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

Abstract

Embodiments of the present application provide a data transmission method and device of a distributed system, equipment and a storage medium, and relate to the technical field of communication. The method comprises: obtaining server computing power related to the energy consumption of each terminal device, and then obtaining target power according to the server computing power, signal transmission power and beamforming power of the receiving end; accumulating all target powers to obtain total power; minimizing the total power to generate an optimization objective function; solving the optimization objective function under the premise of meeting a constraint condition set to obtain target transmission parameters of each terminal device on a corresponding channel. Users are stratified according to data requirements, and different resources are allocated to users with different delay requirements, so that the mobile devices of the users can meet their own requirements while avoiding unnecessary computing burden. The overall optimization modeling is performed in combination with the energy consumption of all mobile devices in the transmission process, so that the total network energy consumption is reduced on the basis of ensuring smooth transmission.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication, and particularly relates to a data transmission method and device of a distributed system, equipment and a storage medium. BACKGROUND

[0002] Wireless virtual reality, as an important application of the fifth generation network, can provide immersive experience and is widely applied in scenes such as performance, education and game. The transmission of virtual reality video requires ultra-high resolution and ultra-low delay tolerance, which is accompanied by a huge amount of data transmission and processing. Therefore, the demand for computing power of virtual reality devices, especially mobile devices, presents an explosive growth.

[0003] In the related art, mobile edge computing is used to assist the transmission of virtual reality video. Through the deployment of edge servers around the user, the computing power of the access point is improved to support real-time and large amount of computing-intensive business. However, in this way, the mobile device still needs to perform a large amount of computing process, which leads to a large energy consumption of the mobile device, and thus reduces the performance of the wireless virtual reality network. SUMMARY

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

[0005] To achieve the above purpose, a first aspect of the embodiments of the present application provides a data transmission method of a distributed system, the distributed system at least including an edge server and a plurality of communication layers, each communication layer including at least one terminal device, the data quality requirements of different communication layers being different, the edge server establishing a channel between each terminal device and a wireless access point, and the method comprising:

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

[0007] The target power corresponding to each terminal device is obtained according to the server computing power, the signal transmission power and the beamforming power of the receiving end, the total power is obtained by accumulating all the target powers, and the optimization target function is generated by minimizing the total power;

[0008] At least based on the user data amount corresponding to each terminal device, the signal-to-interference noise ratio, the computing resource parameter and the signal transmission power, a constraint condition set is generated;

[0009] Solving the optimization objective function under the premise of meeting the constraint condition set, obtaining a target transmission parameter of each terminal device on a corresponding channel, the target transmission parameter including a target computing resource parameter, a target signal transmission power and a target beamforming vector.

[0010] In some embodiments, the server computing power is calculated according to the channel bandwidth of the corresponding channel, the allocation resource parameter, the signal-to-interference-and-noise ratio, the computing resource parameter and the effective capacitance coefficient, including:

[0011] The signal-to-interference-and-noise ratio is calculated according to the beamforming vector of the receiving end, the channel parameter and the signal transmission power;

[0012] The communication rate is obtained by multiplying the logarithm of the signal-to-interference-and-noise ratio plus one by the channel bandwidth, and 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 according to the channel gain from high to low, and the signal-to-interference-and-noise ratio is calculated according to the beamforming vector of the receiving end, the channel parameter and the signal transmission power, including:

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

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

[0016] The same-layer interference signal power is obtained by accumulating the same-layer signal power, the other-layer interference signal power is obtained by accumulating the other-layer signal power, and the total interference signal power is obtained 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 taken 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-and-noise ratio.

[0018] In some embodiments, the constraint condition set is generated at least based on the user data amount corresponding to each terminal device, the signal-to-interference-and-noise ratio, the computing resource parameter and the signal transmission power, including:

[0019] generate, for each terminal device, a delay constraint condition according to the corresponding user data amount, the signal-to-interference-and-noise ratio, the computing resource parameter, and the signal transmission power;

[0020] accumulate the signal transmission power corresponding to each terminal device to obtain a total transmission power, and set the total transmission power to be less than or equal to a maximum power value to generate a transmission power constraint condition;

[0021] accumulate the computing resource parameter corresponding to each terminal device to obtain a total computing resource, and set the total computing resource to be less than or equal to a maximum resource value to generate a computing resource constraint condition;

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

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

[0024] for each terminal device, obtaining a corresponding communication rate, and obtaining a first delay parameter according to a ratio of the user data amount and the communication rate;

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

[0026] setting the total delay parameter of each terminal device to be less than or equal to an upper limit value of a delay corresponding to the terminal device to generate the delay constraint condition.

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

[0028] obtaining, from the optimization objective function, a computing resource optimization item related to the allocated resource parameter, and solving the computing resource optimization item to obtain the target computing resource parameter;

[0029] under the constraint of the target computing resource parameter, obtaining, from the optimization objective function, a transmission optimization item related to the signal transmission power and the beamforming vector, and solving the transmission optimization item to obtain the target signal transmission power and the target beamforming vector.

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

[0031] setting a first Lagrange multiplier corresponding to the latency constraint and a second Lagrange multiplier corresponding to the computing resource constraint;

[0032] obtaining a computing resource Lagrange function according to the computing resource optimization term, the first Lagrange multiplier and the second Lagrange multiplier;

[0033] calculating a partial derivative of the computing resource Lagrange function to obtain a first partial derivative function, and solving the first partial derivative function to obtain the target computing resource parameter.

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

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

[0036] updating the transmission optimization term based on the convex upper bound to obtain a transmission optimization update term, setting the latency reference term to be less than or equal to the convex lower bound to obtain a reference latency constraint;

[0037] solving the transmission optimization update term based on the reference latency constraint and the transmission power constraint to obtain the target signal transmission power and the target beamforming vector.

[0038] In some embodiments, the generating the convex lower bound corresponding to the signal-to-interference-and-noise ratio comprises:

[0039] obtaining an auxiliary variable according to an inverse of the signal transmission power, adjusting the expected signal power based on the auxiliary variable to obtain a first non-convex function, adjusting the interference noise power according to the auxiliary variable to obtain a first convex function, and updating the signal-to-interference-and-noise ratio using the first convex function and the first non-convex function to obtain a signal-to-interference-and-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 to obtain a first inequality at a position where the first iteration value is greater than zero and the second iteration value is greater than zero;

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

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

[0044] obtaining a second convex function according to the first non-convex function and the first convex function, and a second non-convex function according to the reciprocal of the first convex function, and updating the communication rate by 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 a fourth iteration value corresponding to each iteration according to the second convex function;

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

[0047] applying the third inequality to the communication rate function, and substituting the third iteration value and the fourth iteration value to obtain the convex upper bound of the communication rate function corresponding to each iteration process.

