CN114666803A - Deployment and control method and system of mobile edge computing system - Google Patents

Abstract

The present application discloses a deployment and control method of a mobile edge computing system and a system thereof, wherein a deployment and control method of a mobile edge computing system specifically includes the following steps: initializing state information; obtaining the best signal detection result of the user; Obtain the best transmit beam; obtain the best reflection phase; obtain the best UAV power allocation and computing resource allocation results; obtain and output the best UAV flight trajectory; judge whether it converges to the preset accuracy or the number of iterations reaches the maximum iteration times; if it converges to the preset accuracy or the number of iterations reaches the maximum number of iterations, the best result is output. A method for joint design of a drone and an intelligent reflective surface in a mobile edge computing system proposed in this application can achieve the purpose of jointly designing an unmanned aerial vehicle and an intelligent reflective surface in a mobile edge computing system.

Description

Deployment, control method and system of a mobile edge computing system

technical field

The present application relates to the field of mobile communications, and in particular, to a deployment and control method and system of a mobile edge computing system.

Background technique

At present, in large-scale IoT scenarios for major businesses such as smart cities, smart transportation, smart agriculture, and environmental monitoring, it is necessary to collect ultra-large-scale sensor node data, which poses severe challenges to existing communication technologies. In addition, driven by a series of artificial intelligence technology applications such as virtual/augmented reality, face recognition, autonomous driving, and smart factories, future IoT terminal devices will generate computing-intensive business requirements. Due to the high computing power requirements, terminal devices cannot rely on their own limited computing power and energy to complete computing tasks locally. Therefore, to ensure that these tasks can be effectively calculated, mobile edge computing came into being. The mobile edge computing system assisted by drones can further stimulate the computing potential of the network. Using the maneuverability of drones to provide computing task offloading services for ground terminal equipment by carrying edge computing servers is a current research hotspot. Reconfigurable Intelligent Surface (IRS) is a passive planar reflector array whose surface is neatly arranged with many reconfigurable elements, each of which can perform independent phase shift and amplitude on the incident signal. control, thereby changing the transmission characteristics of the incident signal. According to the number of antennas at the transmitting end, the beamforming research of smart reflectors is divided into the following two categories: 1) Single passive beamforming is generally aimed at the SISO system, and both the transmitting end and the receiving end are equipped with only one antenna. When the propagating signal reaches the IRS, the IRS adjusts the amplitude and phase of the reflected signal through software programming, so that the reflected signal is structurally added to the signals of other paths, thereby enhancing the desired signal power at the receiving end. This process is the passive nature of the IRS. Beamforming; 2) Passive + active beamforming is suitable for multi-antenna systems, that is, the transmitting end is equipped with an antenna array composed of multiple antennas. Before the signal is transmitted, the signal can be pre-coded at the transmitter to form a directional beam. This process is also called active beamforming. Through this combination of active and passive beamforming, the transmission signal can be significantly enhanced, and the communication quality can be improved.

In the current UAV edge computing system, although the UAV can establish a line-of-sight transmission path with a high probability of communicating with the ground terminal, in the urban environment with densely distributed obstacles or the field environment with frequent altitude changes, the UAV and the ground The signal fading between them is severe, and the signal transmission faces the risk of strong blocking. At this time, the wireless coverage of the drone is unstable, and there may even be coverage blind spots. In this regard, although the traditional large-scale multi-antenna technology can enhance the signal strength to resist the influence of channel fading, it additionally increases the signal processing complexity of the transceiver and the communication energy consumption of the UAV, which is not suitable for battery-powered devices in specific application scenarios. For IoT terminals and energy-constrained drones, the scalability and application range of the system are affected.

Therefore, how to provide a method for deploying and controlling a mobile edge computing system that improves the performance of the edge computing system is an urgent problem for those skilled in the art to solve.

