CN111600648B - Mobile relay position control method of mobile edge computing system - Google Patents

Mobile relay position control method of mobile edge computing system Download PDF

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CN111600648B
CN111600648B CN202010451720.5A CN202010451720A CN111600648B CN 111600648 B CN111600648 B CN 111600648B CN 202010451720 A CN202010451720 A CN 202010451720A CN 111600648 B CN111600648 B CN 111600648B
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uav
mobile relay
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relay device
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CN111600648A (en
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张国鹏
周世斌
刘鹏
肖硕
张丙鑫
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China University of Mining and Technology CUMT
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
    • H04B7/18502—Airborne stations
    • H04B7/18504—Aircraft used as relay or high altitude atmospheric platform
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/02—Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
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Abstract

A mobile relay position control method of a mobile edge computing system is suitable for a wireless mobile network. The mobile relay equipment provides data relay service for the user equipment and the base station and provides data calculation service within calculation capability for the user equipment; when the calculation force requirement of the task exceeds the calculation capability of the mobile relay equipment, the mobile relay equipment sends the current task to a server of a base station for processing, and then the current task is sent to the user equipment through the mobile relay equipment in a relay mode. The method optimizes the position of the mobile relay by using the size of the task data volume, and finds the best compromise scheme of the distance of the mobile relay and the condition of a communication channel, thereby achieving the aim of minimizing the task completion time of an application program in user equipment.

Description

Mobile relay position control method of mobile edge computing system
Technical Field
The invention relates to a mobile relay position control method of a mobile edge computing system, in particular to a mobile relay position control method of a mobile edge computing system, which is suitable for a wireless mobile network.
Background
With the rapid development of wireless mobile networks and mobile applications, UE applications are more and more computationally scaled and are more sensitive to computational delay. Due to the limited computing resources of the UE, it is difficult to execute large mobile applications on the premise of satisfying the computation delay. The emerging MEC technology becomes an effective way for relieving the shortage of UE computing resources, and the traditional wireless access network has the conditions of service localization and close-range deployment, so that computing, storage and communication services can be more conveniently provided for the UE, thereby reducing network operation, reducing time delay and improving user experience. However, in some application scenarios, such as a mountain area, a post-disaster environment, etc., a direct communication link between the UE and the BS is blocked by an obstacle or the UE is not in the coverage of the BS at all, so that the UE cannot use the MEC service provided by the BS. The UAV has the characteristics of strong maneuverability, high flexibility and the like, and the multi-rotor UAV can vertically take off, land or hover in the air. The UAV is used for carrying the micro base station and the MEC equipment to fly or hover over the coverage area of the UE, so that the UE can be assisted to complete tasks such as data collection, real-time calculation and the like. The UAV can also be used as a relay node to forward the task input data of the UE to the BS for execution, so that the task execution speed is increased, and the task completion time is shortened.
However, in existing UAV assisted MEC systems, most of the research is focused on minimizing the energy consumption of the UAV and the UE by optimizing the resource allocation strategy in the system, and not on how to minimize the task completion time of the application in the UE by optimizing the hover position of the UAV to obtain the best communication channel conditions in the system. Since most of the currently computationally intensive applications are delay sensitive, minimizing the task completion time by finding the best UAV hover position is an effective means to reduce system latency, improving the user experience.
At present, there are a lot of studies in the direction of UAV assisted MEC systems by many domestic and foreign scholars, and the following solutions are proposed:
document 1: zhou, Y.Wu, H.Sun, Z.Chu, "UAV-enabled mobile edge computing: Offloading optimization and project design", Proc.IEEE ICC, pp.1-6, Feb.2018.
Document 2: hu, M.Jiang, Q.Zhang, Q.Li, and Jianyin Qin, "Joint optimization of UAV position, time slot allocation, and computation task partition in multiple user biological-edge computing systems," IEEE Transactions on vehicle Technology, vol.68, pp.7231-7235, July.2019.
Document 3: du, K.Yang, K.Wang, G.Zhang, Y.Zhao, and D.Chen, "Joint resources and work flow scheduling in UAV-enabled wireless-powered MEC for IOT systems," IEEE Transactions on vehicle Technology, pp.1-14, Oct.2019.
Document 4: royal resole, chinese dream, marteng, schopper, yan standing super, shaochuan, zhao fei, zhuang wei, niu bronze, unmanned aerial vehicle assistant mobile edge computing system and information bit distribution method thereof, invented patent 201810755471.
Literature 1 studies a radio power over cellular (MEC) system supported by a UAV, and jointly optimizes a calculation bit unloaded by a UE and a CPU calculation frequency of the UE and the UAV, so as to reduce the calculation energy consumption, flight energy consumption and transmission power of the UAV to the maximum extent. Document 2 studies how a UAV serving as an MEC server provides MEC services to a plurality of UEs on the ground, and by optimizing the problems of time slice division of tasks and grouping of UE calculation tasks, the energy consumption of all UEs in the system is minimized while ensuring that all UEs successfully complete the calculation tasks within a time period. Document 3 studies a UAV supported MEC system, in which a UAV can both wirelessly rf charge UEs and provide MEC services and employs time division multiple access based workflow allocations to minimize UAV hover energy consumption and computational energy consumption by optimizing association of UEs with UAVs, UAV computational resource allocation and power supply durations, UAV hover times, and UE service sequences. Document 4 performs joint optimization on the UE local calculation amount and the transmission data amount, and the calculation amount and the feedback data amount of the UAV, so that the UE data processing energy consumption and the data transmission energy consumption are effectively reduced.
However, the above document only considers that the UAV serves as an MEC server to provide MEC service for the UE, minimizes system energy consumption by optimizing task scheduling, computing resource allocation, and the like, and does not consider optimizing a UAV hovering position and optimizing a communication channel condition between the UAV and the UE, so as to accelerate execution of a UE computing task and reduce task time. Furthermore, the above document does not consider the effect of task partitioning and possibly the priority order of large applications on the computation latency.
Disclosure of Invention
Aiming at the technical problems, the mobile relay position control method of the mobile edge computing system is simple in step, good in using effect and capable of finishing the comprehensive task of program computation and wireless transmission at the fastest speed.