[0048] In some embodiments, the 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 comprises:

[0049] initializing all variables based on the constraint condition set, and obtaining the target computing resource parameter;

[0050] initializing the auxiliary variable to obtain an auxiliary variable initial value corresponding to a first iteration process;

[0051] entering a multiple iteration solving process, solving the transmission optimization update term based on the auxiliary variable value, the beamforming vector value and the target computing resource parameter of the current iteration process to obtain an iteration auxiliary variable value and an iteration beamforming vector value, taking the iteration auxiliary variable value as the auxiliary variable value of the next iteration process, and taking the iteration beamforming vector value as the beamforming vector value of the next iteration process, and continuing the iteration process until an iteration stopping condition is reached;

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

[0053] To achieve the above object, a second aspect of the embodiment of the present application provides a data transmission device of a distributed system, the distributed system comprising at least an edge server and a plurality of communication layers, each of the communication layers comprising at least one terminal device, the data quality requirements of different communication layers being different, the edge server establishing a channel between each of the terminal devices and a wireless access point, and the device comprising:

[0054] a power calculation module configured to acquire, for each of the terminal devices, a signal transmission power and a beamforming power of a receiving end, and calculate a server calculation power according to a channel bandwidth, an allocation resource parameter, a signal-to-interference-noise ratio, a calculation resource parameter and an effective capacitance coefficient of a corresponding channel;

[0055] an optimization target construction module configured to acquire 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 construction module configured to generate a constraint condition set based at least on a user data volume corresponding to each of the terminal devices, the signal-to-interference-noise ratio, the calculation resource parameter and the signal transmission power;

[0057] an optimization solution module configured to solve the optimization target function under the premise of satisfying the constraint condition set to obtain a target transmission parameter of each of the terminal devices on the corresponding channel, the target transmission parameter comprising a target calculation resource parameter, a target signal transmission power and a target beamforming vector.

[0058] To achieve the above object, a third aspect of the embodiment of the present application provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor realizing the method of the first aspect when executing the computer program.

[0059] To achieve the above object, a fourth aspect of the embodiment of the present application provides a storage medium, the storage medium being a storage medium, the storage medium storing a computer program, and the computer program realizing the method of the first aspect when executed by a processor.

[0060] The data transmission method, device and equipment of the distributed system and the storage medium provided by the embodiments of the present application are as follows: for each terminal device, the signal transmission power and the beamforming power of the receiving end are obtained, the server computing power is calculated according to the channel bandwidth, the allocation resource parameter, the signal-to-interference-noise ratio, the computing resource parameter and the effective capacitance coefficient of the corresponding channel, the target power corresponding to each terminal device is obtained according to the server computing power, the signal transmission power and the beamforming power of the receiving end, the total power is obtained by accumulating all the target powers, the optimization target function is generated by minimizing the total power, the constraint condition set is generated based on at least the user data amount, the signal-to-interference-noise ratio, the computing resource parameter and the signal transmission power corresponding to each terminal device, and finally the optimization target function is solved under the premise of meeting the constraint condition set to obtain the target transmission parameter of each terminal device on the corresponding channel, and the target transmission parameter includes the target computing resource parameter, the target signal transmission power and the target beamforming vector. The embodiments of the present application first stratify users according to the data quality requirements of the users. 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 the users. This way can adapt to user requirements, and through reasonable stratification and resource allocation, the mobile device of the user can meet its own requirements while avoiding unnecessary computing burden. In addition, the effective capacitance coefficient is introduced in the process of optimizing the transmission parameter to reflect the energy consumption characteristics of the mobile device in different working states, so as to more accurately describe the energy consumption of the mobile device in the transmission process. At the same time, the overall optimization modeling is carried out in combination with the energy consumption of all mobile devices in the transmission process, so as to maximize the reduction of the total network energy consumption on the basis of ensuring the smoothness of the virtual reality content transmission. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 is a schematic diagram of a distributed system provided by the embodiments of the present application.

[0062] Figure 2 is a flowchart of a data transmission method of a distributed system provided by the embodiments of the present application.

[0063] Figure 3 is a flowchart of calculating the server computing power according to the channel bandwidth, the allocation resource parameter, the signal-to-interference-noise ratio, the computing resource parameter and the effective capacitance coefficient of the corresponding channel provided by the embodiments of the present application.

[0064] Figure 4 is a flowchart of calculating the signal-to-interference-noise ratio according to the beamforming vector of the receiving end, the channel parameter and the signal transmission power provided by the embodiments of the present application.

[0065] Figure 5 is a flowchart of generating a constraint condition set based on at least the user data amount, the signal-to-interference-noise ratio, the computing resource parameter and the signal transmission power corresponding to each terminal device provided by the embodiments of the present application.

[0066] Figure 6 is a flowchart provided by the embodiment of the present application for generating a time delay constraint condition for each terminal device according to corresponding user data volume, signal-to-interference-and-noise ratio, calculation resource parameter and signal transmission power.

[0067] Figure 7 is a flowchart provided by the embodiment of the present application for solving an optimization objective function under the premise of meeting a constraint condition set, to obtain a target transmission parameter of each terminal device on a corresponding channel.

[0068] Figure 8 is a flowchart provided by the embodiment of the present application for solving a calculation resource optimization item to obtain a target calculation resource parameter.

[0069] Figure 9 is a flowchart provided by the embodiment of the present application for solving a transmission optimization item to obtain a target signal transmission power and a target beamforming vector.

[0070] Figure 10 is a flowchart provided by the embodiment of the present application for generating a convex lower bound corresponding to a signal-to-interference-and-noise ratio.

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

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

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

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

[0075] Figure 15 is a diagram showing that power consumption of a data transmission method of a distributed system provided by the embodiment of the present application varies with the number of antennas.

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

[0077] Figure 17 is a hardware structure diagram of an electronic device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

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

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

[0081] First, the meanings of several terms involved in the present application are analyzed:

[0082] Convex function: for a function f(x), if for any two points x1 and x2 in the domain 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 g(x)≤f(x) for all x in the domain, then g(x) is the convex lower bound of f(x).

[0084] With the continuous evolution of wireless networks from the fifth generation to the sixth generation, it is increasingly able to meet the requirements of low energy consumption and low delay for real-time mobile applications of massive devices. Virtual reality is a typical application example under this trend. Wireless virtual reality, as one of the important applications of the fifth generation network, can provide immersive experience for users and is widely used in many scenarios such as performance, education and games. The transmission of virtual reality video needs to have ultra-high resolution and ultra-low delay tolerance, which will inevitably be accompanied by massive data transmission and processing. Therefore, the demand for computing power of virtual reality devices, especially mobile devices, presents an explosive growth trend.

