,k And the corresponding channel allocation resource parameter C l,k .

[0115] Among them, the computing resource parameter f l,k For edge servers, for terminal devices u l,k The amount of computing resources allocated can also be called the server computing resource parameter. l,k The energy consumption level used to evaluate the terminal device is a parameter related to the system hardware, which reflects the energy consumption information required by the terminal device to process each bit of data under a specific communication environment and hardware configuration. This parameter can be pre-defined for different types of terminal devices according to actual conditions. The resource allocation parameter is the amount of computing resources required for the terminal device to calculate each bit of data, which can also be a pre-defined parameter value according to actual conditions.

[0116] Next, refer to Figure 3 , Figure 3 It is a flowchart of calculating the server calculation power according to the channel bandwidth of the corresponding channel, the allocation resource parameter, the signal to noise ratio, the calculation resource parameter and the effective capacitance coefficient provided by the embodiment of the present application, which specifically includes the following steps:

[0117] Step 310: Calculate the signal to interference plus noise ratio according to the beamforming vector, channel parameters, and signal transmission power of the receiving end.

[0118] In one embodiment, referring to Figure 4 , Figure 4 This is a flowchart of calculating the signal to interference and noise ratio according to the beamforming vector, channel parameters, and signal transmission power of the receiving end provided by an embodiment of the present application, which specifically includes the following steps:

[0119] Step 410: Calculate the expected signal power according to the beamforming vector, channel parameters, and signal transmission power corresponding to the terminal device.

[0120] In one embodiment, the desired signal power is expressed as:

[0121]

[0122] Here, H represents transpose.

[0123] Step 420: Treat other terminal devices that are located in the same communication layer as the terminal device and have a greater number than the terminal device as same-layer terminal devices, and treat terminal devices that are located in different communication layers from the terminal device as other-layer terminal devices, obtain the same-layer signal power corresponding to each same-layer terminal device, and obtain the other-layer signal power corresponding to each other-layer terminal device.

[0124] In one embodiment, referring to the above For terminal devices u l,k , the other terminal devices after the kth terminal device in the lth layer are regarded as the same-layer terminal devices, that is, the same-layer terminal devices are represented as: u l,i ,k+1≤i≤K l .

[0125] In addition, the terminal devices located at different communication layers from the terminal devices are referred to as other layer terminal devices, so the other layer terminal devices are represented as u 3-l,j , 1≤j≤K 3-l , where 3 means that the communication layer is explained by taking two as an example, K 3-l Indicates the number of terminal devices corresponding to layer 3-1.

[0126] Next, according to the calculation method of the expected signal power, the beamforming vector w l,k The same-layer signal power corresponding to each same-layer terminal device is obtained, so the i-th same-layer signal power is expressed as:

[0127]

[0128] Among them, p l,i represents the signal transmission power corresponding to the i-th terminal device in the l-th layer, h l,i Represents the channel parameters corresponding to the i-th terminal device in the l-th layer.

[0129] It is also necessary to obtain the other layer signal power corresponding to each other layer terminal device. The jth other layer signal power is expressed as:

[0130]

[0131] Where, represents the signal transmission power corresponding to the jth terminal device in the 3-1st layer, h 3-l,j Indicates the channel parameters corresponding to the j-th terminal device in the 3-1st layer.

[0132] Step 430: Accumulate the same-layer signal power to obtain the same-layer interference signal power, accumulate the other-layer signal power to obtain the other-layer interference signal power, and obtain the total interference signal power according to the same-layer interference signal power and the other-layer interference signal power.

[0133] In one embodiment, the same-layer interference signal power is expressed as:

[0134]

[0135] The interference signal power of other layers is expressed as:

[0136]

[0137] The total interference signal is expressed as:

[0138]

[0139] Step 440: Obtain the sum of the total interference signal power and the noise power as the interference noise power, and calculate the ratio of the expected signal power to the interference noise power to obtain the signal to interference noise ratio.

[0140] In one embodiment, the noise power is expressed as:

[0141]

[0142] Therefore, the interference noise power is expressed as:

[0143]

[0144] Terminal equipment l,k The corresponding signal-to-interference-noise ratio γ l,k It is expressed as:

[0145]

[0146] Step 320: add one to the signal to noise ratio, calculate the logarithm, and then multiply it by the channel bandwidth to obtain the communication rate. Calculate the product of the communication rate, the allocation resource parameter, the computing resource parameter, and the effective capacitance coefficient to obtain the server computing power.

[0147] In one embodiment, based on Shannon's theorem, the maximum achievable communication rate of the terminal device is calculated by the signal to interference noise ratio, so the communication rate is expressed as:

[0148] Blog2(1+γ l,k )

[0149] The server computing power is expressed as:

[0150] C l,k Blog2(1+γ l,k )α l,k f l,k

[0151] Step 120: Obtain the target power corresponding to each terminal device according to the server calculation power, signal transmission power and beamforming power of the receiving end, accumulate all target powers to obtain the total power, and minimize the total power to generate the optimization objective function.

[0152] In one embodiment, for the terminal device u l,k , its target power is expressed as:

[0153] C l,k Blog2(1+γ l,k )α l,k f l,k +p l,k +||w l,k || 2

[0154] The total power is expressed as:

[0155]

[0156] Where L represents the number of communication layers.

[0157] Therefore, the optimization objective function is expressed as:

[0158]

[0159] Step 130: Generate a set of constraint conditions based at least on the amount of user data, signal to interference and noise ratio, computing resource parameters and signal transmission power corresponding to each terminal device.

[0160] In one embodiment, referring to Figure 5 , Figure 5 This is a flowchart of generating a constraint condition set based on at least the user data volume, signal to interference noise ratio, computing resource parameters and signal transmission power corresponding to each terminal device provided by an embodiment of the present application, specifically including the following steps:

[0161] Step 510: For each terminal device, generate a delay constraint condition according to the corresponding user data volume, signal to interference and noise ratio, computing resource parameters and signal transmission power.