In order to achieve the technical purpose, the mobile relay position control method of the mobile edge computing system comprises User Equipment (UE), a mobile relay device (UAV) and a Base Station (BS) with a high-performance edge computing server, wherein the mobile relay device (UAV) has light computing capability and provides light-weight edge computing service for the User Equipment (UE), the high-performance edge computing server of the BS has strong computing capability, and the mobile relay device (UAV) and the BS are combined to solve the problem of insufficient computing resources of the User Equipment (UE);
the user equipment UE cannot be directly in wireless connection with the base station BS, the user equipment UE can be in wireless data connection with the base station BS only through the relay of the mobile relay equipment UAV, and the mobile relay equipment UAV provides data relay service for the user equipment UE and the base station BS and provides data calculation service within computing power for the user equipment UE; when User Equipment (UE) sends a calculation request, a current task is divided into a plurality of subtasks, the plurality of subtasks are sequentially sent to a mobile relay device (UAV) and calculation capacity support required by task calculation is judged in the mobile relay device (UAV), when the calculation capacity requirement of the subtasks is smaller than the calculation capacity of the mobile relay device (UAV), the corresponding subtasks are directly processed in the mobile relay device (UAV), the final calculation result is fed back to the User Equipment (UE), when the calculation capacity requirement of the subtasks exceeds the calculation capacity of the mobile relay device (UAV), the mobile relay device (UAV) sends the current subtasks to a server of a Base Station (BS) for processing, and when the processing is completed, the calculation results of the subtasks are sent to the User Equipment (UE) through the mobile relay device (UAV), so that three calculation task scheduling modes exist: calculating a task scheduling mode I: the former sub-task is executed at the mobile relay device UAV, and the latter sub-task is calculated at the base station BS; and a calculation task scheduling mode II: both the previous subtask and the next subtask perform calculations at the mobile relay device UAV; and calculating a task scheduling mode III: the former subtask and the latter subtask are both calculated in the base station BS; for two subtasks in any kth task group, respectively searching the position of the optimal wireless relay UAV of each subtask by using a basic PSO algorithm for the three calculation task scheduling modes, then selecting the task scheduling mode with the least time spent in the three calculation task scheduling modes as an execution scheme of the two subtasks, and repeatedly comparing the three calculation task scheduling modes for every two subtasks until all subtasks are completed.
Based on three calculation task scheduling modes, the mobile position of the mobile relay device UAV is optimized according to the size of the task data volume unloaded by the user equipment UE, the best compromise of the mobile distance and the communication channel condition of the mobile relay device UAV is found, and the completion of the calculation task of the user equipment UE is accelerated through the parallel execution of the mobile relay device UAV and the base station BS, so that the purpose of minimizing the completion time of all tasks of the UE is achieved, the system delay is reduced, and the user experience is improved;
specifically, the mobile relay UAV collects task data of the user equipment UE when performing a task, and relays the task data to the base station BS, and by controlling the position of the mobile relay UAV, the task completion time is minimized, and the closer the mobile relay UAV is to the user equipment UE and the base station BS, the better the wireless channel condition is, the better the bubble individual decreases the data transmission time, but the moving time of the mobile relay UAV may also be increased.
When User Equipment (UE) executes an application program, the application program is divided into K task groups with priority orders, namely the current task group can not be executed continuously until the execution result of the direct previous task group returns; in K task groups, each task group comprises n independent subtasks, and in order to facilitate parallel calculation of the mobile relay UAV and the base station BS, the subtasks in each task group are divided into two parts according to the data size, wherein the first part is a task 1, and the second part is a task 2;
first of all input into the user terminal UE and the base station BSCoordinates, setting the initial position coordinates of the wireless relay UAV to
Figure GDA0003469152920000031
Before K task groups do not finish operation, the formula is used:
Figure GDA0003469152920000032
Figure GDA0003469152920000033
Figure GDA0003469152920000034
respectively calculating the time required by the three calculation task scheduling modes, wherein
Figure GDA0003469152920000035
The relay position is determined according to the position coordinate u of the mobile relay UAV, the formula is determined, the optimal relay position is obtained by utilizing a basic particle swarm algorithm, the required time is further determined, and then the time is compared with the time of other two schemes;
in the formula:
Figure GDA0003469152920000036
the time used for the task scheduling scheme I,
Figure GDA0003469152920000037
for the time used by the task scheduling scheme II,
Figure GDA0003469152920000038
the time used for the task scheduling scheme III,
Figure GDA0003469152920000039
for the time required for the mobile relay UAV to move from the initial position when processing the kth task set to the position when receiving task 1 data,
Figure GDA0003469152920000041
relaying UAVs for movement while processing kth task groupTime required for the initial position to move to the position when the task 2 data is received;
Figure GDA0003469152920000042
the time required for the mobile relay UAV to receive the data for task 1 in the kth task group,
Figure GDA0003469152920000043
the time required for the mobile relay UAV to receive data for task 2 in the kth task group;
Figure GDA0003469152920000044
the time required for the mobile relay UAV to perform task 1 in the k-th task group,
Figure GDA0003469152920000045
the time required for the mobile relay UAV to perform task 2 in the kth task group;
Figure GDA0003469152920000046
the time required for the mobile relay UAV to move from a location receiving mission 1 data to relay this data location to the base station BS;
Figure GDA0003469152920000047
the time required for the mobile relay UAV to relay the received data for task 1 in the kth task group to the base station BS,
Figure GDA0003469152920000048
for the mobile relay UAV to relay the received data for task 2 in the kth task group to the base station BS,
Figure GDA0003469152920000049
express get
Figure GDA00034691529200000410
And
Figure GDA00034691529200000411
the larger of the two values is the one,
Figure GDA00034691529200000412
express get
Figure GDA00034691529200000413
And
Figure GDA00034691529200000414
the larger of the two values;
selecting
Figure GDA00034691529200000415
And
Figure GDA00034691529200000416
the minimum value among the three is obtained, and the data collection position of the mobile relay UAV corresponding to the minimum value among the three is obtained simultaneously
Figure GDA00034691529200000417
And relay location
Figure GDA00034691529200000418
u represents a position coordinate representation symbol of the mobile relay UAV, including actual coordinate information, and the most basic particle swarm algorithm is utilized to find the optimal u;
updating location parameters of the mobile relay UAV: if the task scheduling scheme I is used for the shortest time, then
Figure GDA00034691529200000419
If the task scheduling scheme III is used for the shortest time, then
Figure GDA00034691529200000420
Then order
