CN114501648B - A resource allocation method for wireless power supply edge computing network - Google Patents
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- H04W72/00—Local resource management
- H04W72/04—Wireless resource allocation
- H04W72/044—Wireless resource allocation based on the type of the allocated resource
- H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power
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Abstract
本发明公开了一种无线供能边缘计算网络的资源分配方法，按照设定的步长，从供能时长比例的取值范围中，依次选取当前供能时长比例，进行迭代，确定当前供能时长比例情况下各无线设备用于任务卸载通信的能量比例和任务卸载通信时长比例，选取所述无线供能边缘计算网络总计算速率最大时的供能时长比例，设置所述射频能量发射器对无线设备在每个时间帧开始时进行无线供能；配置每个无线设备在每个时间帧内以所选取的供能时长比例对应的任务卸载通信时长和能量分配比例进行任务卸载通信。本发明能够快速计算出部分卸载的能量分配和时间分配方案，同时也得到较高的网络计算速率。
The present invention discloses a resource allocation method for a wireless power supply edge computing network. According to a set step size, the current power supply duration ratio is selected in turn from the value range of the power supply duration ratio, and iteration is performed to determine the energy ratio and task unloading communication duration ratio of each wireless device for task unloading communication under the current power supply duration ratio, and the power supply duration ratio when the total computing rate of the wireless power supply edge computing network is the maximum is selected, and the radio frequency energy transmitter is set to wirelessly power the wireless device at the beginning of each time frame; each wireless device is configured to perform task unloading communication in each time frame with the task unloading communication duration and energy allocation ratio corresponding to the selected power supply duration ratio. The present invention can quickly calculate the energy allocation and time allocation scheme of partial unloading, and also obtain a higher network computing rate.
Description
Technical Field
The application belongs to the technical field of wireless energy supply, and particularly relates to a resource allocation method of a wireless energy supply edge computing network.
Background
The development of the Internet of things enables human-computer interaction to be more and more in emerging applications, including intelligent home, automatic driving and the like. Many new high-performance application programs rely on real-time communication and a large amount of computation, but the nodes of the internet of things generally have limited electric quantity and low computation capability and cannot support high-performance computing application, so solving the two limitations is one of the key problems for improving the application performance of the internet of things.
The wireless power transmission technology (WPT) based on radio frequency provides a feasible method for solving the problem of energy shortage in the Internet of things network, the mobile edge computing technology (MEC) can provide high-performance computing service for the Internet of things node with low computing power, and the wireless energy supply edge computing network (WP-MEC) can solve the problems of energy shortage and computing power limitation in the Internet of things network. Specifically, the energy transmitter and edge computation server are deployed at the network edge, and the wireless device offloads the computation task to the edge computation server by capturing radio frequency energy, relying on the collected energy, and computes the remaining tasks locally. The combination of wireless energy transfer and mobile edge computing enables sustainable network operation, significantly extends the life of the wireless network, and enhances the computing and communication capabilities of the wireless edge devices.
In edge computing networks, offloading policies of wireless devices, such as communication time allocation within a time frame, etc., need to be considered, which will result in whether a network is efficient or not. Each of the wireless devices of the Internet of things follows a partial offloading strategy, namely the tasks are divisible, one part of the tasks are executed locally on the wireless devices, and the other part of the tasks are offloaded to an edge server for calculation. There are also a number of criteria to evaluate whether a network is efficient, such as minimum latency, maximum rate, etc.
Most of the current time allocation methods of wireless energy-supply edge computing networks mostly use traditional optimization methods, but the solution process is complex, and the optimal solution can be obtained through multiple iterations, which consumes much time, and is unacceptable for applications requiring high-performance computing services.
Disclosure of Invention
The application aims to provide a resource allocation method and device for a wireless energy supply edge computing network, which are used for overcoming the technical difficulties and achieving higher computing speed.