[0162] In one embodiment, referring to Figure 6 , Figure 6 This is a flowchart of generating a delay constraint condition for each terminal device according to the corresponding user data volume, signal to interference noise ratio, calculation resource parameters and signal transmission power provided by an embodiment of the present application, specifically including the following steps:

[0163] Step 610: For each terminal device, obtain the corresponding communication rate, and obtain the first delay parameter according to the ratio of the user data volume and the communication rate.

[0164] In one embodiment, the terminal device ul,k For example, the first delay parameter is used to represent the delay of data unloading from the terminal device to the edge server. The first delay parameter is expressed as:

[0165]

[0166] Step 620: Obtain a second delay parameter according to the ratio of the user data volume to the computing resource parameter, and calculate the sum of the first delay parameter and the second delay parameter to obtain a total delay parameter corresponding to the terminal device.

[0167] In one embodiment, considering that after the data corresponding to the terminal device is unloaded, the edge server will use part of its computing resources to complete the computing tasks required by the terminal device, and the second delay parameter is used to represent the computing delay of this part. The second delay parameter is expressed as:

[0168]

[0169] Therefore, the terminal device u l,k The total delay parameter is expressed as:

[0170]

[0171] Step 630: Set the total delay parameter of each terminal device to be less than or equal to the delay upper limit value corresponding to the terminal device, and generate a delay constraint condition.

[0172] In one embodiment, the delay constraint is expressed as:

[0173]

[0174] in, Indicates terminal device u l,k The corresponding delay upper limit value can reflect the delay tolerance of different terminal devices. It can be set according to the actual situation. Different delay upper limits can be set for each different type of terminal device, or the same delay upper limit can be set for the same communication layer. This embodiment does not limit this. Generally speaking, taking two communication layers as an example, since the users of the enhanced layer have higher requirements for delay, it can be determined that:

[0175] Step 520: Accumulate the signal transmission power corresponding to each terminal device to obtain the total transmission power, set the total transmission power to be less than or equal to the maximum power value, and generate a transmission power constraint condition.

[0176] In one embodiment, the total transmission power is expressed as:

[0177]

[0178] Therefore, the total transmission power is set to be less than or equal to the maximum power value, and the transmission power of each signal is greater than or equal to zero. The generated transmission power constraint is expressed as:

[0179]

[0180] Among them, P max Indicates the maximum power value.

[0181] Step 530: Accumulate the computing resource parameters corresponding to each terminal device to obtain the total computing resources, set the total computing resources to be less than or equal to the maximum resource value, and generate computing resource constraints.

[0182] In one embodiment, the total computing resources are expressed as:

[0183]

[0184] Therefore, the total computing resources are set to be less than or equal to the maximum resource value, and each computing resource parameter is greater than or equal to zero. The generated computing resource constraint is expressed as:

[0185]

[0186] Step 540: Obtain a constraint set according to the delay constraint, the transmission power constraint and the computing resource constraint.

[0187] In one embodiment, the set of constraints is expressed as:

[0188]

[0189] From the above constraint set and optimization objective function, it can be seen that the optimization goal of the embodiment of the present application is to minimize the power consumed by the distributed system during the transmission process. At the same time, it is necessary to meet the requirements that the delay of each terminal device cannot exceed the delay tolerance upper limit, and the computing resource parameters and signal transmission power must be non-negative, and the sum of each cannot exceed the respective budget upper limit.

[0190] Step 140: Under the premise of satisfying the constraint condition set, solve the optimization objective function to obtain the target transmission parameters of each terminal device in the corresponding channel.

[0191] In one embodiment, the target transmission parameters include target computing resource parameters, target signal transmission power and target beamforming vector. That is, after the process of solving the optimization objective function, the optimal value of the computing resource parameter corresponding to each terminal device is obtained as the target computing resource parameter, the optimal value of the signal transmission power is obtained as the target signal transmission power, and the optimal value of the beamforming vector is obtained as the target beamforming vector.

[0192] In one embodiment, to solve the above optimization problem, the present solution adopts an iterative algorithm, with the help of an alternating optimization framework and a continuous convex approximation method based on a path following process, to achieve the desired optimization goal and thereby obtain the target transmission parameters.

[0193] Specifically, the above-mentioned optimization objective function belongs to a non-convex problem. In this embodiment, the problem is handled by introducing auxiliary variables and proxy functions. On the one hand, auxiliary variables are used to reasonably disassemble and reassemble the originally complex relationship; on the other hand, the proxy function is used to approximate the function form of the original objective function, so that the non-convex problem is converted into a series of convex sub-problems. These convex sub-problems can be solved by solving the Karush-Kuhn-Tucker (KKT) conditions. When specific regularity conditions are met, the KKT condition is a necessary and sufficient condition for the convex optimization problem to obtain the optimal solution. Therefore, when the KKT condition is used to solve each convex sub-problem, the optimal solution of the sub-problem can be ensured. Given that the original non-convex problem has been decomposed into multiple such convex sub-problems, and each convex sub-problem can be optimally solved with the help of the KKT condition, based on this solution method, the algorithm of this embodiment can at least ensure that the optimization result converges to a stationary point of the original constraint condition set.

[0194] In addition, this embodiment uses an alternating optimization framework to split the optimization of each variable, decomposing the complex optimization problem that originally involves multiple variables into several sub-problems that only involve a single or a few variables. Compared with the solution of the original optimization objective function, these sub-problems are more concise in form, and the complexity of the constraints and objective functions are significantly reduced, making them easier to solve.

[0195] The solution process is described in detail below.