SUMMARY OF THE INVENTION

The present application provides a method for deploying and controlling a mobile edge computing system, which specifically includes the following steps: initializing state information; acquiring the user's best signal detection result in response to the initializing state information; in response to acquiring the user's best signal detection result , obtain the best transmit beam; in response to obtaining the best transmit beam, obtain the best reflection phase; in response to obtaining the best transmit beam, obtain the best UAV power allocation and computing resource allocation results; in response to obtaining the best unmanned aerial vehicle Obtain and output the best UAV flight trajectory based on the results of power allocation and computing resource allocation; judge whether to converge to the preset accuracy or the number of iterations reaches the maximum number of iterations; if it converges to the preset accuracy or the number of iterations reaches the maximum number of iterations, output best result.

As above, the initialization status information includes the number of initialized ground user equipment, the number of smart reflector reflection units, the number of drone antennas, the total system time slots, the length of each time slot, the trajectory representation of the drone, and the maximum transmit power of the user. , UAV maximum transmit power, UAV and ground wireless access point maximum computing frequency, system communication bandwidth and white noise power, as well as convergence accuracy and maximum number of iterations.

具体表示为：As above, wherein the determined signal detection result

Specifically expressed as:

h_ur[n]分别表示智能反射面与无线接入点之间、无人机与无线接入点之间、无人机与智能反射面之间的信道系数，σ²表示白噪声功率。where _IL represents an L×L-dimensional matrix,

h _ur [n] represents the channel coefficients between the smart reflective surface and the wireless access point, between the drone and the wireless access point, and between the drone and the smart reflective surface, respectively, and σ ² represents the white noise power.

As above, where the optimal transmit beam w _k [n] is specifically expressed as:

h_ur[n]分别表示智能反射面与无线接入点之间、无人机与无线接入点之间、无人机与智能反射面之间的信道系数，

表示服务用户k的第n时隙中智能反射面的相移矩阵，

表示第在服务用户k的第n时隙，对智能反射面第m个智能单元的相位θ_k,m[n]进行控制。

h _ur [n] represents the channel coefficients between the smart reflector and the wireless access point, between the drone and the wireless access point, and between the drone and the smart reflector, respectively,

represents the phase shift matrix of the smart reflector in the nth time slot serving user k,

Represents the nth time slot of the serving user k, and controls the phase θ _k,m [n] of the mth smart unit on the smart reflective surface.

As above, where the optimal reflection phase shift θk _,m [n] is specifically expressed as:

表示

第m个元素，

表示h_ur[n]第m行向量，w_k[n]表示最佳发射波束。in

express

the mth element,

represents the m-th row vector of h _ur [n], and w _k [n] represents the best transmit beam.

As above, obtaining the optimal UAV power allocation and computing resource allocation results specifically includes the following sub-steps: obtaining first input information; obtaining optimal UAV power allocation and computing resource allocation results according to the first input information ; wherein the first input information includes initialization state information, the best signal detection result, the best transmit beam, and the best reflection phase.

As above, wherein, obtaining and outputting the optimal UAV flight trajectory specifically includes the following sub-steps: obtaining second input information; using the convex optimization toolbox to solve and output the optimal UAV flight trajectory according to the second input information ; wherein the second input information includes initialization state information, the best signal detection result, the best transmit beam, the best reflection phase, and the best UAV power allocation and computing resource allocation results.

As above, it also includes updating the computing capacity of the system and obtaining the best signal detection results, the best transmit beam, the best reflection phase, and the best UAV power allocation and computing resource allocation results.

As above, if the computing capacity of the system converges to the specified accuracy or if the number of iterations reaches the maximum number of iterations, the number of iterations is increased by 1, and the user's best signal detection result is obtained again.