In order to achieve the above purpose, the technical scheme of the application is as follows:
A method of resource allocation for a wireless powered edge computing network comprising a wireless device and a gateway comprising a radio frequency energy transmitter and an edge computing server, the method of resource allocation for a wireless powered edge computing network comprising:
Step S1, sequentially selecting a current energy supply duration proportion from a value range of the energy supply duration proportion according to a set step length, and carrying out the following iteration to determine the energy proportion of each wireless device for task unloading communication and the task unloading communication duration proportion under the condition of the current energy supply duration proportion;
Step S1.1, selecting a first wireless device from a wireless energy supply edge computing network, and initializing the range of the energy proportion of the first wireless device for task unloading communication;
Step S1.2, setting the energy ratio x 1 of the first wireless device for task offload communication as a mean value of the upper limit value and the lower limit value of the range, and calculating the energy ratio of any other wireless device for task offload communication according to the following formula:
Wherein x 1 is the energy proportion of the first wireless device for task offload communication, x i is the energy proportion of the ith wireless device for task offload communication, h 1 is the channel gain of the first wireless device, and h i is the channel gain of the ith wireless device;
step S1.3, calculating the task unloading communication duration proportion of the wireless device according to the following formula:
Wherein t i is the task unloading communication duration proportion of the ith wireless device, B is the wireless communication bandwidth, phi is the CPU cycle number required by the wireless device to locally process a bit task, k e is the calculation energy efficiency coefficient of the wireless device, N 0 is the noise power, a is the energy supply duration proportion, mu is the energy capturing efficiency, and P is the radio frequency energy transmitting power of the radio frequency energy transmitter;
Step S1.4, at If the search accuracy is greater than xiThen taking x 1 as the upper limit value of the range of the energy proportion of the first wireless device for task offloading communication, updating the range of the energy proportion of the first wireless device for task offloading communication and returning to the step S1.2, otherwise taking x 1 as the lower limit value of the range of the energy proportion of the first wireless device for task offloading communication, updating the range of the energy proportion of the first wireless device for task offloading communication and returning to the step S1.2, wherein the step S1.2 is thatWhen the search precision xi is smaller than or equal to the search precision xi, calculating the total calculation rate of the wireless energy supply edge calculation network, and ending iteration;
Step S2, selecting the energy supply duration proportion when the total calculation rate of the wireless energy supply edge calculation network is maximum, setting the radio frequency energy transmitter to wirelessly supply energy to the wireless equipment at the beginning of each time frame, configuring each wireless equipment to carry out task unloading communication according to the task unloading communication duration and the energy distribution proportion corresponding to the selected energy supply duration proportion in each time frame, and configuring each wireless equipment to carry out local task calculation according to the residual energy in each time frame.
Further, the computing wireless energy supply edge computing network total computing speed has the following formula:
where N is the number of wireless devices.
Further, the wireless devices perform task offloading communication in a time division multiplexing manner.
Further, the energy supply duration ratio has a value range of [0,1], and the step length is 0.01.
Compared with the traditional optimization method, the resource allocation method of the wireless energy supply edge computing network provided by the application can solve a complex non-convex problem in the wireless energy supply edge computing network using a TDMA communication mode and a partial unloading mode, can rapidly calculate partial unloading energy allocation and time allocation schemes, and simultaneously obtains higher network calculation rate.
Drawings
FIG. 1 is a schematic diagram of a TDMA-based wireless powered edge computing network;
Fig. 2 is a flow chart of a method for allocating resources in a wireless energy-supply edge computing network according to the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
As shown in fig. 1, the wireless powered edge computing network includes a wireless device and a gateway that includes a radio frequency energy transmitter for powering the wireless device and an edge computing server for computing tasks offloaded from the wireless device.
In one embodiment, as shown in fig. 2, there is provided a resource allocation method of a wireless powered edge computing network, including:
Step S1, sequentially selecting the current energy supply duration proportion from the value range of the energy supply duration proportion according to the set step length, and carrying out the following iteration to determine the energy proportion of each wireless device for task unloading communication and the task unloading communication duration proportion under the condition of the current energy supply duration proportion.
The present embodiment is described by taking a wireless energy supply edge computing network composed of one gateway and 10 wireless devices as an example. The gateway integrates a radio frequency energy transmitter and an edge calculation server, the task calculation of the wireless device is based on time frames, and T is the length of one time frame.
AT the beginning of each time frame, the gateway wirelessly supplies power to 10 wireless devices, wherein the wireless power supply duration is a frame length which is a times of the wireless power supply duration, namely the power supply duration is aT,0< a <1, and a is the power supply duration proportion.
The energy captured by the i-th wireless device is denoted as E i＝μPhi aT, where μ is the energy capture efficiency, P is the rf energy transmit power of the rf energy transmitter, h i is the channel gain of the i-th device aT the current time frame, i E {1, 2..10 }, h= [ h 1,h2,…,h10 ] represents the channel gain of 10 wireless devices within one time frame.
In this embodiment, the ith device uses (1-x i)Ei energy for local computation of T-duration, x i Ei energy for task offload communications, x i is the energy ratio for task offload communications, and 1-x i is the energy ratio for local computation in each time frame.
Starting aT for supplying energy in each time frame, and in the rest time period (1-a) T, performing task unloading by using a part of captured energy by 10 devices based on wireless communication, wherein the task unloading communication of 10 devices is based on a time division multiplexing mode, the i-th wireless device performs task unloading communication by using x iEi energy, the unloading energy allocation proportion of 10 wireless devices is x= [ x 1,x2,…x10 ], the communication duration of the i-th device is T i times of frame length, and the communication duration of 10 wireless devices is t= [ T 1T,t2T,…t10 T ].