Figure GDA00034691529200000421
Otherwise, the time for the task scheduling scheme II is shortest, so
Figure GDA00034691529200000422
That is to say: per executionAfter completing a task group of user terminal UE, the initial position of wireless relay UAV needs to be updated, if yes
Figure GDA00034691529200000423
Or
Figure GDA00034691529200000424
At this time, the wireless relay UAV needs to relay at least one task to the base station BS for execution, so the initial position of the next task group (k +1 th task group) is the relay hovering position when relaying the data of task 2, that is, the wireless relay UAV is capable of relaying the data of task 2
Figure GDA00034691529200000425
If it is
Figure GDA00034691529200000426
It is stated that task 1 and task 2 of the kth task group are both performed by the UAV in the shortest amount of time, so the initial position of the next task group is the data collection position of task 2, i.e., the position
Figure GDA00034691529200000427
Figure GDA00034691529200000428
According to the formula
Figure GDA00034691529200000429
Calculated, where | · | | | represents the mobile relay UAV relay location
Figure GDA00034691529200000430
And
Figure GDA00034691529200000431
the euclidean distance between;
Figure GDA00034691529200000432
according to the formula
Figure GDA00034691529200000433
ComputingObtaining;
Figure GDA00034691529200000434
according to the formula
Figure GDA00034691529200000435
Is obtained by calculation, wherein
Figure GDA00034691529200000436
Denotes bk，1Performing a multiplication operation with c;
Figure GDA00034691529200000437
according to the formula
Figure GDA00034691529200000438
Calculating to obtain;
Figure GDA00034691529200000439
according to the formula
Figure GDA00034691529200000440
Calculating to obtain;
Figure GDA00034691529200000441
according to the formula
Figure GDA00034691529200000442
Calculating to obtain;
Figure GDA00034691529200000443
according to the formula
Figure GDA00034691529200000444
Calculating to obtain;
Figure GDA00034691529200000445
according to the formula
Figure GDA00034691529200000446
Is obtained by calculation, wherein (b)k，2And c) represents bk，2Performing a multiplication operation with c;
Figure GDA00034691529200000447
according to the formula
Figure GDA00034691529200000448
Calculating to obtain;
Figure GDA00034691529200000449
according to the formula
Figure GDA00034691529200000450
Calculating to obtain;
wherein b isk，1Amount of data of task 1 in the kth task group, b, for a user Equipment UEk，2The data volume of task 2 in the kth task group of the user equipment UE, c is the number of CPU cycles needed by the mobile relay UAV to calculate 1-bit data, and f is the calculation capacity of the mobile relay UAV; v denotes the moving speed of the mobile relay UAV, B is the spectral bandwidth occupied by the MEC system,
Figure GDA00034691529200000451
to handle the wireless channel gain of the user terminal UE to the mobile relay UAV at the kth task set,
Figure GDA0003469152920000051
wireless channel gain for moving the relay UAV to the base station BS to process the kth task set;
Figure GDA0003469152920000052
represents the data transmission rate of the system from the UE to the UAV when processing the k-th task group
Figure GDA0003469152920000053
Indicating the data transmission rate of the UAV to the BS when the system is processing the kth task set.
The beneficial effects are that:
the mobile relay of the invention not only can provide light-weight operation service for the user equipment, but also can be used as a relay node between the user equipment and the base station to carry out data forwarding relay by utilizing the advantages of being mobile and capable of providing calculation power. According to the task computing power request of the user equipment, under three selectable task computing power scheduling schemes, the position of the mobile relay is optimized according to the size of the task data unloaded by the user equipment, and the best compromise between the distance of the mobile relay and the communication channel condition is searched, so that the aim of minimizing the task completion time of an application program in the user equipment is fulfilled. The method has the advantages of simple steps, good using effect and wide practicability.
Drawings
FIG. 1 is a flow chart of a mobile relay location control method according to the present invention;
FIG. 2 is a schematic diagram of the components of a UAV-supported MEC system of the present invention;
FIG. 3 is a schematic diagram of the sliding trajectory and hover position selection of a UAV with an application data size of 140 MB;
FIG. 4 is a schematic diagram of the sliding trajectory and hover position selection of a UAV with an application data size of 300 MB;
FIG. 5 is a schematic diagram comparing the time required for completing an application program with different data sizes by using the hovering position control method of an unmanned aerial vehicle proposed by the present invention compared with other methods;
Detailed Description
The following further describes embodiments of the present invention with reference to the drawings.
As shown in fig. 1, the mobile relay location control method of the mobile edge computing system of the present invention includes a user equipment UE, a mobile relay device UAV and a base station BS with a high performance edge computing server, where the mobile relay device UAV has a light computing capability and provides a light edge computing service for the user equipment UE, and the high performance edge computing server of the base station BS has a powerful computing capability, and the mobile relay device UAV and the base station BS are combined to solve the problem of insufficient computing resources of the user equipment UE;
the user equipment UE cannot be directly in wireless connection with the base station BS, the user equipment UE can be in wireless data connection with the base station BS only through the relay of the mobile relay equipment UAV, and the mobile relay equipment UAV provides data relay service for the user equipment UE and the base station BS and provides data calculation service within computing power for the user equipment UE; when User Equipment (UE) sends a calculation request, a current task is divided into a plurality of subtasks, the plurality of subtasks are sequentially sent to a mobile relay device (UAV) and calculation capacity support required by task calculation is judged in the mobile relay device (UAV), when the calculation capacity requirement of the subtasks is smaller than the calculation capacity of the mobile relay device (UAV), the corresponding subtasks are directly processed in the mobile relay device (UAV), the final calculation result is fed back to the User Equipment (UE), when the calculation capacity requirement of the subtasks exceeds the calculation capacity of the mobile relay device (UAV), the mobile relay device (UAV) sends the current subtasks to a server of a Base Station (BS) for processing, and when the processing is completed, the calculation results of the subtasks are sent to the User Equipment (UE) through the mobile relay device (UAV), so that three calculation task scheduling modes exist: calculating a task scheduling mode I: the former sub-task is executed at the mobile relay device UAV, and the latter sub-task is calculated at the base station BS; and a calculation task scheduling mode II: both the previous subtask and the next subtask perform calculations at the mobile relay device UAV; and calculating a task scheduling mode III: the former subtask and the latter subtask are both calculated in the base station BS; for two subtasks in any kth task group, respectively searching the position of the optimal wireless relay UAV of each subtask by using a basic PSO algorithm for the three calculation task scheduling modes, then selecting the task scheduling mode with the least time spent in the three calculation task scheduling modes as an execution scheme of the two subtasks, and repeatedly comparing the three calculation task scheduling modes for every two subtasks until all subtasks are completed.