[0196] In one embodiment, referring to Figure 7 , Figure 7 The present invention provides a flowchart of solving the optimization objective function under the premise of satisfying the constraint condition set to obtain the target transmission parameters of each terminal device in the corresponding channel, which specifically includes the following steps:

[0197] Step 710: Obtain computing resource optimization items related to the resource allocation parameters from the optimization objective function, solve the computing resource optimization items, and obtain target computing resource parameters.

[0198] In one embodiment, when optimizing the computing resource parameters, other variables, such as the beamforming vector w l,k With signal transmission power p l,k Remain unchanged. Thus, the computing resource optimization item related to the allocation resource parameter is obtained from the optimization objective function, and the computing resource optimization item is used as the computing resource parameter f l,k sub-problem of .

[0199] Among them, the computing resource optimization item is expressed as:

[0200]

[0201] In one embodiment, after the sub-problems are split, the computing resource optimization item is a convex problem, so the optimal solution of the sub-problem can be found by the Carlo-Kuhn-Tucker condition. Figure 8 , Figure 8 The flowchart of solving the computing resource optimization item and obtaining the target computing resource parameter provided by the embodiment of the present application specifically includes the following steps:

[0202] Step 810: Set a first Lagrangian multiplier corresponding to a delay constraint and a second Lagrangian multiplier corresponding to a computing resource constraint.

[0203] In one embodiment, the Lagrangian function L is established, and λ is set l,k is the first Lagrangian multiplier corresponding to the delay constraint, and μ≥0 is the second Lagrangian multiplier corresponding to the computing resource constraint.

[0204] Step 820: Obtain a computing resource Lagrangian function according to the computing resource optimization item, the first Lagrangian multiplier, and the second Lagrangian multiplier.

[0205] In one embodiment, the first Lagrangian multiplier and the second Lagrangian multiplier are substituted into the computing resource optimization item and the corresponding constraint condition to obtain the computing resource Lagrangian function. The computing resource Lagrangian function L1 is expressed as:

[0206]

[0207] Step 830: Calculate partial derivatives of the computing resource Lagrangian function to obtain a first partial derivative function, and solve the first partial derivative function to obtain target computing resource parameters.

[0208] In one embodiment, the Lagrangian function of the computing resource is calculated with respect to the computing resource parameter f l,k The partial derivative of , we get the first partial derivative function, which is expressed as:

[0209]

[0210] Let the first partial derivative function Solve the computing resource parameter f l,k The optimal solution of is:

[0211]

[0212] By transposing the items, we can obtain:

[0213]

[0214] Further we get:

[0215]

[0216] Assumptions And because f l,k ≥0, then:

[0217] The optimal solution formula for computing resource parameters is:

[0218]

[0219] in, as well as The following two equations are satisfied, and the final value can be obtained by binary search.

[0220] Substituting the optimal solution formula of computing resource parameters into the computing resource constraints, we get:

[0221]

[0222] When computing resource parameters to obtain the optimal solution, computing resource constraints take the equal sign, so the following formula can be obtained:

[0223]

[0224] Similarly, substituting the optimal solution formula into the delay constraint, we get:

[0225]

[0226] When computing the optimal solution for resource parameters, the delay constraint condition takes the equal sign, so the following formula can be obtained:

[0227]

[0228] Through the above process, the optimal solution of the computing resource parameters can be obtained as the target computing resource parameters.

[0229] Step 720: Under the constraints of the target computing resource parameters, obtain the transmission optimization items related to the signal transmission power and the beamforming vector from the optimization objective function, solve the transmission optimization items, and obtain the target signal transmission power and the target beamforming vector.

[0230] In one embodiment, the target computing resource parameter has been obtained by the above process, wherein the target computing resource parameter is the optimal solution for the edge server to allocate resources for each terminal device. On this basis, the optimal solution for the signal transmission power and the optimal solution for the beamforming vector corresponding to each channel are calculated. When optimizing the beamforming vector and the signal transmission power, the target computing resource parameter remains unchanged. Therefore, the transmission optimization item related to the signal transmission power and the beamforming vector obtained in the optimization objective function is expressed as:

[0231]

[0232] in, It indicates that a delay reference item corresponding to the signal to interference and noise ratio is generated according to the delay constraint condition.

[0233] It can be understood that the above process obtains the transmission optimization items related to the signal transmission power and the beamforming power from the optimization objective function for unified calculation, and can also split the signal transmission power and the beamforming power for separate calculations according to the division method of the calculation resource optimization items.

[0234] In one embodiment, a path following process is used to provide a convex proxy function that is easy to solve for the function in the sub-problem corresponding to the transmission optimization item, and the proxy function is used to solve the transmission optimization item.

[0235] Reference Fig. 9 , Fig. 9 The flowchart of solving the transmission optimization item and obtaining the target signal transmission power and the target beamforming vector provided by the embodiment of the present application specifically includes the following steps:

[0236] Step 910: Generate a delay reference item corresponding to the signal to interference plus noise ratio according to the delay constraint condition, generate a convex lower bound corresponding to the signal to interference plus noise ratio, and generate a convex upper bound corresponding to the communication rate.

[0237] In one embodiment, by finding log2(1+γ l,k ) and the convex upper bound of γ l,k The convex lower bound of

[0238]

[0239] Substitute the above convex upper bound and convex lower bound into the transport optimization term, and then construct an approximate convex subproblem for the subproblem to facilitate the solution of the problem. Through this operation, the original non-convex optimization problem is transformed into an approximate convex subproblem. Given that convex optimization problems have good properties, such as the local optimal solution is the global optimal solution, this makes the problem-solving process simpler and more efficient. With this approximate processing method, convex optimization algorithms can be used to solve originally complex non-convex optimization problems.

