CN116249143A - Time delay optimization method and system for unmanned aerial vehicle auxiliary movement edge calculation - Google Patents
Time delay optimization method and system for unmanned aerial vehicle auxiliary movement edge calculation Download PDFInfo
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Abstract
The invention discloses a time delay optimization method and a time delay optimization system for unmanned aerial vehicle auxiliary movement edge calculation, wherein the time delay optimization method comprises the following steps: acquiring channel state information among all nodes based on a mobile edge computing system, and computing channel capacity among all nodes according to the channel state information; acquiring unloading time of user tasks and calculation time of a mobile edge calculation server based on channel capacity, and acquiring task processing time delay of each user according to the unloading time and the calculation time; based on the user task processing time delay and the flying height of the unmanned aerial vehicle and the task allocation factors, a joint optimization problem model of task allocation and the position of the unmanned aerial vehicle is constructed. According to the invention, the two modes of direct unloading and auxiliary unloading through the unmanned aerial vehicle relay are considered when a user performs task unloading, and the system time delay of multi-user task processing is effectively reduced by jointly optimizing the task allocation of two paths and the position of the unmanned aerial vehicle relay.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to a time delay optimization method and a time delay optimization system for unmanned aerial vehicle auxiliary mobile edge calculation.
Background
With the development of the internet of things technology, the application of virtual reality and automatic driving technologies is becoming wider and wider. Because of the limited computing power of the internet of things devices, it is difficult to handle computationally intensive and time delay sensitive tasks. Mobile edge computing (Mobile Edge Computing, MEC) technology is considered to be one solution to the above-described problems, where MEC servers may be deployed around internet of things devices to provide cloud computing services for the internet of things devices. The internet of things equipment can be used for processing by unloading the computing task to the nearby MEC server, so that the data processing time delay is effectively reduced, and the computing resource is saved. In an emergency scene or an area with poor coverage of a base station, the MEC technology is combined with the unmanned aerial vehicle communication technology, so that flexible and reliable calculation unloading service is provided for user equipment on the ground.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
The present invention has been made in view of the above-described problems occurring in the prior art.
Therefore, the invention provides a time delay optimization method and a time delay optimization system for unmanned aerial vehicle auxiliary movement edge calculation, which solve the problems of prolonged calculation and low sensitivity of Internet of things equipment in an emergency scene or an area with poor coverage of a base station.
In order to solve the technical problems, the invention provides the following technical scheme:
in a first aspect, an embodiment of the present invention provides a delay optimization method for unmanned aerial vehicle auxiliary mobile edge calculation, including:
acquiring channel state information among nodes based on a mobile edge computing system, and computing channel capacity among the nodes according to the channel state information;
acquiring unloading time of user tasks and calculation time of a mobile edge calculation server based on the channel capacity, and acquiring task processing time delay of each user according to the unloading time and the calculation time;
and constructing a joint optimization problem model of task allocation and unmanned aerial vehicle position based on the user task processing time delay and the flying height of the unmanned aerial vehicle and the task allocation factor.
As a preferable scheme of the time delay optimization method for unmanned aerial vehicle auxiliary movement edge calculation, the invention comprises the following steps: the obtaining the channel state information among the nodes comprises the following steps: channel state information relayed from the ground user to the unmanned aerial vehicle, channel state information relayed from the unmanned aerial vehicle to the base station and channel state information relayed from the ground user to the base station;
the channel state information of the ground user to unmanned aerial vehicle relay comprises the line-of-sight propagation probability
Path loss
And small scale fading->
；
The channel state information relayed by the unmanned aerial vehicle to the base station comprises line-of-sight propagation probability
Path loss->
And small scale fading->
；
The channel state information from the ground user to the base station comprises path loss
And small scale fading->
。
As a preferable scheme of the time delay optimization method for unmanned aerial vehicle auxiliary movement edge calculation, the invention comprises the following steps: respectively calculating channel capacities of a ground user to unmanned aerial vehicle relay, an unmanned aerial vehicle relay to a base station and the ground user to the base station according to the channel state information, wherein the method comprises the following steps:
the traversing channel capacity from the ground user to the unmanned aerial vehicle relay is expressed as:
wherein ,
representing the desired value operator,/->
Representing->
Upper bound of traversal channel capacity to unmanned relay r, +.>
Representing a mobile edge computing system to a ground user +.>
Allocated bandwidth->
Representing the ground user +.>
Transmit power of>
Representing noise power in a mobile edge computing system;
the traversing channel capacity of the unmanned aerial vehicle relay to the base station is expressed as:
wherein ,
representing the upper bound of the traversed channel capacity from the drone relay r to base station b, +.>
Representing the forwarding power of the unmanned aerial vehicle relay;
the traversing channel capacity from the ground user to the base station is expressed as:
wherein ,
representing->
To the upper bound of the traversed channel capacity of base station b.