Based on three calculation task scheduling modes, the mobile position of the mobile relay device UAV is optimized according to the size of the task data volume unloaded by the user equipment UE, the best compromise of the mobile distance and the communication channel condition of the mobile relay device UAV is found, and the completion of the calculation task of the user equipment UE is accelerated through the parallel execution of the mobile relay device UAV and the base station BS, so that the purpose of minimizing the completion time of all tasks of the UE is achieved, the system delay is reduced, and the user experience is improved;
specifically, the mobile relay UAV collects task data of the user equipment UE when performing a task, and relays the task data to the base station BS, and by controlling the position of the mobile relay UAV, the task completion time is minimized, and the closer the mobile relay UAV is to the user equipment UE and the base station BS, the better wireless channel conditions can be obtained, so that the data transmission time can be reduced, but the movement time of the mobile relay UAV can be increased, when the UAV is used as a relay, calculation is performed while relaying the data, or calculation is performed in the process of finding the optimal relay position in a moving manner, and the physical position of the relay is obtained by using a particle swarm algorithm.
When User Equipment (UE) executes an application program, the application program is divided into K task groups with priority orders, namely the current task group can not be executed continuously until the execution result of the direct previous task group returns; in K task groups, each task group comprises n independent subtasks, and in order to facilitate parallel calculation of the mobile relay UAV and the base station BS, the subtasks in each task group are divided into two parts according to the data size, wherein the first part is a task 1, and the second part is a task 2;
firstly, inputting coordinates of a user terminal UE and a base station BS, and setting initial position coordinates of a wireless relay UAV as
Figure GDA0003469152920000061
Before K task groups do not finish operation, the formula is used:
Figure GDA0003469152920000062
Figure GDA0003469152920000063
Figure GDA0003469152920000064
respectively calculating the time required by the three calculation task scheduling modes, wherein
Figure GDA0003469152920000065
Is determined according to the position coordinates u of the mobile relay UAV, the formula is determined, and the basic particle swarm algorithm is utilized to obtainObtaining the optimal relay position, further determining the required time, and then comparing the time with the time of other two schemes;
in the formula:
Figure GDA0003469152920000071
the time used for the task scheduling scheme I,
Figure GDA0003469152920000072
for the time used by the task scheduling scheme II,
Figure GDA0003469152920000073
the time used for the task scheduling scheme III,
Figure GDA0003469152920000074
for the time required for the mobile relay UAV to move from the initial position when processing the kth task set to the position when receiving task 1 data,
Figure GDA0003469152920000075
time required for the mobile relay UAV to move from the initial position when processing the kth task group to the position when receiving task 2 data;
Figure GDA0003469152920000076
the time required for the mobile relay UAV to receive the data for task 1 in the kth task group,
Figure GDA0003469152920000077
the time required for the mobile relay UAV to receive data for task 2 in the kth task group;
Figure GDA0003469152920000078
the time required for the mobile relay UAV to perform task 1 in the k-th task group,
Figure GDA0003469152920000079
the time required for the mobile relay UAV to perform task 2 in the kth task group;
Figure GDA00034691529200000710
the time required for the mobile relay UAV to move from a location receiving mission 1 data to relay this data location to the base station BS;
Figure GDA00034691529200000711
the time required for the mobile relay UAV to relay the received data for task 1 in the kth task group to the base station BS,
Figure GDA00034691529200000712
for the mobile relay UAV to relay the received data for task 2 in the kth task group to the base station BS,
Figure GDA00034691529200000713
express get
Figure GDA00034691529200000714
And
Figure GDA00034691529200000715
the larger of the two values is the one,
Figure GDA00034691529200000716
express get
Figure GDA00034691529200000717
And
Figure GDA00034691529200000718
the larger of the two values;
selecting
Figure GDA00034691529200000719
And
Figure GDA00034691529200000720
the minimum value among the three is obtained, and the data collection position of the mobile relay UAV corresponding to the minimum value among the three is obtained simultaneously
Figure GDA00034691529200000721
And relay location
Figure GDA00034691529200000722
u represents a position coordinate representation symbol of the mobile relay UAV, including actual coordinate information, and the most basic particle swarm algorithm is utilized to find the optimal u;
updating location parameters of the mobile relay UAV: if the task scheduling scheme I is used for the shortest time, then
Figure GDA00034691529200000723
If the task scheduling scheme III is used for the shortest time, then
Figure GDA00034691529200000724
Then order
Figure GDA00034691529200000725
Otherwise, the task scheduling scheme II uses the shortest order
Figure GDA00034691529200000726
That is to say: updating the initial position of the wireless relay UAV after each task group of the user terminal UE is executed, if so, updating the initial position of the wireless relay UAV
Figure GDA00034691529200000727
Or
Figure GDA00034691529200000728
At this time, the wireless relay UAV needs to relay at least one task to the base station BS for execution, so the initial position of the next task group (k +1 th task group) is the relay hovering position when relaying the data of task 2, that is, the wireless relay UAV is capable of relaying the data of task 2
Figure GDA00034691529200000729
If it is
Figure GDA00034691529200000730
It is explained that task 1 and task 2 of the kth task group are the shortest time required for the UAV to execute, so the initial position of the next task group is the data of task 2At the location of collection, i.e.