[0085] Moreover, in virtual reality applications, the display device, such as a head-mounted display, is frequently moved with the user's line of sight and motion status. In order to bring a good experience to the user, the virtual reality content needs to track the user's field of view or camera position in real time, and then frequently update. This undoubtedly further aggravates the burden of data transmission, and also exacerbates the delay and energy consumption problems of the virtual reality network. Given the limited resources of mobile devices and the extreme sensitivity of virtual reality applications to delay, it is obviously impractical to completely entrust the heavy computing work to the user's virtual reality device, which requires low delay and high performance to support virtual reality video transmission.

[0086] The applicant finds that in the related art, mobile edge computing is usually used to assist the transmission of virtual reality video. Specifically, by deploying edge servers around the user, the computing power of the access point is improved to support real-time and large amount of computing-intensive services. Due to the almost stringent requirement of virtual reality applications for fluency and the extremely low tolerance for delay, the delay is only about 20 milliseconds. Therefore, in the related art, more attention is often paid to the delay problem of virtual reality video transmission, and the consideration of energy consumption is relatively insufficient. However, even if mobile edge computing can bear most of the computing requirements, in this way, the mobile device still needs to perform part of the computing process, which causes the mobile device to generate more energy consumption. Especially when applied to wireless communication in a specific environment, such as a super large-scale Internet of Things scenario, the increase of data offloading energy consumption will cause the network delay to be significantly improved, thereby reducing the performance of the wireless virtual reality network.

[0087] In the virtual reality application scenario, the energy consumption of the user's mobile device has a relatively intuitive impact on the user experience. Because the rendering of virtual reality is a computing-intensive operation, it not only requires strong computing power, but also consumes a large amount of energy. However, most mobile devices have limited battery capacity, and excessive energy consumption will make it difficult for the mobile device to support long-term virtual reality experience, which will obviously reduce the user experience.

[0088] Based on this, the embodiment of the application provides a data transmission method, device and equipment of a distributed system and a storage medium. First, users are layered 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 layering of the users. This method can adapt to user requirements, and through reasonable layering and resource allocation, the mobile device of the user can meet its own requirements while avoiding unnecessary computing burden. In addition, an effective capacitance coefficient is introduced in the process of optimizing the transmission parameters to reflect the energy consumption characteristics of the mobile device in different working states, so as to more accurately describe the energy consumption of the mobile device in the transmission process. At the same time, the energy consumption of all mobile devices in the transmission process is combined for overall optimization modeling, thereby minimizing the total network energy consumption on the basis of ensuring the smoothness of virtual reality content transmission.

[0089] The embodiment of the application provides a data transmission method, device and equipment of a distributed system and a storage medium, which are specifically described as follows. First, a data transmission method of a distributed system in the embodiment of the application is described.

[0090] The data transmission method of the distributed system provided by the embodiment of the application relates to the technical field of communication. The data transmission method of the distributed system provided by the embodiment of the application can be applied to a terminal, can be applied to a server, and can also be a computer program running in the terminal or the server. For example, the computer program can be a native program or a software module in an operating system; can be a native application program (APP), that is, a program that needs to be installed in an operating system to run, such as a client supporting data transmission of a distributed system, that is, a program that can run only after being downloaded into a browser environment; and can also be a small program that can be embedded into any APP. In summary, the above computer program can be any form of application program, module or plug-in. The terminal communicates with the server through a network. The data transmission method of the distributed system can be executed by the terminal or the server, or can be executed by the terminal and the server cooperatively.

[0091] In some embodiments, the terminal can be a smartphone, a tablet computer, a notebook computer, a desktop computer, a smart watch, or the like. In addition, the terminal can also be a smart vehicle device. The smart vehicle device applies the data transmission method of the distributed system of the embodiments to provide related services and improve the driving experience. The server can be a standalone server, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and basic cloud computing services such as big data and artificial intelligence platforms; or a service node in a blockchain system, the service nodes in the blockchain system form a peer-to-peer (P2P) network, and the P2P protocol is an application layer protocol running on the transmission control protocol (TCP) protocol. The terminal and the server can be connected through a communication connection mode such as Bluetooth, universal serial bus (USB), or a network, and the embodiments are not limited herein.

[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 electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. 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, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment, in which tasks are performed by remote processing devices connected by a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0093] First, the distributed system of the embodiments of the present application is introduced below.

[0094] Referring to Figure 1 , Figure 1 is a schematic diagram of the distributed system provided by the embodiments of the present application. In Figure 1In the distributed system shown in the figure, at least an edge server, a wireless access point and a plurality of communication layers are included. Each communication layer includes at least one user, and each user can correspond to one or more terminal devices. At this time, the edge server establishes a channel with each terminal device through 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 of signal transmission.

[0095] Among them, the edge server is used to provide additional computing power support for the terminal device within a short distance range, thereby providing virtual reality content for the terminal device with lower delay. 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, and in the following embodiment, N antennas will be taken as an example for description.

[0096] In an embodiment, the users of different communication layers have different requirements for data quality, which can be embodied as requirements for delay. By dividing the users into different communication layers, the distributed system can allocate differentiated resources to users with different delay requirements according to the hierarchical status of the users.

[0097] For example, for delay-sensitive users, the system allocates more network bandwidth, better communication links and other resources to them to ensure that they can have a smooth experience. For users with relatively low delay requirements, the system allocates relatively few resources, which can effectively improve the utilization rate of network resources. This resource allocation method not only avoids waste of resources, but also ensures that each user can obtain services that meet their own needs.

[0098] In addition, since the computing resources of mobile devices are usually limited, if a device performs a large number of additional computing tasks in order to meet excessively high data quality requirements, it is likely to cause energy consumption problems such as device heating, battery life shortening, and even affect the normal operation of the device. In this embodiment, through reasonable hierarchical division and resource allocation strategy, the user's mobile device can avoid unnecessary computing burden on the premise of meeting its own needs, thereby improving the stability and service life of the device.

[0099] In an embodiment, the number of communication layers can be L, and the users of the lth communication layer correspond to K l terminal devices. Usually, the number of mobile terminal devices in the communication layer with high delay requirement is less than that in the communication layer with low delay requirement.

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

[0101] It can be understood that, although Figure 1 the embodiments below illustrate two communication layers, this does not mean that the number of communication layers is limited to two.

[0102] The data transmission method of the distributed system in the embodiments of the present application will be described below in combination with the distributed system in Figure 1

[0103] Figure 2 is an optional flowchart of the data transmission method of the distributed system provided in the embodiments of the present application, Figure 2 The method in the figure can include, but is not limited to, steps 110 to 140. It can be understood that the order of steps 110 to 140 in Figure 2 is not specifically limited in the embodiments, 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, the signal transmission power and the beamforming power of the receiving end are obtained, and the server computing power is calculated according to the channel bandwidth, the allocated resource parameters, the signal-to-interference-and-noise ratio, the computing resource parameters, and the effective capacitance coefficient of the corresponding channel.