As a preferable scheme of the time delay optimization method for unmanned aerial vehicle auxiliary movement edge calculation, the invention comprises the following steps: acquiring task processing time delay of each user, including:
acquiring unloading time of a user task directly reaching a base station, wherein the unloading time of the user task reaching the base station through an unmanned aerial vehicle and the calculating time of a mobile edge calculating server for processing the user task are obtained, and taking the sum of the unloading time and the calculating time as task processing time delay of each user;
the transmission delay of the user task directly unloaded to the base station is expressed as:
wherein ,
data quantity representing user computing task, +.>
Representing the ground user +.>
The channel capacity to base station b,
task allocation factor representing the direct offloading of computing tasks by the user to the base station and +.>
；
The transmission delay of the user task unloaded to the base station through the unmanned aerial vehicle relay is expressed as:
wherein ,
representing the ground user +.>
Channel capacity to drone relay r, +.>
The channel capacity from the unmanned aerial vehicle relay r to the base station b is represented;
the calculation time delay of the mobile edge calculation server for processing the user task is expressed as:
wherein ,
representing moving edgesThe edge compute server processes the number of CPU cycles required for each bit of compute task,
representing the computation frequency of the mobile edge computation server.
As a preferable scheme of the time delay optimization method for unmanned aerial vehicle auxiliary movement edge calculation, the invention comprises the following steps: further comprises:
the processing delay of each user task is expressed as:
wherein ,
transmission delay indicating direct offloading of user tasks to base station, < >>
Transmission delay representing offloading of user tasks via unmanned aerial vehicle relay to base station, +.>
Representing the computation time delay of the mobile edge computation server to process the user task;
the system delay of multi-user task processing is defined as the maximum task processing delay in each user, and is expressed as:
wherein ,
representing the processing delay of each user task.
As a preferable scheme of the time delay optimization method for unmanned aerial vehicle auxiliary movement edge calculation, the invention comprises the following steps: the construction of the joint optimization problem model comprises the following steps: the task allocation factors and the flying height of the unmanned aerial vehicle are taken as constraints, the system time delay for minimizing multi-user task processing is taken as a target, and a joint optimization problem model of the task allocation and the unmanned aerial vehicle position is constructed and expressed as follows:
wherein C1 represents the value range constraint of the user task allocation factor, C2 represents the altitude range constraint of the unmanned aerial vehicle flight,
and />
Representing the minimum height and maximum height, respectively, that the unmanned aerial vehicle can fly.
As a preferable scheme of the time delay optimization method for unmanned aerial vehicle auxiliary movement edge calculation, the invention comprises the following steps: further comprises: and solving an optimization problem by utilizing a heuristic iterative algorithm of the particle swarm according to the joint optimization problem model, and obtaining a task allocation factor and an optimal unmanned aerial vehicle position.
In a second aspect, an embodiment of the present invention provides a delay optimization system for unmanned aerial vehicle assisted mobile edge calculation, including:
the data acquisition and calculation module is used for acquiring channel state information among all nodes based on a mobile edge calculation system and calculating channel capacity among all nodes according to the channel state information;
the time delay acquisition module is used for acquiring the unloading time of the user task and the calculation time of the mobile edge calculation server based on the channel capacity, and acquiring the task processing time delay of each user according to the unloading time and the calculation time;
the model building module is used for building a joint optimization problem model of task distribution and the position of the unmanned aerial vehicle based on the user task processing time delay and the flying height of the unmanned aerial vehicle and the task distribution factors.
In a third aspect, embodiments of the present invention provide a computing device comprising:
a memory and a processor;
the memory is configured to store computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to implement a method of latency optimization for unmanned aerial vehicle-assisted mobile edge computation according to any of the embodiments of the present invention.