Figure GDA00034691529200000731
Figure GDA00034691529200000732
According to the formula
Figure GDA00034691529200000733
Calculated, where | · | | | represents the mobile relay UAV relay location
Figure GDA00034691529200000734
And
Figure GDA00034691529200000735
the euclidean distance between;
Figure GDA00034691529200000736
according to the formula
Figure GDA00034691529200000737
Calculating to obtain;
Figure GDA00034691529200000738
according to the formula
Figure GDA00034691529200000739
Is obtained by calculation, wherein (b)k，1And c) represents bk，1Performing a multiplication operation with c;
Figure GDA00034691529200000740
according to the formula
Figure GDA00034691529200000741
Calculating to obtain;
Figure GDA00034691529200000742
according to the formula
Figure GDA00034691529200000743
Calculating to obtain;
Figure GDA00034691529200000744
according to the formula
Figure GDA00034691529200000745
Calculating to obtain;
Figure GDA00034691529200000746
according to the formula
Figure GDA00034691529200000747
Calculating to obtain;
Figure GDA00034691529200000748
according to the formula
Figure GDA00034691529200000749
Is obtained by calculation, wherein (b)k，2And c) represents bk，2Performing a multiplication operation with c;
Figure GDA00034691529200000750
according to the formula
Figure GDA00034691529200000751
Calculating to obtain;
Figure GDA00034691529200000752
according to the formula
Figure GDA00034691529200000753
Calculating to obtain;
wherein b isk，1Amount of data of task 1 in the kth task group, b, for a user Equipment UEk，2The data volume of task 2 in the kth task group of the user equipment UE, c is the number of CPU cycles needed by the mobile relay UAV to calculate 1-bit data, and f is the calculation capacity of the mobile relay UAV; v denotes the moving speed of the mobile relay UAV, B is the spectral bandwidth occupied by the MEC system,
Figure GDA0003469152920000081
to handle the wireless channel gain of the user terminal UE to the mobile relay UAV at the kth task set,
Figure GDA0003469152920000082
wireless channel gain for moving the relay UAV to the base station BS to process the kth task set;
Figure GDA0003469152920000083
represents the data transmission rate of the system from the UE to the UAV when processing the k-th task group
Figure GDA0003469152920000084
Indicating the data transmission rate of the UAV to the BS when the system is processing the kth task set.
Example one
In the MEC system of the mobile edge computing system provided by the invention, user terminal UE is a mobile phone, mobile relay UAV is an unmanned aerial vehicle with the computing capability of relay function, and the application scene of the UAV hovering position control method is as follows: in order to solve the problems that the UE cannot meet the time delay requirement of a user due to insufficient computing resources and the MEC service cannot be carried out when a direct communication link does not exist between the UE and the BS, the UE and the BS are assisted by the UAV to realize the MEC service, and the UAV can not only provide the light-weight MEC service for the UE, but also be used as a relay node between the UE and the BS to carry out data forwarding. The hovering position of the Unmanned Aerial Vehicle (UAV) is optimized according to the size of the task data volume unloaded by the UE, the best compromise between the UAV flight distance and the communication channel condition is found, and the aim of minimizing the task completion time of an application program in the UE is achieved. The method is characterized in that: the scheme comprises the following operation steps:
as shown in FIG. 2, the system is composed of 1 base station BS, 1 user equipment UE and 1 UAV (unmanned aerial vehicle) UAV, wherein the BS is embedded with AMAX (advanced metering and tracking) high-performance edge computing server ServMaxTMPT-1 provides a mobile edge computing MEC service for UE; user Equipment (UE) is configured with 1 antenna for receiving and sending data; an Unmanned Aerial Vehicle (UAV) is used as a data relay node between User Equipment (UE) and a Base Station (BS) and is provided with 1 antenna for receiving and sending data; unmanned aerial vehicle UAV is still configured with quad-core ARM Cortex-A57 as a lightweight meterThe computing server can provide mobile edge computing MEC service for User Equipment (UE); the BS, UE, and UAV constitute all devices of the system.
The user equipment UE has one and only one application, which when executed is divided into K prioritized computing task groups. The priority order means that the current task group must wait until the execution result of the task group immediately before the current task group returns to continue execution. In the K task groups, each task group includes n independent subtasks. In order to support parallel calculation of an Unmanned Aerial Vehicle (UAV) and a Base Station (BS), n subtasks in any k-th task group are divided into two parts according to the size of data volume to be represented, the first part is represented as a task 1, the second part is represented as a task 2, and the ratio of the size of the data volume of the task 1 to the size of the data volume of the task 2 is 1:3 (preset ratio); by bk，1(unit: bit) represents the amount of data to complete task 1 in the kth task group of the user equipment UE, denoted by bk，2(unit: bit) represents the amount of data to complete task 2 in the kth task group of the user equipment UE; c represents the number of CPU cycles required by the UAV (unmanned aerial vehicle) to calculate 1-bit data, and f (unit: Hz) represents the calculation capacity of the UAV carrying a lightweight calculation server; the flight speed of the unmanned aerial vehicle UAV is denoted by v.
The spectral bandwidth occupied by the MEC system is expressed by B (unit: Hz)
Figure GDA0003469152920000085
Represents the wireless channel gain of the system from UE to UAV when processing the k-th task group
Figure GDA0003469152920000086
Represents the wireless channel gain of the UAV to the BS when the system processes the k-th task group; by using
Figure GDA0003469152920000087
Represents the data transmission rate of the system from the UE to the UAV when processing the k-th task group
Figure GDA0003469152920000088
Indicating the data transmission rate of the UAV to the BS when the system is processing the kth task set.
By uIRepresenting the initial hover position of the UAV after system initialization, set coordinates as uI(500, 0, 40), (this coordinate means that the drone initially hovers at an x-axis coordinate of 500m, a y-axis coordinate of 0m, and a height coordinate of 40 m). The coordinates of the UE location are (0, 0, 0), the coordinates of the BS location are (1000,0, 0), the x-axis in fig. 2 is taken as the flight orbit of the UAV, and the orbit length is 1000 m. By using
Figure GDA0003469152920000091
Representing the initial hover position when the UAV is ready to process the kth task group, note: for each task group, the unmanned aerial vehicle UAV processes task 1 and then task 2. By using
Figure GDA0003469152920000092
Indicating the hover position when the UE transmits data for task 1 in the kth task group to the UAV
Figure GDA0003469152920000093
Indicating the hover position when the UE transmits data for task 2 in the kth task group to the UAV
Figure GDA0003469152920000094
Indicating the hover position when UAV relays data for task 1 in the kth task group to BS
Figure GDA0003469152920000095
Indicating the hover position when the UAV relays data for task 2 in the kth task group to the BS.