[0240] In one embodiment, two communication layers are used as an example for description. Fig.10 , Fig.10 This is a flowchart of generating a convex lower bound corresponding to a signal to interference noise ratio provided by an embodiment of the present application, which specifically includes the following steps:

[0241] Step 1010: Obtain an auxiliary variable according to the inverse of the signal transmission power, adjust the expected signal power based on the auxiliary variable to obtain a first non-convex function, and adjust the interference noise power according to the auxiliary variable to obtain a first convex function, and use the first convex function and the first non-convex function to update the signal to interference plus noise ratio to obtain a signal to interference plus noise ratio function.

[0242] In one embodiment, the auxiliary variable θ l,k It is expressed as:

[0243]

[0244] The first non-convex function x(θ l,k ,w l,k ) is expressed as:

[0245]

[0246] The first convex function y(θ l,k ,w l,k ) is expressed as:

[0247]

[0248] Then the first convex function and the first non-convex function are used to update the signal to interference noise ratio, and the signal to interference noise ratio function is expressed as:

[0249]

[0250] Step 1020: Obtain a first iteration value corresponding to each iteration according to the first non-convex function, and obtain a second iteration value corresponding to each iteration according to the first convex function.

[0251] In one embodiment, a first iteration value is obtained according to a first non-convex function It is expressed as:

[0252]

[0253] Here, n represents the nth iteration.

[0254] The second iteration value obtained according to the first convex function It is expressed as:

[0255]

[0256] Step 1030: Introduce a first auxiliary convex function, and perform a first-order Taylor expansion on the first auxiliary convex function at positions where the first iteration value is greater than zero and the second iteration value is greater than zero to obtain a first inequality.

[0257] In one embodiment, the first auxiliary convex function f(x, y) is expressed as:

[0258]

[0259] Among them, x>0,y>0.

[0260] At the position where the first iteration value is greater than zero and the second iteration value is greater than zero, the first auxiliary convex function is expanded by the first order Taylor to obtain the first inequality, that is, using f(x, y) at any The first-order Taylor series expansion at gives the first inequality, expressed as:

[0261]

[0262] Step 1040: Perform a first-order Taylor expansion on the first non-convex function to obtain a second inequality, and substitute the first inequality into the second inequality to obtain a convex lower bound corresponding to the signal-to-interference-noise ratio function in each iteration process.

[0263] In one embodiment, a first-order Taylor expansion is performed on the first non-convex function to obtain a second inequality, that is, In any given Performing a first-order Taylor series expansion, the second inequality is expressed as:

[0264]

[0265] in,

[0266] Next, substitute the first inequality into the second inequality to obtain the signal-to-interference-noise ratio function in each iteration process γ l,k (w l,k ,θ l,k ) corresponds to the convex lower bound, expressed as:

[0267]

[0268] In one embodiment, referring to Fig.11 , Fig.11 This is a flowchart for generating a convex upper bound corresponding to a communication rate provided by an embodiment of the present application, which specifically includes the following steps:

[0269] Step 1110: Obtain a second convex function according to the first non-convex function and the first convex function, and obtain the second non-convex function according to the inverse of the first convex function, and use the second convex function and the second non-convex function to update the communication rate to obtain a communication rate function.

[0270] In one embodiment, the second convex function a(θ l,k ,w l,k ) is expressed as:

[0271] a(θ l,k ,w l,k )=x(θ l,k ,w l,k )+y(θ l,k ,w l,k )

[0272] The second non-convex function b(θ l,k ,w l,k ) is expressed as:

[0273]

[0274] Next, the communication rate is updated using the second convex function and the second non-convex function, and the obtained communication rate function is expressed as:

[0275] log2(1+γ l,k (w l,k ,θ l,k ))=log2(a(θ l,k ,w l,k )b(θ l,k ,w l,k ))

[0276] Step 1120: Obtain a third iteration value corresponding to each iteration according to the second non-convex function, and obtain a fourth iteration value corresponding to each iteration according to the second convex function.

[0277] In one embodiment, the third iteration value It is expressed as:

[0278]

[0279] Fourth iteration value It is expressed as:

[0280]

[0281] Step 1130: Introduce a second auxiliary convex function, and perform a first-order Taylor expansion on the second auxiliary convex function at positions where the third iteration value is greater than zero and where the fourth iteration value is greater than zero to obtain a third inequality.

[0282] In one embodiment, the second auxiliary convex function g(a,b) is expressed as:

[0283] g(a,b)=ln(ab)

[0284] Among them, a>0 and b>0.

[0285] At the position where the third iteration value is greater than zero and the fourth iteration value is greater than zero, the first-order Taylor expansion of the second auxiliary convex function is performed to obtain the third inequality, that is, using g(a,b) at any The first-order Taylor series expansion at gives the second inequality, expressed as:

[0286]

[0287] Step 1140: After applying the third inequality to the communication rate function, substitute the third iteration value and the fourth iteration value to obtain the convex upper bound corresponding to the communication rate function in each iteration process.

[0288] In one embodiment, in order to obtain log2(1+γ l,k (w l,k ,θ l,k )) has a convex upper bound, the second inequality after Taylor expansion can be applied to the communication rate function log2(a(θ l,k ,w l,k )b(θ l,k ,w l,k )) and then the third iteration value and the fourth iteration value Substituting in, we get the convex upper bound, expressed as:

[0289]

[0290] Among them, the intermediate quantity M (n) (θ l,k ,w l,k ) is defined as:

[0291]

[0292] Step 920: Update the transmission optimization item based on the convex upper bound to obtain the transmission optimization update item, set the delay reference item to be less than or equal to the convex lower bound, and obtain the reference delay constraint condition.

[0293] In one embodiment, log2(1+γ l,k ) and the convex upper bound of γ l,k After obtaining the convex lower bound of , the transmission optimization term can be approximated. Specifically, the transmission optimization term is updated using the convex upper bound to obtain the transmission optimization update term, and the delay reference term is set to be less than or equal to the convex lower bound to obtain the reference delay constraint condition.