In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement a method for latency optimization for unmanned aerial vehicle assisted movement edge computation.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, two modes of direct unloading and auxiliary unloading through unmanned aerial vehicle relay exist when a user performs task unloading, the task allocation of two paths and the position of unmanned aerial vehicle relay are jointly optimized, the system delay for minimizing multi-user task processing is taken as a target, the task allocation factor and the height range of unmanned aerial vehicle flight are taken as constraints, the heuristic iterative algorithm based on particle swarm is designed to solve the optimization problem, and the system delay for multiuser task processing is effectively reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is a flowchart of a method and a system for delay optimization of unmanned aerial vehicle auxiliary movement edge calculation according to an embodiment of the present invention;
fig. 2 is a system model diagram of a method and a system for optimizing time delay of unmanned aerial vehicle auxiliary movement edge calculation according to an embodiment of the invention;
fig. 3 is a diagram of simulation results of a relationship between a user task amount and a system delay of a method and a system for optimizing a delay of an unmanned aerial vehicle auxiliary mobile edge calculation according to an embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Example 1
Referring to fig. 1 to fig. 2, in an embodiment of the present invention, a delay optimization method for unmanned aerial vehicle auxiliary movement edge calculation is provided, including:
s1, obtaining channel state information among nodes based on a mobile edge computing system, and computing channel capacity among the nodes according to the channel state information;
further, the mobile edge computing system comprises a base station, an unmanned aerial vehicle relay and a plurality of ground users; the base station is provided with an MEC server, the ground user needs to offload the computationally intensive tasks to the MEC server for processing, and the unmanned aerial vehicle serves as a relay auxiliary user to offload the tasks.
Further, obtaining channel state information between nodes includes: channel state information relayed from the ground user to the unmanned aerial vehicle, channel state information relayed from the unmanned aerial vehicle to the base station and channel state information relayed from the ground user to the base station;
the channel state information of the ground user to unmanned aerial vehicle relay comprises the line-of-sight propagation probability
Path loss->
And small scale fading->
；
The channel state information relayed by the unmanned aerial vehicle to the base station comprises line-of-sight propagation probability
Path loss->
And small scale fading->
；
Channel state information for terrestrial users to base stations including path loss
And small scale fading->
。
Specifically, a ground user
The line-of-sight propagation probability to the unmanned aerial vehicle relay r is expressed as:
wherein ,
representing a ground user and +.>
，/>
Representing the position of the unmanned aerial vehicle relative to the user +.>
The pitch angle generated by the position of the lens,hfor the altitude of the unmanned aerial vehicle flight, +.>
The distance from the ground user to the unmanned aerial vehicle relay is provided; />
and />
Is an environment-dependent parameter that has different values corresponding to a city, suburban area, dense user urban area, or tall building urban area.
Specifically, a ground user
The path loss to the drone relay r is expressed as:
wherein ,
is the path loss at the reference point, +.>
Is the path loss factor, ">
Is the extra path loss factor that non-line-of-sight propagation brings relative to line-of-sight propagation.
Specifically, a ground user
The small-scale fading to the unmanned aerial vehicle relay r obeys the rice distribution, expressed as:
wherein ,
lesi factor representing ground user to drone relay channel,>
representing LoS component in ground user to drone relay channel fading, +.>
Representing the NLoS component in the ground user to drone relay channel fading.
Specifically, the line-of-sight propagation probability from the unmanned aerial vehicle relay r to the base station b is expressed as;
wherein ,
representing pitch angle generated by the position of the unmanned aerial vehicle relative to the position of the ground base station, < ->
Is the distance of the unmanned aerial vehicle relay to the ground base station.
Specifically, the path loss from the unmanned aerial vehicle relay r to the base station b is expressed as:
specifically, the small-scale fading of the unmanned aerial vehicle relay r to the base station b obeys the rice distribution, expressed as:
wherein ,
lesi factor indicating the relay of the drone to the ground base station channel,>
LoS component in signal channel fading representing relay of unmanned aerial vehicle to ground base station, < ->
Representing the NLoS component of the drone relaying to the terrestrial base station in channel fading.
Specifically, a ground user
The path loss to base station b is expressed as:
specifically, a ground user
The small-scale fading to base station b follows the rayleigh distribution.