By using
Figure GDA0003469152920000096
Representing the time required for the UAV to fly from an initial position while processing the kth task group to a hover position while receiving task 1 data, using
Figure GDA0003469152920000097
Indicating a UAVTime required to fly from the initial position to the time hover position receiving task 2 data while processing the kth task group; by using
Figure GDA0003469152920000098
Represents the time required for the UAV to receive data for task 1 in the kth task group
Figure GDA0003469152920000099
Represents the time required for the UAV to receive data for task 2 in the kth task group; by using
Figure GDA00034691529200000910
Represents the time required for UAV to perform task 1 in the kth task group
Figure GDA00034691529200000911
Represents the time required for the UAV to perform task 2 in the kth task group; by using
Figure GDA00034691529200000912
Represents the time required for the UAV to fly from a location receiving mission 1 data to relay this data location to the BS; by using
Figure GDA00034691529200000913
Represents the time required for UAV to relay the received data of task 1 in the k-th task group to BS
Figure GDA00034691529200000914
Represents the time required for the UAV to relay the received data for task 2 in the kth task group to the BS.
Task 1 and task 2 in any kth task group have three calculation task scheduling schemes. The first calculation task scheduling scheme I is as follows: task 1 performs calculations at the UAV and task 2 performs calculations at the BS, using
Figure GDA00034691529200000915
Indicating the time required to complete both tasks; the second calculation task scheduling scheme II is: both task 1 and task 2 are performed by the UAVLine calculation, this time with
Figure GDA00034691529200000916
Indicating the time required to complete both tasks; the third calculation task scheduling scheme III is: both task 1 and task 2 perform computations at the BS, using
Figure GDA00034691529200000917
Indicating the time required to complete both tasks; by TkRepresents the time required to complete the kth task group; t represents the total time for completing all K task groups in the application program;
for task 1 and task 2 in any kth task group, the three mentioned task scheduling schemes all use a basic PSO algorithm to find the respective optimal hovering position of the unmanned aerial vehicle UAV, and then the minimum value of the three is selected as the final selection scheme until each task group determines the calculation task scheduling scheme and the optimal hovering position of the UAV. For convenience of calculation, the position coordinates of the mobile relay UAV only consider x-axis coordinates, and y-axis and z-axis are fixed values, y is 0, and z is 40.
The UAV assists the UE and the BS to realize the calculation task scheduling of the MEC and the optimization method of the UAV hovering position as follows:
1 initialization parameter T: t is 0; first the coordinates of the user terminal UE and the base station BS are entered,
2, initializing parameter k: k is equal to 1, and k is equal to 1,
Figure GDA00034691529200000918
(meaning the initial position of the first task group is equal to the initial position of the drone system);
3, when K is less than or equal to K, executing the step 3.1 to the step 3.6;
using the formula:
Figure GDA0003469152920000101
Figure GDA0003469152920000102
Figure GDA0003469152920000103
respectively calculating the time required by the three data processing scheduling modes, wherein
Figure GDA0003469152920000104
Express get
Figure GDA0003469152920000105
And
Figure GDA0003469152920000106
the larger of the two values is the one,
Figure GDA0003469152920000107
express get
Figure GDA0003469152920000108
And
Figure GDA0003469152920000109
the larger of the two values;
: data processing is carried out on an independent mobile relay device UAV, data processing is carried out on a high-performance edge computing server connected with an independent base station BS, and data processing is carried out on a mobile relay device UAV and a high-performance edge computing service connected with the base station BS at the same time; according to the formula
Figure GDA00034691529200001010
Figure GDA00034691529200001011
Figure GDA00034691529200001012
Operating PSO algorithm, and obtaining by calculation
Figure GDA00034691529200001013
And
Figure GDA00034691529200001014
a value of (1), wherein
Figure GDA00034691529200001015
Express get
Figure GDA00034691529200001016
And
Figure GDA00034691529200001017
the larger of the two values is the one,
Figure GDA00034691529200001018
express get
Figure GDA00034691529200001019
And
Figure GDA00034691529200001020
the larger of the two values;
order to
Figure GDA00034691529200001021
Wherein
Figure GDA00034691529200001022
Express get
Figure GDA00034691529200001023
And
Figure GDA00034691529200001024
the minimum value among the three is obtained, and the UAV data collection hovering position corresponding to the minimum value among the three is obtained simultaneously
Figure GDA00034691529200001025
And data relay location
Figure GDA00034691529200001026
Updating the hover position parameter for the UAV as follows:
if it is not
Figure GDA00034691529200001027
Or
Figure GDA00034691529200001028
Then order
Figure GDA00034691529200001029
Otherwise, it orders
Figure GDA00034691529200001030
Let T be T + Tk；
Let k be k + 1;
returning to the step 3;
4, if K is equal to K +1, the algorithm has executed all task groups of the UE, and outputs T;
otherwise, if K is less than or equal to K, returning to the step 3 for execution;
in the above-mentioned step 3.1,
Figure GDA00034691529200001031
according to the formula
Figure GDA00034691529200001032
Is obtained by calculation, wherein | | · | | represents the hovering position
Figure GDA00034691529200001033
And
Figure GDA00034691529200001034
the euclidean distance between;
Figure GDA00034691529200001035
according to the formula
Figure GDA00034691529200001036
Calculating to obtain;
Figure GDA00034691529200001037
according to the formula
Figure GDA00034691529200001038
Is obtained by calculation, wherein (b)k，1And c) represents bk，1Performing a multiplication operation with c;
Figure GDA00034691529200001039
according to the formula
Figure GDA00034691529200001040
Calculating to obtain;
Figure GDA00034691529200001041
according to the formula
Figure GDA00034691529200001042
Calculating to obtain;
Figure GDA00034691529200001043
according to the formula
Figure GDA00034691529200001044
Calculating to obtain;
Figure GDA00034691529200001045
according to the formula
Figure GDA0003469152920000111
Calculating to obtain;
Figure GDA0003469152920000112
according to the formula
Figure GDA0003469152920000113
Is obtained by calculation, wherein (b)k，2And c) represents bk，2Performing a multiplication operation with c;
Figure GDA0003469152920000114
according to the formula
Figure GDA0003469152920000115
Calculating to obtain;
Figure GDA0003469152920000116
according to the formula
Figure GDA0003469152920000117
Calculating to obtain;
in step 3.3, the initial position of the UAV needs to be updated after each UE has executed one task group, and if the UE has executed one task group, the initial position of the UAV needs to be updated
Figure GDA0003469152920000118
Or
Figure GDA0003469152920000119
At this time, the UAV needs to relay at least one task to the BS for execution, so the initial position of the next task group is the hovering position when relaying the data of task 2, that is
Figure GDA00034691529200001110
If it is
Figure GDA00034691529200001111
It is stated that task 1 and task 2 of the kth task group are both performed by the UAV in the shortest amount of time, so the initial position of the next task group is the data collection position of task 2, i.e., the position
Figure GDA00034691529200001112
Multiple simulation experiments were performed, and specific examples and performance analyses thereof are described below. Referring to the MEC system supported by UAV shown in fig. 2, the following parameters are set: the frequency spectrum bandwidth B is 10MHz, the UAV flight speed v is 30m/s, the computation capability f of the UAV carrying a lightweight computing server is 2GHz, and the CPU periodicity c required by the UAV for computing each bit of data is 300cycles/bit.