[0105] In an embodiment, for the k-th terminal device u l,k in the l-th layer, the channel parameter corresponding to the channel is defined as h l,k , which can be a channel gain or a channel frequency response, etc. The signal transmission power is p l,k , and the beamforming vector of the receiving end is w l,k . It is assumed that the signal related to virtual reality transmitted by the terminal device in the network is represented by a complex number s l,k , and the signal has unit energy, i.e.,

[0106] Therefore, the signal received by the edge server about the k-th terminal device in the l-th layer can be represented as:

[0107] ​

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

[0109] wherein, denotes the interference signal generated by other terminal devices in the lth layer to the terminal device u l,k The reason why the interference signal is calculated from k+1 is that 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 numbered and sorted according to the channel gain from high to low. For the edge server, the detection process is as follows: first, the terminal device with strong signal strength is detected, and then the signal of the detected terminal device is subtracted from the received signal, and the next terminal device is detected. Therefore, after detecting a terminal device and subtracting its signal, the detection environment of the subsequent terminal device changes, and the remaining interference is mainly generated by the terminal devices that have not been detected. This means that after the processing of a certain terminal device is completed, the terminal device no longer causes interference to the subsequent detection. Therefore, only the terminal devices arranged in the back will cause interference to the terminal device being detected. Based on such detection and cancellation order, for the kth terminal device, the terminal devices arranged after k will generate corresponding interference.

[0110] wherein, denotes the interference generated by the corresponding signal of the terminal device of the other communication layer. This embodiment takes two communication layers as an example for illustration, so when l=1, 3-l=2 corresponds to the terminal device of the reinforcement layer, and when l=2, 3-l=1 corresponds to the terminal device of the basic layer.

[0111] wherein, denotes the channel noise, n denotes additive white Gaussian noise, and is subject to complex Gaussian distribution σ 2 denotes the noise energy, I N denotes the unit matrix corresponding to the number of antennas N.

[0112] In an embodiment, for the terminal device u l,k , the beamforming power of the receiving end can be obtained according to the beamforming vector w l,k of the receiving end, so the beamforming power is represented as:

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

[0114] In an embodiment, in the process of obtaining the terminal device ul,k Before the corresponding server calculates the power, it needs to obtain the following relevant communication parameters, such as channel bandwidth B, the amount of user data to be transmitted U l,k , computing 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 assess terminal devices is a parameter related to system hardware. It reflects the energy consumption required for a 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 based on actual conditions. The resource allocation parameter, which measures the amount of computing resources required for the terminal device to calculate each bit of data, can also be pre-defined based on actual conditions.

[0116] Next, refer to Figure 3 , Figure 3 This is a flowchart of calculating 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 provided by an embodiment of the present application, which specifically includes the following steps:

[0117] Step 310: Calculate the signal to interference and noise ratio based on the beamforming vector, channel parameters, and signal transmission power at 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 based on the beamforming vector, channel parameters, and signal transmission power at the receiving end provided in 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: other terminal devices in the same communication layer as the terminal device and having numbers greater than the terminal device are regarded as same-layer terminal devices, and terminal devices in different communication layers from the terminal device are regarded as other-layer terminal devices, a same-layer signal power corresponding to each same-layer terminal device is obtained, and an other-layer signal power corresponding to each other-layer terminal device is obtained.

[0124] In an embodiment, referring to the above introduction, for the terminal device u l,k , other terminal devices after the kth terminal device in the lth layer are regarded as 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, terminal devices in different communication layers from the terminal device are regarded 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 represents two communication layers for example, and K 3-l represents the number of terminal devices corresponding to the 3-lth layer.

[0126] Next, according to the calculation method of the expected signal power, a same-layer signal power corresponding to each same-layer terminal device is obtained under the beamforming vector w l,k , so the ith same-layer signal power is represented as:

[0127]

[0128] where p l,i represents the signal transmission power corresponding to the ith terminal device in the lth layer, and h l,i represents the channel parameter corresponding to the ith terminal device in the lth layer.

[0129] It is also necessary to obtain an other-layer signal power corresponding to each other-layer terminal device, and the jth other-layer signal power is represented as:

[0130]

[0131] where p 3-l,j represents the signal transmission power corresponding to the jth terminal device in the 3-lth layer, and h 3-l,j represents the channel parameter corresponding to the jth terminal device in the 3-lth layer.

[0132] Step 430: the same-layer signal powers are accumulated to obtain a same-layer interference signal power, the other-layer signal powers are accumulated to obtain an other-layer interference signal power, and a total interference signal power is obtained according to the same-layer interference signal power and the other-layer interference signal power.

[0133] In an embodiment, the co-layer interference signal power is represented as:

[0134]

[0135] And the other-layer interference signal power is represented as:

[0136]

[0137] The total interference signal is represented 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 an embodiment, the noise power is represented as:

[0141]

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

[0143]

[0144] Terminal device u l,k Corresponding signal-to-interference-noise ratio γ l,k is represented as:

[0145]

[0146] Step 320: Calculate the communication rate by multiplying the signal-to-interference-noise ratio plus one after taking the logarithm by the channel bandwidth, and calculate the product of the communication rate, the allocated resource parameter, the calculated resource parameter, and the effective capacitance coefficient to obtain the server calculation power.

[0147] In an embodiment, based on the Shannon theorem, the maximum achievable communication rate of the terminal device is calculated through the signal-to-interference-noise ratio, and therefore the communication rate is represented as:

[0148] Blog2(1+γ l,k )

[0149] The server calculation power is represented as:

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

[0151] Step 120: obtaining a target power corresponding to each terminal device according to server computing power, signal transmission power and beamforming power of a receiving end, accumulating all target powers to obtain total power, and minimizing the total power to generate an optimization target function.

[0152] In an embodiment, for terminal device u l,k The 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] Wherein, L represents the number of communication layers.

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

[0158]

[0159] Step 130: generating a constraint condition set based at least on user data volume corresponding to each terminal device, signal-to-interference-and-noise ratio, computing resource parameter and signal transmission power.

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

[0161] Step 510: for each terminal device, generating a time delay constraint condition according to corresponding user data volume, signal-to-interference-and-noise ratio, computing resource parameter and signal transmission power.

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

[0163] Step 610: for each terminal device, obtaining a corresponding communication rate, and obtaining a first time delay parameter according to a ratio of user data volume and communication rate.