A deployment and control system for a mobile edge computing system, specifically including an initialization unit, an optimal signal detection result acquisition unit, an optimal transmit beam acquisition unit, an optimal reflection phase acquisition unit, an optimal allocation acquisition unit, and an optimal trajectory acquisition unit unit, judging unit, and output unit; an initialization unit for initializing state information; an optimal signal detection result acquisition unit for acquiring the user's best signal detection results; an optimal transmit beam acquisition unit for acquiring optimal transmission Beam; the best reflection phase acquisition unit, used to obtain the best reflection phase; the best allocation acquisition unit, used to obtain the best UAV power allocation and computing resource allocation results; the best trajectory acquisition unit, used to acquire and output The best UAV flight trajectory; the judgment unit is used to judge whether to converge to the preset accuracy or the number of iterations reaches the maximum number of iterations; if the computing capacity of the system does not converge to the specified accuracy, or if the number of iterations does not reach the maximum number of iterations, then Re-acquire the user's best signal detection result; the output unit is used to output the best result if the computing capacity of the system converges to the specified accuracy, or if the number of iterations reaches the maximum number of iterations.

This application has the following beneficial effects:

A method for joint design of a drone and an intelligent reflective surface in a mobile edge computing system proposed in this application can achieve the purpose of jointly designing an unmanned aerial vehicle and an intelligent reflective surface in a mobile edge computing system.

Description of drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only These are some embodiments described in this application. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings.

1 is an internal structure diagram of a deployment and control system of a mobile edge computing system provided according to an embodiment of the present application;

FIG. 2 is a flowchart of a deployment and control method of a mobile edge computing system provided according to an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of this application.

The present invention provides a deployment, control method and system of a mobile edge computing system. By controlling the uplink signal detection vector, beamforming vector, reflection unit phase shift, and UAV transmit power in the system, and deploying the UAV Optimize the design to achieve the purpose of joint design of the UAV and the intelligent reflector, and realize the maximum computing capacity of the system.

无人机向无线接入点传输用户k数据的时间为

此外，用

表示第在服务用户k的第n时隙，对智能反射面第m个智能单元的相位θ_k,m[n]进行控制；用户发射功率为固定值p_t；无人机在每个时隙内对用户k的波束成形向量和发射功率为w_k[n]和p_k[n]；无人机每个时隙内的上行信号检测向量为T_k[n]；系统通信带宽和白噪声功率分别为B和σ²。Scenario assumption: There are K ground users in the target area, a ground wireless access point and a drone can provide MEC computing services at the same time. The ground user needs to transmit the computing task to the UAV. After the UAV receives the computing task, it decides to forward some tasks to the ground wireless receiving point for calculation according to the situation. Assume that the total time slots of the system are N, and the length of each time slot is δ _t ; the number of reflection units on the smart reflector is M; the flight trajectory of the nth time slot of the UAV is expressed as q[n]. The time for user k to transmit to the UAV in each time slot is

The time for the drone to transmit user k data to the wireless access point is

Furthermore, with

Represents the nth time slot serving user k, and controls the phase θ _k,m [n] of the mth intelligent unit on the smart reflector; the user transmit power is a fixed value p _t ; the UAV is in each time slot The beamforming vector and transmit power for user k are w _k [n] and p _k [n]; the uplink signal detection vector in each time slot of the UAV is T _k [n]; the system communication bandwidth and white noise The powers are B and σ ² , respectively.

Example 1

As shown in FIG. 1 , it is the deployment and control system of the mobile edge computing system provided by this application.

Based on the above scenario assumptions, the computing capacity C of the defined system is specifically expressed as:

表示的是地面用户k在时隙n时到无人机的卸载任务量。其中

具体表示为：in

Represents the amount of unloading tasks from ground user k to the UAV at time slot n. in

Specifically expressed as:

表示为每个时隙内用户k向无人机传输的时间，B表示系统通信带宽，p_t表示用户发射功率为固定值，

表示确定的信号检测结果。where _hu,k [n] represents the channel coefficient from user k to the UAV at time slot n, and (·) ^H represents the conjugate transpose of the matrix or vector.

It is expressed as the time when user k transmits to the UAV in each time slot, B represents the system communication bandwidth, p _t represents the user’s transmit power is a fixed value,

Indicates a definite signal detection result.