Referring to fig. 3, a schematic diagram of the sliding trajectory and hovering position selection of the UAV when the data size of the application is 140MB is shown, and how the UAV assists the UE in executing the application is intuitively explained.
The solid line in fig. 3 represents the sliding trajectory and hover position coordinates along the x-axis of the UAV performing tasks 1 and 2 in the same task group, the dashed line represents the sliding trajectory of the UAV between two consecutive task groups, and the arrow direction represents the flight direction of the UAV. As can be seen from fig. 3, scheduling scheme I is selected for all task groups except scheduling scheme II for task group 1.
Referring to fig. 4, in contrast to fig. 3, a schematic diagram of the sliding trajectory and the hovering position selection of the UAV when the data size of the application is 300MB is shown, illustrating the effect of different data sizes of the application on the sliding trajectory and the hovering position selection of the UAV.
As can be seen from fig. 4, task group 1 and task group 3 select scheduling scheme I, task group 2 selects scheduling scheme II, and task group 4 selects scheduling scheme III. As can be seen by comparison with fig. 3, when the amount of application data is larger, the task is more likely to be performed using the selection scheduling scheme III, and the data collection hover position of the UAV is closer to the UE, the data relay position is closer to the BS. It can thus be concluded that: by using the unmanned aerial vehicle hovering position control method provided by the invention, compromise between UAV flight distance and communication channel conditions can be always found, and the optimal UAV hovering position in each task group is determined.
Referring to fig. 5, a schematic diagram of the total time required for completing all tasks by using the unmanned aerial vehicle hovering position control method provided by the present invention along with the continuous increase of the data volume of the UE application program task in the system is described.
As can be seen from fig. 5: with the increasing of the data volume of the UE application program task in the system, the total time required for completing all tasks is increased continuously, but the increasing trend is gradually gentle. In order to show the superiority of the unmanned aerial vehicle hovering position control method provided by the invention, compared with other three unmanned aerial vehicle hovering position control methods, the invention can be seen that the task completion time required by the method for controlling the hovering position of the unmanned aerial vehicle is less than that of other three schemes for any task data size.

Claims (2)

1. A mobile relay position control method of a mobile edge computing system, characterized by: the mobile edge computing system comprises User Equipment (UE), a mobile relay device (UAV) and a Base Station (BS) with a high-performance edge computing server, wherein the mobile relay device (UAV) has light computing capability and provides light-weight edge computing service for the User Equipment (UE), the high-performance edge computing server of the BS has strong computing capability, and the mobile relay device (UAV) and the BS are combined to solve the problem of insufficient computing resources of the User Equipment (UE);
the user equipment UE cannot be directly in wireless connection with the base station BS, the user equipment UE can be in wireless data connection with the base station BS only through the relay of the mobile relay equipment UAV, and the mobile relay equipment UAV provides data relay service for the user equipment UE and the base station BS and provides data calculation service within computing power for the user equipment UE; when User Equipment (UE) sends a calculation request, a current task is divided into a plurality of subtasks, the plurality of subtasks are sequentially sent to a mobile relay device (UAV) and calculation capacity support required by task calculation is judged in the mobile relay device (UAV), when the calculation capacity requirement of the subtasks is smaller than the calculation capacity of the mobile relay device (UAV), the corresponding subtasks are directly processed in the mobile relay device (UAV), the final calculation result is fed back to the User Equipment (UE), when the calculation capacity requirement of the subtasks exceeds the calculation capacity of the mobile relay device (UAV), the mobile relay device (UAV) sends the current subtasks to a server of a Base Station (BS) for processing, and when the processing is finished, the calculation results of the subtasks are sent to the User Equipment (UE) through the mobile relay device (UAV), so that three calculation task scheduling modes exist: calculating a task scheduling mode I: the former sub-task is executed at the mobile relay device UAV, and the latter sub-task is calculated at the base station BS; and a calculation task scheduling mode II: both the previous subtask and the next subtask perform calculations at the mobile relay device UAV; and calculating a task scheduling mode III: the former subtask and the latter subtask are both calculated in the base station BS; for two subtasks in any kth task group, respectively searching the positions of respective optimal wireless relay UAVs by using a basic PSO algorithm for the three calculation task scheduling modes, then selecting the task scheduling mode with the least time spent in the three calculation task scheduling modes as an execution scheme of the two subtasks, and repeatedly comparing the three calculation task scheduling modes for every two subtasks until all subtasks are completed;
based on three calculation task scheduling modes, the mobile position of the mobile relay device UAV is optimized according to the size of the task data volume unloaded by the user equipment UE, the best compromise of the mobile distance and the communication channel condition of the mobile relay device UAV is found, and the completion of the calculation task of the user equipment UE is accelerated through the parallel execution of the mobile relay device UAV and the base station BS, so that the purpose of minimizing the completion time of all tasks of the UE is achieved, the system delay is reduced, and the user experience is improved;
when User Equipment (UE) executes an application program, the application program is divided into K task groups with priority orders, namely the current task group can not be executed continuously until the execution result of the direct previous task group returns; in K task groups, each task group comprises n independent subtasks, and in order to facilitate parallel calculation of the mobile relay device UAV and the base station BS, the subtasks in each task group are divided into two parts according to the data size, wherein the first part is a task 1, and the second part is a task 2;