[0294] Therefore, the transmission optimization update term is expressed as:

[0295]

[0296] in, represents the above convex upper bound.

[0297] The reference delay constraint is expressed as:

[0298]

[0299] Step 930: Based on the reference delay constraint and the transmission power constraint, the transmission optimization update term is solved to obtain the target signal transmission power and the target beamforming vector.

[0300] In one embodiment, under the common constraint of the reference delay constraint and the transmission power constraint, the transmission optimization update term is solved to obtain the target signal transmission power and the target beamforming vector, so the common constraint is expressed as:

[0301]

[0302] In one embodiment, since the transmission optimization update term has strong convexity, an interior point method tool is used to solve its optimal solution. Fig.12 , Fig.12 The present invention provides a flowchart for solving the transmission optimization update term based on the reference delay constraint and the transmission power constraint to obtain the target signal transmission power and the target beamforming vector, which specifically includes the following steps:

[0303] Step 1210: Initialize all variables based on the constraint set and obtain target computing resource parameters.

[0304] In one embodiment, referring to Fig.13 , Fig.13 Schematic diagram of the algorithm flow for solving the transmission optimization update item in the embodiment of the present application. Fig.13 , under the premise of the constraint set, initialize all used variables, that is, computing resource parameters Signal transmission power and the beamforming vector at the receiver And obtain the target computing resource parameters calculated previously to update the computing resource parameters f l,k .

[0305] Step 1220: Initialize auxiliary variables to obtain the corresponding initial values ​​of the auxiliary variables in the first iteration process.

[0306] In one embodiment, referring to Fig.13 , initialize the number of iterations n = 0, and initialize the auxiliary variables. The initial values ​​of the auxiliary variables are expressed as:

[0307]

[0308] Step 1230: Enter multiple iterative solution processes, solve the transmission optimization update items according to the auxiliary variable values, beamforming vector values ​​and target computing resource parameters of the current iterative process, obtain iterative auxiliary variable values ​​and iterative beamforming vector values, use the iterative auxiliary variable values ​​as the auxiliary variable values ​​of the next iterative process, and use the iterative beamforming vector values ​​as the beamforming vector values ​​of the next iterative process, and continue the iterative process until the iteration stop condition is reached.

[0309] In one embodiment, multiple iterations are performed. In each iteration, the transmission optimization update term is solved according to the auxiliary variable value, beamforming vector value and target computing resource parameter of the current iteration process using the interior point method to obtain the optimal solution of the current iteration process: the iterative auxiliary variable value θ l ' ,k and the iterative beamforming vector value w l ' ,k That is to say, the interior point method is used to calculate the two solutions corresponding to the transmission optimization update term as the iterative auxiliary variable value and the iterative beamforming vector value. In the first iteration, the auxiliary variable value is the initial value of the auxiliary variable, and the beamforming vector value is the initial value.

[0310] If the iteration is not terminated, the iterative auxiliary variable value is used as the auxiliary variable value of the next iterative process, and the iterative beamforming vector value is used as the beamforming vector value of the next iterative process, that is: The iteration process is continued, and the number of iterations is increased by one until an iteration stop condition is reached. The iteration stop condition here may be reaching a preset number of iterations, or reaching a preset iteration time, etc., which is not limited in this embodiment.

[0311] Step 1240: Obtain the target signal transmission power according to the iterative auxiliary variable value obtained in the last iterative process, and obtain the target beamforming vector according to the iterative beamforming vector value obtained in the last iterative process.

[0312] In one embodiment, after the iteration is terminated, the target signal transmission power is obtained according to the iteration auxiliary variable value obtained in the last iteration process, that is, The iterative beamforming vector value obtained in the last iteration process is used as the target beamforming vector.

[0313] The data transmission method of the distributed system provided in the embodiment of the present application can be used in a virtual reality content distribution network for heterogeneous users with a variety of different service quality requirements. Compared with related technologies, the proposed network can better serve mobile virtual reality users in actual scenarios. When there are a large number of users with different needs in the network, the network architecture can more accurately match the needs of different users and serve more users with limited virtual reality resources. At the same time, the system energy consumption optimization method can make full use of the limited energy resources of edge servers and mobile virtual reality terminal devices to maximize the long-term virtual reality service needs of a large number of concurrent users.

[0314] In one embodiment, referring to Fig.14 , Fig.14 It is a schematic diagram of the convergence performance curve of the data transmission method of the distributed system in the embodiment of the present application. It can be seen that the data transmission method of the distributed system in the embodiment of the present application can converge well in multiple scenarios. Taking two communication layers as an example, the scenarios are: K1=4, K2=1, K1=4 with changed optimization order, K2=1, K1=20, K2=5, and K1=20, K2=5 with changed optimization order. The iterative stop condition of each layer is set as follows: after two consecutive iterations, the relative change of the solution corresponding to the optimization objective function is less than 0.0001. It can be seen that the data transmission method of the distributed system in the embodiment of the present application can achieve rapid convergence within about 10 iterations under different numbers of terminal devices. Moreover, even if the optimization order of each variable under the alternating optimization framework is changed, the data transmission method of the distributed system in the embodiment of the present application can still converge quickly without causing performance loss. This fully demonstrates that the proposed data transmission method for the distributed system can stably and efficiently solve the power optimization problem of the distributed virtual reality system.

[0315] In one embodiment, in order to intuitively demonstrate the impact of the existence of multiple layers of heterogeneous users on network performance, as well as the advantages of the data transmission method of the distributed system of the embodiment of the present application in optimizing the network performance, the proposed distributed virtual reality network is compared with a traditional network without user stratification.