[0164] In an embodiment, for terminal device ul,k For example, the first latency parameter is used to represent the delay of data offloading of the terminal device to the edge server, and the first latency parameter is represented as:

[0165]

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

[0167] In an embodiment, considering that after the data offloading of the terminal device is completed, the edge server will use part of the computing resources thereof to complete the computing task required by the terminal device, the second latency parameter is used to represent the computing delay of this part, and the second latency parameter is represented as:

[0168]

[0169] Therefore, the total latency parameter of the terminal device u l,k is represented as:

[0170]

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

[0172] In an embodiment, the latency constraint condition is represented as:

[0173]

[0174] wherein, the latency upper limit value corresponding to the terminal device u l,k , which can reflect the latency tolerance of different terminal devices, and can be set according to actual conditions, that is, different latency upper limit values can be set for different types of terminal devices, or the same latency upper limit value can be set for the same communication layer, and the embodiment does not limit this. Generally speaking, taking two communication layers as an example, since the users of the reinforcement layer have higher requirements for delay, it can be determined that:

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

[0176] In an embodiment, the total transmission power is represented as:

[0177]

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

[0179]

[0180] wherein P max represents the maximum power value.

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

[0182] In an embodiment, the total calculation resources are expressed as:

[0183]

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

[0185]

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

[0187] In an embodiment, the constraint condition set is expressed as:

[0188]

[0189] According to the constraint condition set and the optimization objective function, the optimization objective of the embodiment of the present application is to minimize the power consumed in the transmission process of the distributed system. At the same time, it is necessary to satisfy that the delay of each terminal device cannot exceed the upper limit of delay tolerance, and the calculation resource parameters and the signal transmission power must be non-negative, and the respective total cannot exceed the respective upper limit of the budget.

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

[0191] In an embodiment, the target transmission parameters include target calculation resource parameters, target signal transmission power and target beamforming vectors, that is, through the solving process of the optimization objective function, the optimal value of the calculation resource parameters corresponding to each terminal device is obtained as the target calculation resource parameters, 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 an embodiment, to solve the optimization problem described above, the present solution adopts an iterative algorithm, with the aid of an alternating optimization framework and a continuous convex approximation method based on a path-following procedure, to achieve the expected optimization goal and thus obtain the target transmission parameter.

[0193] Specifically, the optimization objective function described above belongs to a non-convex problem. In the present embodiment, the problem is handled by introducing auxiliary variables and proxy functions. On the one hand, the auxiliary variables are used to reasonably disassemble and recombine the originally complex relationship; on the other hand, the proxy functions are used to approximate and replace the function form of the original objective function, so as to convert the non-convex problem into a series of convex sub-problems. These convex sub-problems can be solved by solving the Karush-Kuhn-Tucker (KKT) condition. When certain regular conditions are met, the KKT condition is a necessary and sufficient condition for the optimal solution of a convex optimization problem. Therefore, when the KKT condition is used to solve each convex sub-problem, the optimal solution of the sub-problem can be ensured. Since the original non-convex problem has been decomposed into a plurality of such convex sub-problems, and the optimal solution of each convex sub-problem can be obtained by means of the KKT condition, the algorithm of the present embodiment can at least guarantee that the optimization result converges to a stationary point of the original constraint condition set.

[0194] In addition, the present embodiment utilizes an alternating optimization framework to split the optimization of each variable, and decomposes the originally complex optimization problem involving multiple variables into a plurality of sub-problems involving only a single variable or a small number of variables. Compared with the solution of the original optimization objective function, these sub-problems are more concise in form, and the complexity of the constraint conditions and the objective function is significantly reduced, so that they are easier to solve.

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

[0196] In an embodiment, with reference to Figure 7 , Figure 7 is a flowchart provided by the present embodiment for solving the optimization objective function under the premise of meeting the constraint condition set, and obtaining the target transmission parameter of each terminal device on the corresponding channel, and specifically includes the following steps:

[0197] Step 710: obtaining a computing resource optimization item related to the allocation resource parameter from the optimization objective function, solving the computing resource optimization item, and obtaining the target computing resource parameter.

[0198] In an embodiment, when optimizing the computing resource parameter, other variables, such as the beamforming vector w l,k are kept 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 taken as a sub-problem about the computing resource parameter f l,k . l,k l,k

[0199] wherein the computing resource optimization item is expressed as:

[0200]

[0201] In an embodiment, the computing resource optimization item is a convex problem after the splitting of the sub-problems, and thus the optimal solution of the sub-problems can be found by Karush-Kuhn-Tucker conditions. Refer to Figure 8 , Figure 8 is a flowchart provided by the embodiments of the present application for solving the computing resource optimization item to obtain the target computing resource parameter, and specifically includes the following steps:

[0202] Step 810: setting a first Lagrange multiplier corresponding to the time delay constraint condition and a second Lagrange multiplier corresponding to the computing resource constraint condition.

[0203] In an embodiment, a Lagrange function L is established, and λ l,k is the first Lagrange multiplier corresponding to the time delay constraint condition, and μ≥0 is the second Lagrange multiplier corresponding to the computing resource constraint condition.

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

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

[0206]

[0207] Step 830: calculating the partial derivative of the computing resource Lagrange function to obtain a first partial derivative function, and solving the first partial derivative function to obtain the target computing resource parameter.

[0208] In an embodiment, the partial derivative of the computing resource Lagrange function with respect to the computing resource parameter f l,k is calculated to obtain the first partial derivative function, and the first partial derivative function is expressed as:

[0209]

[0210] Let the first partial derivative function solve the optimal solution of the computing resource parameter f l,k , and obtain:

[0211]

[0212] After moving the term, the following can be obtained:

[0213]

[0214] Further, we have:

[0215]

[0216] Assume and since f l,k ≥ 0, we have:

[0217] The optimal solution of the computing resource parameter is expressed as:

[0218]

[0219] wherein, and The following two formulas are satisfied, and the final value can be obtained by bisection method.

[0220] Substitute the optimal solution expression formula of the computing resource parameter into the computing resource constraint condition to obtain:

[0221]

[0222] When the computing resource parameter takes the optimal solution, the computing resource constraint condition takes the equal sign, so the following formula can be obtained:

[0223]

[0224] Similarly, substitute the optimal solution expression formula into the delay constraint condition to obtain:

[0225]

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

[0227]

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

[0229] Step 720: Under the constraint of the target computing resource parameter, obtain the transmission optimization term related to the signal transmission power and the beamforming vector from the optimization target function, solve the transmission optimization term, and obtain the target signal transmission power and the target beamforming vector.

[0230] In an embodiment, the target computing resource parameter has been obtained by the above process, wherein the target computing resource parameter is the optimal solution of the resource allocation of the edge server for each terminal device. On this basis, the optimal solution of the signal transmission power and the optimal solution of 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 term related to the signal transmission power and the beamforming vector in the optimization objective function is obtained as follows:

[0231]

[0232] wherein, denotes generating a delay reference term corresponding to the signal-to-interference-and-noise ratio according to the delay constraint condition.