firstly, inputting coordinates of a user terminal UE and a base station BS, and setting initial position coordinates of a wireless relay UAV as
Figure FDA0003462647940000021
Before K task groups do not finish operation, the formula is used:
Figure FDA0003462647940000022
Figure FDA0003462647940000023
respectively calculating the time required by the three calculation task scheduling modes, wherein
Figure FDA0003462647940000024
Figure FDA0003462647940000025
The method is characterized in that the method is determined according to position coordinates u of a mobile relay device UAV, a formula is determined, the optimal relay position is obtained by utilizing a basic particle swarm algorithm, the required time is further determined, and then the time is compared with the time of other two schemes;
in the formula:
Figure FDA0003462647940000026
the time used for the task scheduling scheme I,
Figure FDA0003462647940000027
for the time used by the task scheduling scheme II,
Figure FDA0003462647940000028
the time used for the task scheduling scheme III,
Figure FDA0003462647940000029
for the time required for the mobile relay device UAV to move from the initial position when processing the kth task set to the position when receiving task 1 data,
Figure FDA00034626479400000210
time required for the mobile relay device UAV to move from the initial position when processing the kth task group to the position when receiving task 2 data;
Figure FDA00034626479400000211
the time required for the mobile relay device UAV to receive the data for task 1 in the k-th task group,
Figure FDA00034626479400000212
the time required for the mobile relay device UAV to receive data for task 2 in the kth task group;
Figure FDA00034626479400000213
the time required for the mobile relay device UAV to perform task 1 in the k-th task group,
Figure FDA00034626479400000214
the time required for the mobile relay device UAV to perform task 2 in the kth task group;
Figure FDA00034626479400000215
the time required for the mobile relay device UAV to move from a location where task 1 data is received to relay this data location to the base station BS;
Figure FDA00034626479400000216
for the time required for the mobile relay device UAV to relay the received data for task 1 in the kth task group to the base station BS,
Figure FDA00034626479400000217
for the time required for the mobile relay device UAV to relay the received data for task 2 in the kth task group to the base station BS,
Figure FDA00034626479400000218
express get
Figure FDA00034626479400000219
And
Figure FDA00034626479400000220
the larger of the two values is the one,
Figure FDA0003462647940000031
express get
Figure FDA0003462647940000032
And
Figure FDA0003462647940000033
the numerical value of the two is largerOne of (a); selecting
Figure FDA0003462647940000034
And
Figure FDA0003462647940000035
the minimum value among the three is obtained, and the data collection position of the mobile relay device UAV corresponding to the minimum value among the three is obtained simultaneously
Figure FDA0003462647940000036
And relay location
Figure FDA0003462647940000037
u represents a position coordinate representation symbol of the mobile relay device UAV, including actual coordinate information, and the optimal u is searched by using the most basic particle swarm algorithm;
updating location parameters of the mobile relay device UAV: if the task scheduling scheme I is used for the shortest time, then
Figure FDA0003462647940000038
If the task scheduling scheme III is used for the shortest time, then
Figure FDA0003462647940000039
Then order
Figure FDA00034626479400000310
Otherwise, the time for the task scheduling scheme II is shortest, so
Figure FDA00034626479400000311
That is to say: updating the initial position of the wireless relay UAV after each task group of the user terminal UE is executed, if so, updating the initial position of the wireless relay UAV
Figure FDA00034626479400000312
Or
Figure FDA00034626479400000313
At this time, the wireless relay UAV needs to relay at least one task to the base station BS for execution, so the initial position of the K +1 th task group is the relay hovering position when relaying the data of task 2, that is, the wireless relay UAV is configured to relay the data of task 2
Figure FDA00034626479400000314
If it is
Figure FDA00034626479400000315
It is stated that task 1 and task 2 of the kth task group are both performed by the UAV in the shortest amount of time, so the initial position of the next task group is the data collection position of task 2, i.e., the position
Figure FDA00034626479400000316
2. The mobile relay location control method of a mobile edge computing system of claim 1, wherein:
Figure FDA00034626479400000317
according to the formula
Figure FDA00034626479400000318
Calculating to obtain, wherein | · | | | represents a mobile relay, and i ═ 1 or 2; device UAV relay location
Figure FDA00034626479400000319
And
Figure FDA00034626479400000320
the euclidean distance between;
Figure FDA00034626479400000321
according to the formula
Figure FDA00034626479400000322
Calculating to obtain;
Figure FDA00034626479400000323
according to the formula
Figure FDA00034626479400000324
Is obtained by calculation, wherein (b)k，1And c) represents bk，1Performing a multiplication operation with c;
Figure FDA00034626479400000325
according to the formula
Figure FDA00034626479400000326
Calculating to obtain;
Figure FDA00034626479400000327
according to the formula
Figure FDA00034626479400000328
Calculating to obtain;
Figure FDA00034626479400000329
according to the formula
Figure FDA00034626479400000330
Calculating to obtain;
Figure FDA00034626479400000331
according to the formula
Figure FDA00034626479400000332
Calculating to obtain;
Figure FDA00034626479400000333
according to the formula
Figure FDA00034626479400000334
Is obtained by calculation, wherein (b)k，2And c) represents bk，2Performing a multiplication operation with c;
Figure FDA00034626479400000335
according to the formula
Figure FDA00034626479400000336
Calculating to obtain;
Figure FDA00034626479400000337
according to the formula
Figure FDA00034626479400000338
Calculating to obtain;
wherein b isk，1Amount of data of task 1 in the kth task group, b, for a user Equipment UEk，2The data volume of task 2 in the kth task group of the user equipment UE, C is the number of CPU cycles needed by the mobile relay device UAV to calculate 1-bit data, and f is the calculation capacity of the mobile relay device UAV; the moving speed of the mobile relay device UAV is denoted by v.
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