[0316] Reference Fig.15 , Fig.15A schematic diagram of the power consumption of the data transmission method of the distributed system provided in the embodiment of the present application as the number of antennas varies. As shown in the figure, under different numbers of antennas, the network corresponding to the data transmission method of the distributed system provided in the embodiment of the present application has lower power consumption than the traditional network without user stratification. And when the number of users is large, the performance improvement is more significant. These results confirm that the hierarchical network structure designed in the data transmission method of the distributed system provided in the embodiment of the present application can significantly reduce system power consumption, especially in large-scale virtual reality networks containing a large number of users.

[0317] In addition, from Fig.15 It can also be seen that the system power consumption decreases as the number of antennas increases. This is because as the number of antennas increases, the network can obtain more spatial diversity gain, which relaxes the required delay constraint. In this way, the power consumption requirement will be reduced when achieving the same performance level.

[0318] The technical solution provided in the embodiment of the present application is to obtain the signal transmission power and the beamforming power of the receiving end for each terminal device, and calculate the server calculation power according to the channel bandwidth, allocation resource parameters, signal to noise ratio, calculation resource parameters and effective capacitance coefficient of the corresponding channel, and then obtain the target power corresponding to each terminal device according to the server calculation power, signal transmission power and beamforming power of the receiving end, accumulate all target powers to obtain the total power, minimize the total power to generate the optimization objective function, at least based on the user data volume, signal to noise ratio, calculation resource parameters and signal transmission power corresponding to each terminal device to generate a constraint condition set, and finally solve the optimization objective function on the premise of satisfying the constraint condition set to obtain the target transmission parameters of each terminal device in the corresponding channel, and the target transmission parameters include target calculation resource parameters, target signal transmission power and target beamforming vector. The embodiment of the present application first stratifies users according to their data quality requirements. Since different communication layers correspond to different data quality requirements, different resources can be allocated to users with different delay requirements according to the stratification of users. This method can adapt to user needs, and through reasonable stratification and resource allocation, the user's mobile device can avoid unnecessary computing burden while meeting its own needs. In addition, the effective capacitance coefficient is introduced in the process of optimizing transmission parameters to reflect the energy consumption characteristics of mobile devices under different working conditions, so as to more accurately describe the energy consumption of mobile devices during the transmission process. At the same time, the energy consumption of all mobile devices during the transmission process is combined to perform overall optimization modeling, thereby minimizing the total network energy consumption while ensuring the smooth transmission of virtual reality content.

[0319] The present application also provides a data transmission device for a distributed system, which can implement the data transmission method of the distributed system. Fig.16, the device comprises:

[0320] Power calculation module 1610: used to obtain the signal transmission power and the beamforming power of the receiving end for each terminal device, and calculate the server calculation power according to the channel bandwidth, allocation resource parameters, signal-to-interference-noise ratio, calculation resource parameters and effective capacitance coefficient of the corresponding channel.

[0321] Optimization target construction module 1620: used to obtain the target power corresponding to each terminal device according to the server calculation power, signal transmission power and beamforming power of the receiving end, accumulate all target powers to obtain the total power, and minimize the total power to generate the optimization target function.

[0322] The constraint condition building module 1630 is used to generate a constraint condition set based on at least the user data volume, signal to interference and noise ratio, computing resource parameters and signal transmission power corresponding to each terminal device.

[0323] Optimization solution module 1640: used to solve the optimization objective function under the premise of satisfying the constraint condition set, and obtain the target transmission parameters of each terminal device in the corresponding channel. The target transmission parameters include target computing resource parameters, target signal transmission power and target beamforming vector.

[0324] The specific implementation of the data transmission device of the distributed system of this embodiment is basically the same as the specific implementation of the data transmission method of the distributed system described above, and will not be repeated here.

[0325] The present application also provides an electronic device, including:

[0326] at least one memory;

[0327] at least one processor;

[0328] at least one program;

[0329] The program is stored in the memory, and the processor executes the at least one program to implement the data transmission method of the distributed system implemented in the present application. The electronic device can be any intelligent terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), a car computer, etc.

[0330] See also Fig.17 , Fig.17 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0331] The processor 1701 may be implemented by a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0332] The memory 1702 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1702 can store an operating system and other applications. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1702, and the processor 1701 calls and executes the data transmission method of the distributed system of the embodiment of the present application;

[0333] Input / output interface 1703, used to implement information input and output;

[0334] Communication interface 1704, used to realize communication interaction between the device and other devices, which can be realized by wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.); and

[0335] A bus 1705 that transmits information between the various components of the device (e.g., the processor 1701, the memory 1702, the input / output interface 1703, and the communication interface 1704);

[0336] The processor 1701 , the memory 1702 , the input / output interface 1703 and the communication interface 1704 are connected to each other in communication within the device via the bus 1705 .

[0337] An embodiment of the present application further provides a storage medium, which is a storage medium storing a computer program. When the computer program is executed by a processor, the data transmission method of the above-mentioned distributed system is implemented.

[0338] As a non-transient storage medium, the memory can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely set relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0339] The data transmission method, device, equipment and storage medium of the distributed system proposed in the embodiment of the present application obtain the signal transmission power and the beamforming power of the receiving end for each terminal device, and calculate the server calculation power according to the channel bandwidth, allocation resource parameter, signal to noise ratio, calculation resource parameter and effective capacitance coefficient of the corresponding channel, and then obtain the target power corresponding to each terminal device according to the server calculation power, signal transmission power and beamforming power of the receiving end, accumulate all target powers to obtain the total power, minimize the total power to generate the optimization objective function, generate a constraint condition set based on at least the user data volume, signal to noise ratio, calculation resource parameter and signal transmission power corresponding to each terminal device, and finally solve the optimization objective function on the premise of satisfying the constraint condition set to obtain the target transmission parameters of each terminal device in the corresponding channel, and the target transmission parameters include target calculation resource parameters, target signal transmission power and target beamforming vector. The embodiment of the present application first stratifies users according to their data quality requirements. Since different communication layers correspond to different data quality requirements, different resources can be allocated to users with different delay requirements according to the stratification of users. This method can adapt to user needs and, through reasonable stratification and resource allocation, enable users' mobile devices to avoid unnecessary computing burdens while meeting their own needs. In addition, the effective capacitance coefficient is introduced in the process of optimizing transmission parameters to reflect the energy consumption characteristics of mobile devices under different working conditions, so as to more accurately describe the energy consumption of mobile devices during transmission. At the same time, the energy consumption of all mobile devices during the transmission process is combined for overall optimization modeling, so as to minimize the total network energy consumption while ensuring the smooth transmission of virtual reality content.