Further, according to the channel state information, respectively calculating channel capacities of the ground user to unmanned aerial vehicle relay, the unmanned aerial vehicle relay to the base station and the ground user to the base station, including:
the traversal channel capacity of the ground user to drone relay is expressed as:
wherein ,
representing the desired value operator,/->
Representing->
Upper bound of traversal channel capacity to unmanned relay r, +.>
Representing a mobile edge computing system to a ground user +.>
Allocated bandwidth->
Representing the ground user +.>
Transmit power of>
Representing noise power in a mobile edge computing system;
specifically, the ground user-to-drone relay channel capacity expectation operator, expressed as:
wherein ,
representing the variance of the small-scale channel fading from the ground user to the drone relay.
The traversed channel capacity of the drone relay to the base station is expressed as:
wherein ,
representing the upper bound of the traversed channel capacity from the drone relay r to base station b, +.>
Representing the forwarding power of the unmanned aerial vehicle relay;
specifically, the expected value operator of the base station channel capacity relayed by the unmanned aerial vehicle is expressed as:
wherein ,
representing the variance of the small-scale channel fading of the drone relay to the base station.
The traversed channel capacity of a ground user to a base station is expressed as:
wherein ,
representing->
An upper bound of the traversed channel capacity to base station b;
specifically, the ground user to base station channel capacity expectation operator, expressed as:
wherein ,
representing the variance of small-scale channel fading from the terrestrial users to the base station.
It should be noted that the ground users do not have the ability to handle computationally intensive tasks and therefore need to divide their tasks into two parts, one part being directly offloaded to the MEC server at the base station for computation and the other part being forwarded to the MEC server at the base station for computation by the drone relay.
S2, acquiring unloading time of user tasks and calculation time of a mobile edge calculation server based on channel capacity, and acquiring task processing time delay of each user according to the unloading time and the calculation time;
further, acquiring the task processing delay of each user includes:
acquiring unloading time of a user task directly reaching a base station, wherein the unloading time of the user task reaching the base station through an unmanned aerial vehicle and the calculating time of a mobile edge calculating server for processing the user task are obtained, and taking the sum of the unloading time and the calculating time as the task processing time delay of each user;
the transmission delay of the user task directly offloaded to the base station is expressed as:
wherein ,
data quantity representing user computing task, +.>
Representing the ground user +.>
The channel capacity to base station b,
task allocation factor representing the direct offloading of computing tasks by the user to the base station and +.>
；
The transmission delay of the user task unloaded to the base station through the unmanned aerial vehicle relay is expressed as:
wherein ,
representing the ground user +.>
To unmanned planeChannel capacity of relay r>
The channel capacity from the unmanned aerial vehicle relay r to the base station b is represented;
the computation delay of the mobile edge computation server for processing the user task is expressed as:
wherein ,
representing the number of CPU cycles required by the mobile edge computing server to process each bit of computing task,
representing the computation frequency of the mobile edge computation server.
Still further, still include:
the processing delay of each user task is expressed as:
wherein ,
transmission delay indicating direct offloading of user tasks to base station, < >>
Transmission delay representing offloading of user tasks via unmanned aerial vehicle relay to base station, +.>
Representing the computation time delay of the mobile edge computation server to process the user task;
the system delay of multi-user task processing is defined as the maximum task processing delay in each user, and is expressed as:
wherein ,
representing the processing delay of each user task.
It should be noted that, since the task offloading is performed simultaneously by using a frequency division multiplexing manner between multiple users, that is, the maximum task processing delay in each user is defined as the system delay of multi-user task processing.
S3, constructing a joint optimization problem model of task allocation and unmanned aerial vehicle position based on the combination of user task processing time delay, the flying height of the unmanned aerial vehicle and task allocation factors;
further, the constructing of the joint optimization problem model includes: the task allocation factors and the flying height of the unmanned aerial vehicle are taken as constraints, the system time delay for minimizing multi-user task processing is taken as a target, and a joint optimization problem model of the task allocation and the unmanned aerial vehicle position is constructed and expressed as follows:
wherein C1 represents the value range constraint of the user task allocation factor, C2 represents the altitude range constraint of the unmanned aerial vehicle flight,
and />
Representing the minimum height and maximum height, respectively, that the unmanned aerial vehicle can fly.