[0233] It can be understood that the transmission optimization term related to the signal transmission power and the beamforming power obtained from the optimization objective function is calculated uniformly by the above process, or the signal transmission power and the beamforming power are calculated separately according to the division manner of the computing resource optimization term.

[0234] In an embodiment, a convex surrogate function is provided for the function in the sub-problem of the transmission optimization term by using a path following process, and the transmission optimization term is solved by using the surrogate function.

[0235] Referring to Figure 9 , Figure 9 is a flowchart provided by the embodiment of the present application for solving the transmission optimization term to obtain the target signal transmission power and the target beamforming vector, and specifically includes the following steps:

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

[0237] In an embodiment, the convex upper bound of log2(1+γ l,k ) and the convex lower bound of γ l,k are found, that is,

[0238]

[0239] The convex upper bound and the convex lower bound are substituted into the transmission optimization term, and then an approximate convex sub-problem is constructed for the sub-problem to facilitate the solution of the problem. By this operation, the original non-convex optimization problem is converted into an approximate convex sub-problem. Since the convex optimization problem has good properties, such as the local optimal solution is the global optimal solution, which makes the solution process of the problem become more simple and efficient. With this approximate processing method, the convex optimization algorithm can be used to solve the originally complex non-convex optimization problem.

[0240] In an embodiment, two communication layers are taken as an example for illustration. Referring to Figure 10 , Figure 10 is a flowchart for generating a convex lower bound corresponding to a signal-to-interference-and-noise ratio provided by an embodiment of the present application, and specifically includes the following steps:

[0241] Step 1010: obtaining an auxiliary variable according to the reciprocal of the signal transmission power, adjusting the expected signal power based on the auxiliary variable to obtain a first non-convex function, adjusting the interference noise power according to the auxiliary variable to obtain a first convex function, and updating the signal-to-interference-and-noise ratio by using the first convex function and the first non-convex function to obtain a signal-to-interference-and-noise ratio function.

[0242] In an embodiment, the auxiliary variable θ l,k is represented as:

[0243]

[0244] The first non-convex function x(θ l,k ,w l,k ) obtained by adjusting the expected signal power based on the auxiliary variable is represented as:

[0245]

[0246] The first convex function y(θ l,k ,w l,k ) obtained by adjusting the interference noise power according to the auxiliary variable is represented as:

[0247]

[0248] The signal-to-interference-and-noise ratio function obtained by updating the signal-to-interference-and-noise ratio by using the first convex function and the first non-convex function is represented as:

[0249]

[0250] Step 1020: 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.

[0251] In an embodiment, the first iteration value x obtained according to the first non-convex function is represented as:

[0252]

[0253] wherein n represents the nth iteration.

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

[0255]

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

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

[0258]

[0259] wherein x>0, y>0.

[0260] Performing first-order Taylor expansion on the first auxiliary convex function to obtain a first inequality at a position where the first iteration value is greater than zero and the second iteration value is greater than zero, that is, using the first-order Taylor series expansion of f(x, y) at any , the first inequality is obtained and denoted as:

[0261]

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

[0263] In an embodiment, performing first-order Taylor expansion on the first non-convex function to obtain a second inequality, that is, performing first-order Taylor series expansion on at any given , the second inequality is obtained and denoted as:

[0264]

[0265] wherein

[0266] Next, substituting the first inequality into the second inequality to obtain a convex lower bound of the signal-to-interference-and-noise ratio function corresponding to γ l,k (w l,k ,θ l,k ) in each iteration process, and denoted as:

[0267]

[0268] In an embodiment, referring to Figure 11 , Figure 11 is a flowchart provided by the embodiment of the application for generating a convex upper bound corresponding to a communication rate, and specifically includes the following steps:

[0269] Step 1110: 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 by using the second convex function and the second non-convex function to obtain a communication rate function.

[0270] In an 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 by using the second convex function and the second non-convex function to obtain a communication rate function, which 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: 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.

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

[0278]

[0279] The fourth iteration value is expressed as:

[0280]

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

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

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

[0284] where 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 second auxiliary convex function is first-order Taylor expanded to obtain a third inequality, that is, the first-order Taylor series expansion of g(a,b) at any point is used to obtain the second inequality, denoted as:

[0286]

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

[0288] In an embodiment, in order to obtain the convex upper bound of log2(l+γ l,k (w l,k ,θ l,k )), the second inequality after the 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 are substituted to obtain the convex upper bound, denoted as:

[0289]

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

[0291]

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

[0293] In an embodiment, after the convex upper bound of log2(l+γ l,k ) and the convex lower bound of γ l,k , the transmission optimization term can be approximated, specifically, the transmission optimization update term is obtained by updating the transmission optimization term using the convex upper bound, and the reference delay constraint condition is obtained by setting the delay reference term to be less than or equal to the convex lower bound.

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

[0295]

[0296] wherein, denotes the above convex upper bound.

[0297] The reference delay constraint condition is expressed as:

[0298]

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

[0300] In an embodiment, under the joint constraint of the reference delay constraint condition and the transmission power constraint condition, the transmission optimization update item is solved to obtain the target signal transmission power and the target beamforming vector, and therefore the joint constraint is expressed as:

[0301]

[0302] In an embodiment, since the transmission optimization update item has strong convexity, the interior point method tool is used to solve the optimal solution thereof. Referring to Figure 12 , Figure 12 is a flowchart provided by the embodiments 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, and specifically includes the following steps:

[0303] Step 1210: based on the constraint condition set, all variables are initialized, and the target computing resource parameter is obtained.

[0304] In an embodiment, referring to Figure 13 , Figure 13 is a flowchart of the solving algorithm of the transmission optimization update item in the embodiments of the present application. Referring to Figure 13 , under the premise of the constraint condition set, all variables used are initialized, that is, the computing resource parameter signal transmission power and the beamforming vector of the receiving end and the target computing resource parameter calculated in the foregoing is obtained to update the computing resource parameter f l,k .

[0305] Step 1220: the auxiliary variable is initialized to obtain the auxiliary variable initial value corresponding to the first iteration process.

[0306] In an embodiment, referring to Figure 13 , the iteration number n=0 is initialized, and the auxiliary variable is initialized to obtain the auxiliary variable initial value.

[0307]

[0308] Step 1230: enter a multi-iteration solving process, solve the transmission optimization update item according to the auxiliary variable value, the beamforming vector value and the target computing resource parameter of the current iteration process, obtain the iteration auxiliary variable value and the iteration beamforming vector value, take the iteration auxiliary variable value as the auxiliary variable value of the next iteration process, take the iteration beamforming vector value as the beamforming vector value of the next iteration process, and continue the iteration process until the iteration stopping condition is reached.