[0340] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0341] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0342] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0343] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0344] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0345] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0346] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0347] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0348] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0349] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store programs.

[0350] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A data transmission method for a distributed system, characterized in that: The distributed system includes at least an edge server and multiple communication layers, each of the communication layers includes at least one terminal device, different communication layers have different data quality requirements, the edge server establishes a channel with each of the terminal devices through a wireless access point, and the method includes: For each of the terminal devices, the signal transmission power and the beamforming power of the receiving end are obtained, and the server calculation power is calculated according to the channel bandwidth, the allocation resource parameter, the signal to interference noise ratio, the calculation resource parameter and the effective capacitance coefficient of the corresponding channel; Obtaining a target power corresponding to each of the terminal devices according to the server calculation power, the signal transmission power, and the beamforming power of the receiving end, accumulating all the target powers to obtain a total power, and minimizing the total power to generate an optimization objective function; Generate a set of constraint conditions based at least on the amount of user data corresponding to each of the terminal devices, the signal to interference and noise ratio, the computing resource parameter, and the signal transmission power; On the premise of satisfying the constraint condition set, the optimization objective function is solved to obtain the target transmission parameters of each terminal device in the corresponding channel, wherein the target transmission parameters include target computing resource parameters, target signal transmission power and target beamforming vector.

2. The data transmission method of a distributed system according to claim 1, characterized in that: The server calculation power is calculated according to the channel bandwidth of the corresponding channel, the allocation resource parameter, the signal to interference noise ratio, the calculation resource parameter and the effective capacitance coefficient, including: Calculating a signal to interference plus noise ratio according to a beamforming vector of a receiving end, channel parameters, and the signal transmission power; The communication rate is obtained by adding one to the signal to interference noise ratio and calculating the logarithm, and then multiplying the result by the channel bandwidth. The product of the communication rate, the allocation resource parameter, the computing resource parameter and the effective capacitance coefficient is calculated to obtain the server computing power.

3. The data transmission method of a distributed system according to claim 2, characterized in that: The terminal devices corresponding to the same communication layer are sorted and numbered from high to low according to the channel gain, and the signal to interference noise ratio is calculated according to the beamforming vector of the receiving end, the channel parameter, and the signal transmission power, including: Calculate the expected signal power according to the beamforming vector, the channel parameter, and the signal transmission power corresponding to the terminal device; Other terminal devices located in the same communication layer as the terminal device and with a number greater than that of the terminal device are regarded as same-layer terminal devices, and terminal devices located in different communication layers from the terminal device are regarded as other-layer terminal devices, and the same-layer signal power corresponding to each of the same-layer terminal devices is obtained, and the other-layer signal power corresponding to each of the other-layer terminal devices is obtained; Accumulating the same-layer signal powers to obtain the same-layer interference signal power, accumulating the other-layer signal powers to obtain the other-layer interference signal power, and obtaining the total interference signal power according to the same-layer interference signal power and the other-layer interference signal power; The sum of the total interference signal power and the noise power is acquired as the interference noise power, and the ratio of the expected signal power to the interference noise power is calculated to obtain the signal to interference noise ratio.

4. The data transmission method of a distributed system according to claim 3, characterized in that: The generating of the constraint condition set based at least on the amount of user data corresponding to each of the terminal devices, the signal to interference and noise ratio, the computing resource parameter, and the signal transmission power comprises: For each of the terminal devices, generating a delay constraint condition according to the corresponding user data volume, the signal to interference and noise ratio, the computing resource parameter, and the signal transmission power; Accumulating the signal transmission power corresponding to each of the terminal devices to obtain a total transmission power, setting the total transmission power to be less than or equal to a maximum power value, and generating a transmission power constraint condition; Accumulating the computing resource parameters corresponding to each of the terminal devices to obtain a total computing resource, setting the total computing resource to be less than or equal to a maximum resource value, and generating a computing resource constraint condition; The constraint condition set is obtained according to the delay constraint condition, the transmission power constraint condition and the computing resource constraint condition.

5. The data transmission method of a distributed system according to claim 4, characterized in that: The generating, for each of the terminal devices, a delay constraint condition according to the corresponding user data volume, the signal to interference and noise ratio, the computing resource parameter, and the signal transmission power comprises: For each of the terminal devices, the corresponding communication rate is obtained, and a first delay parameter is obtained according to a ratio of the user data volume to the communication rate; Obtaining a second delay parameter according to a ratio of the user data volume to the computing resource parameter, and calculating the sum of the first delay parameter and the second delay parameter to obtain a total delay parameter corresponding to the terminal device; The total delay parameter of each terminal device is set to be less than or equal to the delay upper limit value corresponding to the terminal device, and the delay constraint condition is generated.

6. The data transmission method of a distributed system according to claim 4, characterized in that: Under the premise of satisfying the constraint condition set, solving the optimization objective function to obtain the target transmission parameters of each terminal device in the corresponding channel includes: Acquire computing resource optimization items related to the allocated resource parameters from the optimization objective function, solve the computing resource optimization items, and obtain the target computing resource parameters; Under the constraint of the target computing resource parameters, a transmission optimization item related to the signal transmission power and the beamforming vector is obtained from the optimization objective function, and the transmission optimization item is solved to obtain the target signal transmission power and the target beamforming vector.