Still further, still include: and solving an optimization problem by utilizing a heuristic iterative algorithm of the particle swarm according to the joint optimization problem model, and obtaining a task allocation factor and the optimal unmanned aerial vehicle position.
Specifically, the optimization problem is solved by using a heuristic iterative algorithm of the particle swarm, as shown in table 1:
table 1 task allocation factor and unmanned aerial vehicle position joint optimization algorithm based on particle swarm optimization
Preferably, the number of particles, the position and the speed of the particles are randomly initialized, the optimal solution of each particle and the optimal solution of all particles are obtained, and the maximum tolerance error is set;
if the difference value of the fitness function value of the current particle position and the fitness function value of the last position is larger than the maximum tolerance error, updating the particle speed and the particle position of the next iteration;
if the fitness function value of the current position of a certain particle is smaller than the fitness function value of the optimal solution of the particle, taking the current position of the particle as the optimal solution of the particle;
and if the fitness function value of the optimal solution of a certain particle is smaller than that of the optimal solution of all particles, taking the optimal solution of the particle as the new optimal solution of all particles.
The above is an exemplary scheme of a delay optimization method for unmanned aerial vehicle auxiliary movement edge calculation in this embodiment. It should be noted that, the technical solution of the delay optimization system for unmanned aerial vehicle auxiliary movement edge calculation and the technical solution of the delay optimization method for unmanned aerial vehicle auxiliary movement edge calculation belong to the same concept, and details of the technical solution of the delay optimization system for unmanned aerial vehicle auxiliary movement edge calculation in this embodiment, which are not described in detail, can be seen from the details of the delay optimization of the unmanned aerial vehicle auxiliary movement edge calculation system
Description of the technical scheme of the method.
Fig. 2 is a system of a delay optimization system for unmanned aerial vehicle auxiliary movement edge calculation provided by the invention
The model diagram can be applied to the situation of a time delay optimization method for unmanned aerial vehicle auxiliary moving edge calculation.
Referring to fig. 2, in this embodiment, a delay optimization system for unmanned aerial vehicle auxiliary movement edge calculation includes:
the data acquisition and calculation module is used for acquiring channel state information among all nodes based on the mobile edge calculation system and calculating channel capacity among all nodes according to the channel state information;
the time delay acquisition module is used for acquiring the unloading time of the user task and the calculation time of the mobile edge calculation server based on the channel capacity, and acquiring the task processing time delay of each user according to the unloading time and the calculation time;
the model building module is used for building a joint optimization problem model of task distribution and the position of the unmanned aerial vehicle based on the combination of the user task processing time delay, the flying height of the unmanned aerial vehicle and the task distribution factor.
The embodiment also provides a computing device, which is suitable for the situation of a time delay optimization method for unmanned aerial vehicle auxiliary movement edge calculation, and comprises the following steps:
a memory and a processor; the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions to implement the method for optimizing latency of unmanned aerial vehicle-assisted mobile edge computation according to the above embodiment.
The computer device may be a terminal comprising a processor, a memory, a communication interface, a display screen and input means connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
The present embodiment also provides a storage medium, on which a computer program is stored, which when executed by a processor implements a time delay optimization method for implementing unmanned aerial vehicle assisted mobile edge calculation as proposed in the above embodiment.
The storage medium according to the present embodiment belongs to the same inventive concept as the data storage method according to the above embodiment, and technical details not described in detail in the present embodiment can be seen in the above embodiment, and the present embodiment has the same advantageous effects as the above embodiment.
Example 2
Referring to fig. 3, the beneficial effects of the method of the present invention are verified by comparative experiments for one embodiment of the present invention.
In the embodiment, the comparison scheme adopts a mode of randomly deploying the positions of the equal-task-allocation unmanned aerial vehicle, the residual conditions are consistent with the method adopted by the task allocation and unmanned aerial vehicle position joint optimization scheme of the embodiment, and the simulation experiment is carried out through MATLAB.
The parameter settings in this embodiment are shown in table 2:
table 2 parameter setting data table
Further, the bandwidth allocated by the system to each user is 100MHz, and the problem of interference between the channels of multiple users is not existed in consideration of orthogonality of the channels. The user's transmit power is set to 1W, the drone relay adopts a decode-and-forward protocol, and the forward power is set to 2W.