[0309] In an embodiment, a multi-iteration solving process is entered, and in each iteration, the transmission optimization update item is solved according to the auxiliary variable value, the beamforming vector value and the target computing resource parameter of the current iteration process by using the interior point method, to obtain the optimal solution of the current iteration process: the iteration auxiliary variable value θ l ,k and the iteration beamforming vector value w l ,k . That is, two solutions corresponding to the transmission optimization update item are calculated by using the interior point method, as the iteration auxiliary variable value and the iteration beamforming vector value. In the first iteration, the auxiliary variable value is the auxiliary variable initial value, and the beamforming vector value is the initial

[0310] If the iteration is not terminated, the iteration auxiliary variable value is taken as the auxiliary variable value of the next iteration process, and the iteration beamforming vector value is taken as the beamforming vector value of the next iteration process, that is: The iteration process is continued, the iteration number is increased by one, and the iteration process is continued until the iteration stopping condition is reached. The iteration stopping condition can be that the preset iteration number is reached, or the iteration time reaches the preset time, and the embodiment is not limited in this regard.

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

[0312] In an 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 iteration beamforming vector value obtained in the last iteration process is taken as the target beamforming vector.

[0313] ​​The data transmission method of the distributed system provided in the embodiments of the present application can be used in a virtual reality content distribution network for heterogeneous users with various quality of service 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 requirements in the network, the network architecture can more accurately match the requirements of different users to serve more users with limited virtual reality resources. Meanwhile, 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 guarantee of virtual reality service requirements of a large number of concurrent users for a long time.

[0314] In an embodiment, referring to Figure 14 , Figure 14 FIG. 1 is a convergence performance curve diagram of the data transmission method of the distributed system in the embodiments of the present application. It can be seen that the data transmission method of the distributed system in the embodiments 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, K2=1 with changing the optimization order, K1=20, K2=5, and K1=20, K2=5 with changing the optimization order. The iteration stop condition of each layer is set as: 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 embodiments of the present application can realize rapid convergence within about 10 iterations under different terminal device quantities. Moreover, even if the optimization order of each variable in the alternating optimization framework is changed, the data transmission method of the distributed system in the embodiments of the present application can still converge rapidly and will not cause performance loss. This fully shows that the proposed data transmission method of the distributed system can stably and efficiently solve the power optimization problem of the distributed virtual reality system.

[0315] In an embodiment, to intuitively show the influence of the existence of multiple layers of heterogeneous users on network performance and the advantage of the data transmission method of the distributed system in the embodiments of the present application in optimizing the network performance, the proposed distributed virtual reality network is compared with a traditional network without user layering.

[0316] Referring to Figure 15 , Figure 15The schematic diagram of power consumption of the data transmission method of the distributed system provided in the embodiments of the present application varying with the number of antennas is shown in the figure. As shown in the figure, compared with the traditional network without user stratification, the network corresponding to the data transmission method of the distributed system provided in the embodiments of the present application has lower power consumption in the case of different numbers of antennas. Moreover, when the number of users is large, the performance is improved more significantly. These results confirm that the stratified network structure designed in the data transmission method of the distributed system provided in the embodiments of the present application can significantly reduce the system power consumption, especially in a large-scale virtual reality network containing a large number of users.

[0317] In addition, it can also be seen from Figure 15 that the system power consumption decreases with the increase of the number of antennas. This is because as the number of antennas increases, the network can obtain more spatial diversity gain, so that the required delay constraint is relaxed. In this way, when the same performance level is reached, the power consumption requirement is reduced.

[0318] The technical scheme provided in the embodiments of the present application acquires the signal transmission power and the beamforming power of the receiving end for each terminal device, calculates the server computing power according to the channel bandwidth of the corresponding channel, the allocation resource parameter, the signal-to-interference-noise ratio, the computing resource parameter and the effective capacitance coefficient, acquires the target power corresponding to each terminal device according to the server computing power, the signal transmission power and the beamforming power of the receiving end, accumulates all the target powers to obtain the total power, minimizes the total power to generate an optimization objective function, generates a constraint condition set based on at least the user data volume, the signal-to-interference-noise ratio, the computing resource parameter and the signal transmission power corresponding to each terminal device, and finally solves the optimization objective function under the premise of meeting the constraint condition set to obtain the target transmission parameter of each terminal device on the corresponding channel, which includes the target computing resource parameter, the target signal transmission power and the target beamforming vector. The embodiments of the present application first stratify the 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 the users. This way can adapt to the user requirements, and through reasonable stratification and resource allocation, the mobile devices of the users can meet their own requirements while avoiding unnecessary computing burden. In addition, the effective capacitance coefficient is introduced in the process of optimizing the transmission parameter to reflect the energy consumption characteristics of the mobile devices in different working states, so as to more accurately describe the energy consumption of the mobile devices in the transmission process. At the same time, the overall optimization modeling is performed in combination with the energy consumption of all mobile devices in the transmission process, so as to maximize the reduction of the total network energy consumption on the basis of ensuring the smoothness of the virtual reality content transmission.

[0319] The embodiments of the present application also provide a data transmission device of a distributed system, which can implement the data transmission method of the distributed system, and the data transmission device is described with reference to Figure 16The apparatus comprises:

[0320] The power calculation module 1610 is configured to acquire, for each terminal device, the signal transmission power and the beamforming power of the receiving end, and calculate the server computing power according to the channel bandwidth of the corresponding channel, the allocated resource parameter, the signal-to-noise ratio, the computing resource parameter and the effective capacitance coefficient.

[0321] The optimization target construction module 1620 is configured to acquire the target power corresponding to each terminal device according to the server computing power, the signal transmission power and the beamforming power of the receiving end, accumulate all the target powers to obtain the total power, and minimize the total power to generate an optimization target function.

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

[0323] The optimization solving module 1640 is configured to solve the optimization target function under the premise of satisfying the constraint condition set, to obtain the target transmission parameter of each terminal device on the corresponding channel, the target transmission parameter including the target computing resource parameter, the target signal transmission power and the target beamforming vector.

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

[0325] The embodiment of the present application further provides an electronic device comprising:

[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 as described above. The electronic device can be any intelligent terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), a vehicle-mounted computer, etc.

[0330] Please refer to Figure 17 , Figure 17 The hardware structure of the electronic device of another embodiment is illustrated, and the electronic device comprises:

[0331] The processor 1701 can 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 configured to execute related programs to implement the technical solutions provided by the embodiments of the present application.

[0332] The memory 1702 can be implemented by 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 application programs. When the technical solutions provided by the embodiments of the present application are implemented by software or firmware, the related program codes are stored in the memory 1702 and are called and executed by the processor 1701 to implement the data transmission method of the distributed system according to the embodiments of the present application.

[0333] The input / output interface 1703 is configured to realize information input and output.