7. The data transmission method of a distributed system according to claim 6, characterized in that: The step of solving the computing resource optimization item to obtain the target computing resource parameter includes: Setting a first Lagrangian multiplier corresponding to the time delay constraint and a second Lagrangian multiplier corresponding to the computing resource constraint; Obtaining a computing resource Lagrangian function according to the computing resource optimization item, the first Lagrangian multiplier, and the second Lagrangian multiplier; Partial derivatives of the computing resource Lagrangian function are calculated to obtain a first partial derivative function, and the first partial derivative function is solved to obtain the target computing resource parameter.

8. The data transmission method of a distributed system according to claim 6, characterized in that: The solving the transmission optimization item to obtain the target signal transmission power and the target beamforming vector includes: generating a delay reference item corresponding to the signal to interference plus noise ratio according to the delay constraint, generating a convex lower bound corresponding to the signal to interference plus noise ratio, and generating a convex upper bound corresponding to the communication rate; Based on the convex upper bound, the transmission optimization item is updated to obtain a transmission optimization update item, and the delay reference item is set to be less than or equal to the convex lower bound to obtain a reference delay constraint condition; Based on the reference delay constraint and the transmission power constraint, the transmission optimization update term is solved to obtain the target signal transmission power and the target beamforming vector.

9. The data transmission method of a distributed system according to claim 8, characterized in that: The generating a convex lower bound corresponding to the signal to interference plus noise ratio includes: Obtaining an auxiliary variable according to the inverse of the signal transmission power, adjusting the expected signal power based on the auxiliary variable to obtain a first non-convex function, and adjusting the interference noise power according to the auxiliary variable to obtain a first convex function, and updating the signal to interference plus noise ratio using the first convex function and the first non-convex function to obtain a signal to interference plus noise ratio function; Obtaining a first iteration value corresponding to each iteration according to the first non-convex function, and obtaining a second iteration value corresponding to each iteration according to the first convex function; Introducing a first auxiliary convex function, and performing a first-order Taylor expansion on the first auxiliary convex function at a position where the first iteration value is greater than zero and the second iteration value is greater than zero to obtain a first inequality; Performing a first-order Taylor expansion on the first non-convex function to obtain a second inequality, substituting the first inequality into the second inequality to obtain the convex lower bound corresponding to the signal to interference noise ratio function in each iteration process.

10. The data transmission method of a distributed system according to claim 9, characterized in that: The generating a convex upper bound corresponding to the communication rate includes: Obtaining a second convex function according to the first non-convex function and the first convex function, and obtaining a second non-convex function according to the inverse of the first convex function, and updating the communication rate using the second convex function and the second non-convex function to obtain a communication rate function; Obtaining a third iteration value corresponding to each iteration according to the second non-convex function, and obtaining a fourth iteration value corresponding to each iteration according to the second convex function; Introducing a second auxiliary convex function, and performing a first-order Taylor expansion on the second auxiliary convex function at a position where the third iteration value is greater than zero and the fourth iteration value is greater than zero to obtain a third inequality; After applying the third inequality to the communication rate function, substituting the third iteration value and the fourth iteration value into the third iteration value, the convex upper bound corresponding to the communication rate function in each iteration process is obtained.

11. The data transmission method of a distributed system according to claim 9, characterized in that: The step of solving the transmission optimization update term based on the reference delay constraint condition and the transmission power constraint condition to obtain the target signal transmission power and the target beamforming vector includes: Based on the constraint set, all variables are initialized, and the target computing resource parameters are obtained; Initializing the auxiliary variables to obtain the initial values ​​of the auxiliary variables corresponding to the first iteration process; Entering multiple iterative solution processes, solving the transmission optimization update item according to the auxiliary variable value, beamforming vector value and the target computing resource parameter of the current iterative process, obtaining iterative auxiliary variable value and iterative beamforming vector value, using the iterative auxiliary variable value as the auxiliary variable value of the next iterative process, and using the iterative beamforming vector value as the beamforming vector value of the next iterative process, and continuing the iterative process until the iteration stop condition is reached; The target signal transmission power is obtained according to the iterative auxiliary variable value obtained in the last iterative process, and the target beamforming vector is obtained according to the iterative beamforming vector value obtained in the last iterative process.

12. A data transmission device for a distributed system, characterized in that: The distributed system includes at least an edge server and multiple communication layers, each of the communication layers includes at least one terminal device, different communication layers have different data quality requirements, the edge server establishes a channel with each of the terminal devices through a wireless access point, and the device includes: Power calculation module: used to obtain the signal transmission power and the beamforming power of the receiving end for each of the terminal devices, and calculate the server calculation power according to the channel bandwidth, allocation resource parameters, signal to interference noise ratio, calculation resource parameters and effective capacitance coefficient of the corresponding channel; An optimization target construction module is used to obtain a target power corresponding to each of the terminal devices according to the server calculation power, the signal transmission power and the beamforming power of the receiving end, accumulate all the target powers to obtain a total power, and minimize the total power to generate an optimization target function; A constraint condition building module: used to generate a constraint condition set based on at least the amount of user data corresponding to each of the terminal devices, the signal to interference and noise ratio, the computing resource parameter and the signal transmission power; Optimization solution module: used to solve the optimization objective function on the premise of satisfying the constraint condition set, and obtain the target transmission parameters of each terminal device in the corresponding channel, wherein the target transmission parameters include target computing resource parameters, target signal transmission power and target beamforming vector.

13. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the data transmission method of the distributed system according to any one of claims 1 to 11 when executing the computer program.

14. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the data transmission method of a distributed system according to any one of claims 1 to 11 is implemented.

Images