By means of the parameter setting, simulation is carried out, and the simulation is compared with a random deployment scheme of the equal task allocation unmanned aerial vehicle position, the simulation result is shown in a figure, and the fact that two modes of direct unloading and auxiliary unloading through unmanned aerial vehicle relay exist when a user performs task unloading can be seen from the figure, and by means of joint optimization of task allocation of two paths and the position of the unmanned aerial vehicle relay, system time delay of multi-user task processing is effectively reduced, and the task processing agility is improved.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. The delay optimization method for the unmanned aerial vehicle auxiliary movement edge calculation is characterized by comprising the following steps of:
acquiring channel state information among nodes based on a mobile edge computing system, and computing channel capacity among the nodes according to the channel state information;
acquiring unloading time of user tasks and calculation time of a mobile edge calculation server based on the channel capacity, and acquiring task processing time delay of each user according to the unloading time and the calculation time;
and constructing a joint optimization problem model of task allocation and unmanned aerial vehicle position based on the user task processing time delay and the flying height of the unmanned aerial vehicle and the task allocation factor.
2. The method for optimizing delay of unmanned aerial vehicle assisted mobile edge calculation according to claim 1, wherein the obtaining channel state information between each node comprises: channel state information relayed from the ground user to the unmanned aerial vehicle, channel state information relayed from the unmanned aerial vehicle to the base station and channel state information relayed from the ground user to the base station;
the channel state information of the ground user to unmanned aerial vehicle relay comprises the line-of-sight propagation probability
Path loss
And small scale fading->
；
The unmanned aerial vehicle relays to the signal of the base stationThe track state information includes line-of-sight propagation probabilities
Path loss->
And small scale fading->
；
The channel state information from the ground user to the base station comprises path loss
And small scale fading->
。
3. The unmanned aerial vehicle assisted mobile edge computing time delay optimization method of claim 1 or 2, wherein computing the ground user to unmanned aerial vehicle relay, the unmanned aerial vehicle relay to base station, and the ground user to base station channel capacities, respectively, based on the channel state information, comprises:
the traversing channel capacity from the ground user to the unmanned aerial vehicle relay is expressed as:
wherein ,
representing the desired value operator,/->
Representing->
Upper bound of traversal channel capacity to unmanned relay r, +.>
Representing a mobile edge computing system to a ground user +.>
Allocated bandwidth->
Representing the ground user +.>
Transmit power of>
Representing noise power in a mobile edge computing system;
the traversing channel capacity of the unmanned aerial vehicle relay to the base station is expressed as:
wherein ,
representing the upper bound of the traversed channel capacity from the drone relay r to base station b, +.>
Representing the forwarding power of the unmanned aerial vehicle relay;
the traversing channel capacity from the ground user to the base station is expressed as:
wherein ,
representing->
To the upper bound of the traversed channel capacity of base station b.
4. The method for optimizing delay in unmanned aerial vehicle assisted mobile edge computation of claim 3, wherein obtaining a task processing delay for each user comprises:
acquiring unloading time of a user task directly reaching a base station, wherein the unloading time of the user task reaching the base station through an unmanned aerial vehicle and the calculating time of a mobile edge calculating server for processing the user task are obtained, and taking the sum of the unloading time and the calculating time as task processing time delay of each user;
the transmission delay of the user task directly unloaded to the base station is expressed as:
wherein ,
data quantity representing user computing task, +.>
Representing the ground user +.>
Channel capacity to base station b, is->
Task allocation factor representing the direct offloading of computing tasks by the user to the base station and +.>
；
The transmission delay of the user task unloaded to the base station through the unmanned aerial vehicle relay is expressed as:
wherein ,
representing the ground user +.>
Channel capacity to drone relay r, +.>
The channel capacity from the unmanned aerial vehicle relay r to the base station b is represented;
the calculation time delay of the mobile edge calculation server for processing the user task is expressed as:
wherein ,
representing the number of CPU cycles required by the mobile edge computing server to handle each bit of computing task,/for the computing task>
Representing the computation frequency of the mobile edge computation server.