[0334] The communication interface 1704 is configured to realize the communication interaction between the device and other devices. The communication can be realized by a wired manner (for example, a USB, a network cable, etc.) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, etc.).

[0335] The bus 1705 is configured to transmit information between various components (for example, the processor 1701, the memory 1702, the input / output interface 1703, and the communication interface 1704) of the device.

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

[0337] The embodiments of the present application further provide a storage medium. The storage medium is a storage medium, and the storage medium stores a computer program. The computer program is executed by the processor to implement the data transmission method of the distributed system.

[0338] Memory, as a non-transitory storage medium, can be used to store non-transitory software programs and non-transitory computer executable programs. In addition, the memory can include high-speed random access memory and can also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include memory that is remotely set with respect to the processor, and these remote memories can be connected to the processor through a network. Examples of the above 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 provided by the embodiments of the present application are described as follows: for each terminal device, 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 of the corresponding channel, the allocation resource parameter, the signal-to-interference-noise ratio, the calculation resource parameter and the effective capacitance coefficient; then the target power corresponding to each terminal device is obtained according to the server calculation power, the signal transmission power and the beamforming power of the receiving end; the total power is obtained by accumulating all the target powers; the optimization target function is generated by minimizing the total power; the constraint condition set is generated based on at least the user data amount of each terminal device, the signal-to-interference-noise ratio, the calculation resource parameter and the signal transmission power; finally, the optimization target function is solved under the premise of meeting the constraint condition set, and the target transmission parameter of each terminal device on the corresponding channel is obtained, which includes the target calculation resource parameter, the target signal transmission power and the target beamforming vector. The embodiments of the present application first stratify 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 the users. This way can adapt to user needs, and through reasonable stratification and resource allocation, the mobile device of the user can meet its own needs while avoiding unnecessary computing burden. In addition, the effective capacitance coefficient is introduced in the process of optimizing the transmission parameter to reflect the energy consumption characteristics of the mobile device in different working states, so as to more accurately describe the energy consumption of the mobile device in the transmission process. At the same time, the overall optimization modeling is carried out in combination with the energy consumption of all mobile devices in the transmission process, so as to maximize the reduction of the total network energy consumption on the basis of ensuring the smoothness of virtual reality content transmission.

[0340] The embodiments described in the embodiments of the present application are used 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 by the embodiments of the present application. Those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0341] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation to the embodiments of the present application, and can include more or fewer steps than the figures, or combine certain steps, or different steps.

[0342] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separated, that is, can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments.

[0343] Those skilled in the art can understand that all or some steps in the above disclosed method, functions of the modules / units in the system and the device can be implemented as software, firmware, hardware and appropriate combinations thereof.

[0344] The terms "first", "second", "third", "fourth" and the like in the description of the present application and the above-mentioned figures (if any) are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units 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" means one or more, and "multiple" means two or more. "And / or" is used to describe the association between the associated objects, which means that there can be three relationships, for example, "A and / or B" can represent three cases: only A, only B, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple 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, and c can be single or multiple.

[0346] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented by other manners. For example, the apparatus embodiments described above are merely illustrative, for example, the division of the above units is merely a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or components shown or discussed can be indirect coupling or communication connection through some interfaces, apparatuses or units, and can be electrical, mechanical or other forms.

[0347] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they can be located in one place or distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0348] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0349] If the integrated unit is realized 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 solutions of the present application essentially or the part of the prior art that contributes to the technical solutions or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method of each embodiment of the present application. The foregoing storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk, and various program storage media.

[0350] The preferred embodiments of the embodiments of the present application are described above with reference to the accompanying drawings, but this does not limit the scope of the embodiments of the present application. Any modifications, equivalent replacements and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the embodiments of the present application.

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 via a wireless access point, and the method includes: For each of the terminal devices, 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; Obtaining a target power corresponding to each terminal device 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 constraint condition set based at least on the user data volume corresponding to each terminal device, 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, and 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 calculating server calculation power according to the channel bandwidth, allocation resource parameter, signal to interference noise ratio, calculation resource parameter and effective capacitance coefficient of the corresponding channel includes: Calculating a signal-to-interference-and-noise ratio based on a beamforming vector at a receiving end, channel parameters, and the signal transmission power; The communication rate is obtained by adding one to the signal-to-interference-and-noise ratio, calculating the logarithm, and then multiplying the result by the channel bandwidth. The product of the communication rate, the allocated 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 channel gain, and the signal-to-interference-and-noise ratio is calculated according to the beamforming vector of the receiving end, the channel parameter, and the signal transmission power, including: Calculating 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, as well as 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 based on the same-layer interference signal power and the other-layer interference signal power; A sum of the total interference signal power and the noise power is obtained as the interference noise power, and a 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: Generating a constraint condition set based at least on the amount of user data corresponding to each terminal device, the signal to interference and noise ratio, the computing resource parameter, and the signal transmission power includes: For each terminal device, 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: Generating, for each terminal device, 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: For each of the terminal devices, obtaining the corresponding communication rate, and obtaining a first delay parameter 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 to generate the delay constraint condition.

6. The data transmission method of a distributed system according to claim 4, characterized in that: The step of solving the optimization objective function on the premise that the constraint condition set is satisfied to obtain the target transmission parameters of each terminal device in the corresponding channel includes: Obtaining a computing resource optimization item related to the allocated resource parameter from the optimization objective function, solving the computing resource optimization item to obtain the target computing resource parameter; Under the constraints 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: 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 parameters.

8. The data transmission method of a distributed system according to claim 6, characterized in that: 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; The transmission optimization item is updated based on the convex upper bound 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: 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, 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 positions where the first iteration value is greater than zero and the second iteration value is greater than zero to obtain a first inequality; A first-order Taylor expansion is performed on the first non-convex function to obtain a second inequality, and the first inequality is substituted into the second inequality to obtain the convex lower bound corresponding to the signal-to-interference-plus-noise ratio function in each iteration process.

10. The data transmission method of a distributed system according to claim 9, characterized in that: 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 positions 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 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 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: Initialize all variables based on the constraint set and obtain the target computing resource parameters; Initializing the auxiliary variables to obtain the corresponding initial values ​​of the auxiliary variables in the first iteration process; Entering a multiple-iteration solution process, solving the transmission optimization update term according to the auxiliary variable value, the beamforming vector value, and the target computing resource parameter of the current iterative process to obtain an iterative auxiliary variable value and an 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 an 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 apparatus includes: Power calculation module: 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; an optimization target construction module configured to obtain a target power corresponding to each terminal device based on 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 objective function; A constraint condition construction module is configured to generate a constraint condition set based on at least the user data volume corresponding to each terminal device, 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 under 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 the distributed system according to any one of claims 1 to 11 is implemented.

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