The range of the energy supply duration ratio in the embodiment is [0,1], and the step length is 0.01. The energy supply duration ratio takes on the value in [0,1] according to the step length of 0.01, namely the different value of the energy supply duration ratio a is 0.01,0.02. The step size used may be set to other values, for example 0.02, etc., depending on the actual situation.
Under the condition that the current energy supply duration proportion is determined, the energy proportion of each wireless device for task unloading communication and the task unloading communication duration proportion are calculated through the following steps:
Step S1.1, selecting a first wireless device from a wireless energy supply edge computing network, and initializing the range of the energy proportion of the first wireless device for task unloading communication.
For example, the range of the transmission task energy allocation ratio x 1 of the 1 st wireless device is initialized, x 1max represents the upper limit value of x 1, x 1max＝1,x1min represents the lower limit value of x 1, and x 1min =0.
Step S1.2, setting the energy ratio x 1 of the first wireless device for task offload communication as a mean value of the upper limit value and the lower limit value of the range, and calculating the energy ratio of any other wireless device for task offload communication according to the following formula:
Wherein x 1 is the energy ratio of the first wireless device for task offload communication, x i is the energy ratio of the ith wireless device for task offload communication, h 1 is the channel gain of the first wireless device, and h i is the channel gain of the ith wireless device.
The embodiment initializes x 1 to the value
According toA value of x i is obtained where i e {2,3,..10 }, so that the energy fraction of each wireless device for task offload communications can be obtained.
Step S1.3, calculating the task unloading communication duration proportion of the wireless device according to the following formula:
Where t i is the i-th wireless device task offload communication duration ratio, B is the wireless communication bandwidth, phi is the number of CPU cycles required by the wireless device to locally process a bit task, k e is the calculated energy efficiency coefficient of the wireless device, N 0 is the noise power, a is the energy supply duration ratio, μ is the energy capture efficiency, and P is the radio frequency energy transmit power of the radio frequency energy transmitter.
And the parameters are brought into a formula, so that the task unloading communication duration proportion of each wireless device can be obtained.
Step S1.4, atIf the search accuracy is greater than xiThen taking x 1 as the upper limit value of the range of the energy proportion of the first wireless device for task offloading communication, updating the range of the energy proportion of the first wireless device for task offloading communication and returning to the step S1.2, otherwise taking x 1 as the lower limit value of the range of the energy proportion of the first wireless device for task offloading communication, updating the range of the energy proportion of the first wireless device for task offloading communication and returning to the step S1.2, wherein the step S1.2 is thatAnd when the search precision xi is smaller than or equal to the search precision xi, calculating the total calculation rate of the wireless energy supply edge calculation network, and ending the iteration.
The number of wireless devices in this embodiment is N, and in the case of setting the search accuracy ζ, by the above determination, inAnd when the search accuracy xi is greater than the search accuracy xi, updating the range of the energy proportion of the first wireless device for task unloading communication, and continuing to return to the step S1.2 for recalculation.
In particular, ifX 1max＝x1 is set, and the step returns to the step S1.2, and the energy proportion and the task unloading communication duration proportion of each wireless device for task unloading communication are calculated again. If it isX 1min＝x1 is set, and the step returns to the step S1.2, and the energy proportion and the task unloading communication duration proportion of each wireless device for task unloading communication are calculated again.
Up toAnd when the search precision xi is smaller than or equal to the search precision xi, calculating the total calculation rate of the wireless energy supply edge calculation network, and ending the iteration.
Step S2, selecting the energy supply duration proportion when the total calculation rate of the wireless energy supply edge calculation network is maximum, setting the radio frequency energy transmitter to wirelessly supply energy to the wireless equipment at the beginning of each time frame, configuring each wireless equipment to carry out task unloading communication according to the task unloading communication duration and the energy distribution proportion corresponding to the selected energy supply duration proportion in each time frame, and configuring each wireless equipment to carry out local task calculation according to the residual energy in each time frame.
For any selected energy supply duration proportion a, the total calculation rate of the wireless energy supply edge calculation network, the energy proportion of each corresponding wireless device for task unloading communication and the task unloading communication duration proportion can be calculated. And finally, selecting the energy supply duration proportion when the total calculation rate is the maximum, and the energy proportion and the task unloading communication duration proportion of each corresponding wireless device for task unloading communication as parameters of the wireless edge calculation network to work.
Specifically, the radio frequency energy transmitter is set to wirelessly supply energy to the wireless device aT the beginning of each time frame according to the selected energy supply duration proportion, and the radio frequency energy transmitter wirelessly supplies energy to the wireless device within the energy supply duration aT after the beginning of the current time frame, which belongs to energy supply time.
And configuring each wireless device to carry out task unloading communication according to the task unloading communication duration corresponding to the selected energy supply duration proportion and the energy proportion for the task unloading communication in each time frame. Each wireless device is configured to perform local task calculations with the remaining energy in each time frame.