具体表示为：In addition, the drone transmits the data volume of user k to the wireless access point in time slot n

Specifically expressed as:

h_ur[n]分别表示智能反射面与无线接入点之间、无人机与无线接入点之间、无人机与智能反射面之间的信道系数,

表示服务用户k的第n时隙中智能反射面的相移矩阵，diag(x)表示将向量x变为方阵，其中对角元素分别对应向量x的每个元素，其它元素都为0。

表示无人机向无线接入点传输用户k数据的时间，σ²表示白噪声功率。in

h _ur [n] represents the channel coefficients between the smart reflector and the wireless access point, between the drone and the wireless access point, and between the drone and the smart reflector, respectively,

Represents the phase shift matrix of the smart reflector in the nth time slot serving user k, diag(x) represents the transformation of the vector x into a square matrix, where the diagonal elements correspond to each element of the vector x, and the other elements are 0.

is the time for the UAV to transmit user k data to the wireless access point, and σ ² represents the white noise power.

其中f_k[n]为无人机向用户k分配的计算资源，c_u表示每计算1bit数据所需要的CPU周期数，δ_t表示每个时隙长度。在系统运行的整个过程中，无人机向无线接入点传输的数据量不能超过其接收到的数据量，即需满足以下因果约束关系：The calculation amount of the UAV for user k in time slot n is:

where f _k [n] is the computing resource allocated by the drone to user k, _{cu represents the number of CPU cycles required to calculate 1 bit of data, and δ t represents} the length of each time slot. During the whole process of system operation, the amount of data transmitted by the drone to the wireless access point cannot exceed the amount of data it receives, that is, the following causal constraints must be satisfied:

表示的是地面用户k在时隙n时到无人机的卸载任务量，

表示无人机在时隙n+1向无线接入点传输用户k的数据量，

表示无人机在时隙n+1对用户k的计算量。in

represents the amount of unloading tasks from ground user k to the UAV at time slot n,

represents the amount of data that the drone transmits to the wireless access point for user k in time slot n+1,

Indicates the amount of computation performed by the drone on user k in time slot n+1.

The system of the present application specifically includes the following units: an initialization unit 110, an optimal signal detection result acquisition unit 120, an optimal transmit beam acquisition unit 130, an optimal reflection phase acquisition unit 140, an optimal allocation acquisition unit 150, and an optimal trajectory acquisition unit unit 160 , judging unit 170 , and output unit 180 .

The initialization unit 110 is used to initialize state information.

The best signal detection result obtaining unit 120 is connected to the initialization unit 110 for obtaining the best signal detection result of the user.

The optimum transmit beam obtaining unit 130 is connected to the optimum signal detection result obtaining unit 120 for obtaining the optimum transmit beam.

The optimal reflection phase acquisition unit 140 is connected to the optimal transmission beam acquisition unit 130 for acquiring the optimal reflection phase.

The optimal allocation obtaining unit 150 is connected to the optimal reflection phase obtaining unit 140, and is used for obtaining the optimal UAV power allocation and computing resource allocation results.

The optimal trajectory acquisition unit 160 is connected to the optimal distribution acquisition unit 150 for acquiring and outputting the optimal UAV flight trajectory.

The judging unit 170 is respectively connected with the optimal trajectory obtaining unit 160 and the optimal signal detection result obtaining unit 120, and is used for judging whether the convergence has reached the preset precision or the number of iterations has reached the maximum number of iterations. If the calculation capacity of the system does not converge to the specified accuracy, or if the number of iterations does not reach the maximum number of iterations, the optimal signal detection result obtaining unit 120 re-executes to obtain the user's best signal detection result.

The output unit 180 is connected with the judgment unit 170, and is used for outputting the best result if the calculation capacity of the system converges to the specified accuracy, or if the number of iterations reaches the maximum number of iterations.

Embodiment 2

As shown in FIG. 2, it is a deployment and control method of a mobile edge computing system provided by this application, which specifically includes the following steps:

Step S210: Initialize state information.