5. The method for latency optimization of unmanned aerial vehicle assisted movement edge computation of claim 4, further comprising:
the processing delay of each user task is expressed as:
wherein ,
transmission delay indicating direct offloading of user tasks to base station, < >>
Transmission delay representing offloading of user tasks via unmanned aerial vehicle relay to base station, +.>
Representing the computation time delay of the mobile edge computation server to process the user task;
the system delay of multi-user task processing is defined as the maximum task processing delay in each user, and is expressed as:
wherein ,
representing the processing delay of each user task.
6. The method for optimizing the time delay of unmanned aerial vehicle-assisted mobile edge calculation according to claim 5, wherein the constructing of the joint optimization problem model comprises: the task allocation factors and the flying height of the unmanned aerial vehicle are taken as constraints, the system time delay for minimizing multi-user task processing is taken as a target, and a joint optimization problem model of the task allocation and the unmanned aerial vehicle position is constructed and expressed as follows:
wherein C1 represents the value range constraint of the user task allocation factor, C2 represents the altitude range constraint of the unmanned aerial vehicle flight,
and />
Representing the minimum height and maximum height, respectively, that the unmanned aerial vehicle can fly.
7. The method for latency optimization of unmanned aerial vehicle assisted movement edge computation of claim 6, further comprising: and solving an optimization problem by utilizing a heuristic iterative algorithm of the particle swarm according to the joint optimization problem model, and obtaining a task allocation factor and an optimal unmanned aerial vehicle position.
8. A delay optimization system for unmanned aerial vehicle assisted movement edge computation, comprising:
the data acquisition and calculation module is used for acquiring channel state information among all nodes based on a mobile edge calculation system and calculating channel capacity among all nodes according to the channel state information;
the time delay acquisition module is used for acquiring the unloading time of the user task and the calculation time of the mobile edge calculation server based on the channel capacity, and acquiring the task processing time delay of each user according to the unloading time and the calculation time;
the model building module is used for building a joint optimization problem model of task distribution and the position of the unmanned aerial vehicle based on the user task processing time delay and the flying height of the unmanned aerial vehicle and the task distribution factors.
9. A computing device, comprising:
a memory and a processor;
the memory is configured to store computer executable instructions that, when executed by the processor, implement the steps of the unmanned aerial vehicle assisted mobile edge computing latency optimization method of any of claims 1 to 7.
10. A computer readable storage medium storing computer executable instructions which when executed by a processor perform the steps of the unmanned aerial vehicle assisted movement edge computing delay optimization method of any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117376985A (en) * | 2023-12-08 | 2024-01-09 | 吉林大学 | Energy efficiency optimization method for multi-unmanned aerial vehicle auxiliary MEC task unloading under rice channel |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114268966A (en) * | 2021-12-27 | 2022-04-01 | 杭州电子科技大学 | Unmanned aerial vehicle-assisted MEC network low-delay multi-task distribution method and system |
| CN114979135A (en) * | 2022-04-22 | 2022-08-30 | 福建师范大学 | Unmanned aerial vehicle flight height and task allocation control method based on mobile edge calculation |
| CN115150891A (en) * | 2022-06-09 | 2022-10-04 | 南京邮电大学 | Interrupt probability auxiliary task unloading optimization method based on mobile edge calculation |
-
2023
- 2023-05-11 CN CN202310527651.5A patent/CN116249143B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114268966A (en) * | 2021-12-27 | 2022-04-01 | 杭州电子科技大学 | Unmanned aerial vehicle-assisted MEC network low-delay multi-task distribution method and system |
| CN114979135A (en) * | 2022-04-22 | 2022-08-30 | 福建师范大学 | Unmanned aerial vehicle flight height and task allocation control method based on mobile edge calculation |
| CN115150891A (en) * | 2022-06-09 | 2022-10-04 | 南京邮电大学 | Interrupt probability auxiliary task unloading optimization method based on mobile edge calculation |
Non-Patent Citations (1)
| Title |
|---|
| 董超;沈?;屈毓锛;: "基于无人机的边缘智能计算研究综述", 智能科学与技术学报, no. 03 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117376985A (en) * | 2023-12-08 | 2024-01-09 | 吉林大学 | Energy efficiency optimization method for multi-unmanned aerial vehicle auxiliary MEC task unloading under rice channel |
| CN117376985B (en) * | 2023-12-08 | 2024-03-19 | 吉林大学 | Energy efficiency optimization method for multi-unmanned aerial vehicle auxiliary MEC task unloading under rice channel |
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