According to the energy supply time length obtained through calculation, the communication time length of each wireless equipment task unloading and the occupied energy distribution proportion, a wireless energy supply edge calculation network is further configured to work.
And each wireless device performs task unloading according to the calculated task unloading communication duration. All wireless devices carry out task unloading communication in a time division multiplexing mode, and the communication duration of the ith wireless device is T i T. And performing energy allocation with the calculated energy ratio for the task offload communication, the ith wireless device performs the task offload communication with the energy of x iEi.
Each wireless device also performs local task calculations, which can be performed with the remaining energy throughout the time frame, and the i-th device performs local calculations of the T-duration with the remaining energy (1-x i)Ei).
In a specific embodiment, for a given energization period ratio a, the wireless energization edge calculates a network total calculated rate Q (x, t) as:
Where φ is the number of CPU cycles required by the wireless device to process a bit task locally, k e is the computational energy efficiency coefficient of the wireless device, B is the wireless communication bandwidth, N 0 is the noise power, Q (x, t) is a concave function about x i、ti and is 0.ltoreq.t i.ltoreq.1, And x i is more than or equal to 0 and less than or equal to 1, and the energy distribution ratio x and the communication duration t of the maximized Q (x, t) under the given a are obtained by adopting the existing methods for solving the convex optimization problem, such as an interior point method, a Lagrange dual method and the like.
Where N is the number of wireless devices, t i is the task offload communication duration ratio of the ith wireless device, and the communication duration of the ith wireless device is t iT.xi is the energy ratio for task offload communication.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (4)
1. A method for resource allocation of a wireless powered edge computing network, the wireless powered edge computing network comprising a wireless device and a gateway, the gateway comprising a radio frequency energy transmitter and an edge computing server, the method comprising:
Step S1, sequentially selecting a current energy supply duration proportion from a value range of the energy supply duration proportion according to a set step length, and carrying out the following iteration to determine the energy proportion of each wireless device for task unloading communication and the task unloading communication duration proportion under the condition of the current energy supply duration proportion;
Step S1.1, selecting a first wireless device from a wireless energy supply edge computing network, and initializing the range of the energy proportion of the first wireless device for task unloading communication;
Step S1.2, setting the energy ratio x 1 of the first wireless device for task offload communication as a mean value of the upper limit value and the lower limit value of the range, and calculating the energy ratio of any other wireless device for task offload communication according to the following formula:
Wherein x 1 is the energy proportion of the first wireless device for task offload communication, x i is the energy proportion of the ith wireless device for task offload communication, h 1 is the channel gain of the first wireless device, and h i is the channel gain of the ith wireless device;
step S1.3, calculating the task unloading communication duration proportion of the wireless device according to the following formula:
Wherein t i is the task unloading communication duration proportion of the ith wireless device, B is the wireless communication bandwidth, phi is the CPU cycle number required by the wireless device to locally process a bit task, k e is the calculation energy efficiency coefficient of the wireless device, N 0 is the noise power, a is the energy supply duration proportion, mu is the energy capturing efficiency, and P is the radio frequency energy transmitting power of the radio frequency energy transmitter;
Step S1.4, at If the search accuracy is greater than xiThen taking x 1 as the upper limit value of the range of the energy proportion of the first wireless device for task offloading communication, updating the range of the energy proportion of the first wireless device for task offloading communication and returning to the step S1.2, otherwise taking x 1 as the lower limit value of the range of the energy proportion of the first wireless device for task offloading communication, updating the range of the energy proportion of the first wireless device for task offloading communication and returning to the step S1.2, wherein the step S1.2 is thatWhen the search precision xi is smaller than or equal to the search precision xi, calculating the total calculation rate of the wireless energy supply edge calculation network, and ending iteration;
Step S2, selecting the energy supply duration proportion when the total calculation rate of the wireless energy supply edge calculation network is maximum, setting the radio frequency energy transmitter to wirelessly supply energy to the wireless equipment at the beginning of each time frame, configuring each wireless equipment to carry out task unloading communication according to the task unloading communication duration and the energy distribution proportion corresponding to the selected energy supply duration proportion in each time frame, and configuring each wireless equipment to carry out local task calculation according to the residual energy in each time frame.
2. The method for allocating resources of a wireless powered edge computing network of claim 1, wherein the computing wireless powered edge computing network has a total computing rate as follows:
where N is the number of wireless devices.
3. The method for resource allocation of a wireless powered edge computing network of claim 1, wherein the wireless devices communicate task offloading in a time division multiplexed manner.
4. The method for allocating resources in a wireless energy-supplying edge computing network according to claim 1, wherein the energy-supplying duration ratio has a value range of [0,1] and a step size of 0.01.
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