The initialization status information includes the number of initialized ground user equipment, the number of smart reflectors, the number of UAV antennas, the total system time slots, the length of each time slot, the UAV trajectory representation, the user's maximum transmit power, the UAV antenna Maximum transmit power, maximum computing frequency of drones and ground wireless access points, system communication bandwidth and white noise power, as well as convergence accuracy and maximum number of iterations.

Step S220: In response to the initialization state information, obtain the best signal detection result for the user.

Specifically, the uplink signal detection control is performed, and the user's best signal detection result is obtained at the UAV side, so that the receiving performance is the best.

具体表示为：Definite signal detection results

Specifically expressed as:

h_ur[n]分别表示智能反射面与无线接入点之间、无人机与无线接入点之间、无人机与智能反射面之间的信道系数，σ²表示白噪声功率。where _IL represents an L×L-dimensional matrix,

h _ur [n] represents the channel coefficients between the smart reflective surface and the wireless access point, between the drone and the wireless access point, and between the drone and the smart reflective surface, respectively, and σ ² represents the white noise power.

Step S230: In response to obtaining the user's best signal detection result, obtain the best transmit beam.

Among them, the UAV beamforming control is performed to obtain the best transmission beam.

The optimal transmit beam w _k [n] is specifically expressed as:

h_ur[n]分别表示智能反射面与无线接入点之间、无人机与无线接入点之间、无人机与智能反射面之间的信道系数，

表示服务用户k的第n时隙中智能反射面的相移矩阵，

表示第在服务用户k的第n时隙，对智能反射面第m个智能单元的相位θ_k,m[n]进行控制。

h _ur [n] represents the channel coefficients between the smart reflector and the wireless access point, between the drone and the wireless access point, and between the drone and the smart reflector, respectively,

represents the phase shift matrix of the smart reflector in the nth time slot serving user k,

Represents the nth time slot of the serving user k, and controls the phase θ _k,m [n] of the mth smart unit on the smart reflective surface.

Step S240: In response to obtaining the optimal transmit beam, obtain the optimal reflection phase.

Among them, the optimal reflection phase of the reflection unit of the agent is determined, and the optimal reflection phase shift θ _{k, m} [n] is specifically expressed as:

表示

第m个元素，

表示h_ur[n]第m行向量，w_k[n]表示最佳发射波束。in

express

the mth element,

represents the m-th row vector of h _ur [n], and w _k [n] represents the best transmit beam.

Step S250: In response to obtaining the optimal transmit beam, obtain the optimal UAV power allocation and computing resource allocation results.

The UAV power and computing control are carried out, and the optimal UAV power allocation value and computing resource allocation are obtained by the CCCP (concave-convex procedure) method.

Wherein step S250 specifically includes the following sub-steps:

Step S2501: Obtain first input information.

Specifically, the first input information includes the initialization state information in step S210, the best signal detection result in step S220, the best transmit beam in step S230, and the best reflection phase in step S240.

Step S2502: Obtain the optimal UAV power allocation and computing resource allocation results according to the first input information.

Specifically, using the CCCP method, the UAV power and computational optimization problem is transformed into a convex problem, and then the convex optimization toolbox is used to solve the optimal UAV power allocation and computational resource allocation results.

Step S260: In response to obtaining the optimal UAV power allocation and computing resource allocation results, obtain and output the optimal UAV flight trajectory.

The optimal UAV flight trajectory is obtained by the SCA (successive convex approximation, continuous convex approximation) method, and step S260 specifically includes the following sub-steps:

Step S2601: Obtain second input information.

The second input information includes the initialization state information in step S210, the best signal detection result in step S220, the acquisition of the best transmit beam in step S230, and the best reflection phase in step S240, and in step S250 The optimal UAV power allocation and computing resource allocation results.

Step S2602: According to the second input information, use the convex optimization toolbox to obtain the optimal flight trajectory of the UAV, and output the optimal flight trajectory.

Step S270: Determine whether the convergence has reached the preset precision or the number of iterations has reached the maximum number of iterations.

After each iteration is completed, the computing capacity of the system obtained last time and the best signal detection results, the best transmit beam, the best reflection phase, and the best UAV power allocation and computing resource allocation results are updated. .

If the computing capacity of the system converges to the specified accuracy, or if the number of iterations reaches the maximum number of iterations, the iteration is terminated, and step S280 is executed. Step S280: output the best result.

The best results represent the best signal detection results for the user, the best transmit beams, the best reflection phases, the best UAV power allocation and computing resource allocation results, and the best UAV flight trajectory.

The specified precision can be preset according to the actual situation.

If the computing capacity of the system converges to the specified accuracy or if the number of iterations reaches the maximum number of iterations, the number of iterations is incremented by 1, and steps S220-S260 are re-executed until the computing capacity of the system converges to the preset accuracy, or the number of iterations reaches the maximum number of iterations .

This application has the following beneficial effects:

A method for joint design of a drone and an intelligent reflective surface in a mobile edge computing system proposed in this application can achieve the purpose of jointly designing an unmanned aerial vehicle and an intelligent reflective surface in a mobile edge computing system. Specifically, on the one hand, the signal detection vector of the UAV can be designed to improve the strength of signal reception; the beamforming vector of the UAV can be designed to enhance the signal transmission capability of the unloading task; The flight trajectory is optimized to improve the coverage performance. On the other hand, through the phase shift design of the surface unit of the intelligent reflective surface, the transmission characteristics of the channel are improved, thereby improving the transmission performance of the signal. After the joint design of the UAV and the intelligent reflector, the computing capacity of the system can be significantly improved.

Although the examples referenced by the current application are described for purposes of explanation only and not limitation of the application, changes, additions and/or deletions to the embodiments may be made without departing from the scope of the application.

The above are only specific embodiments of the present application, but the protection scope of the present application is not limited to this. should be covered within the scope of protection of this application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims (10)

1. A deployment and control method of a mobile edge computing system is characterized by comprising the following steps:

initializing state information;

responding to the initialization state information, and acquiring an optimal signal detection result of a user;

responding to the signal detection result which is obtained by the user and is optimal, and obtaining an optimal transmitting beam;

acquiring an optimal reflection phase in response to acquiring the optimal transmit beam;

responding to the obtained optimal transmitting wave beam, obtaining optimal unmanned aerial vehicle power distribution and calculation resource distribution results;

responding to the obtained optimal unmanned aerial vehicle power distribution and calculation resource distribution results, and obtaining and outputting an optimal unmanned aerial vehicle flight track;

judging whether the precision is converged to a preset precision or the iteration frequency reaches the maximum iteration frequency; and if the precision is converged to the preset precision or the iteration frequency reaches the maximum iteration frequency, outputting the optimal result.

2. The method of deploying and controlling a mobile edge computing system according to claim 1, wherein initializing the state information comprises initializing a number of ground user equipments, a number of intelligent reflector units, a number of drone antennas, a total system time slot, a length of each time slot, a drone trajectory representation, a user maximum transmit power, a drone and ground wireless access point maximum computing frequency, a system communication bandwidth and a white noise power, and a convergence accuracy and a maximum number of iterations.

3. The method for deploying and controlling a mobile edge computing system according to claim 1, wherein the determined signal detection result

The concrete expression is as follows:

wherein I_LA matrix representing dimensions of L x L,

h_ur[n]respectively represents the channel coefficients between the intelligent reflecting surface and the wireless access point, between the unmanned aerial vehicle and the wireless access point and between the unmanned aerial vehicle and the intelligent reflecting surface, sigma²Representing white noiseAnd (4) power.

4. The method for deploying and controlling a mobile edge computing system according to claim 1, wherein the optimal transmit beam w_k[n]The concrete expression is as follows:

h_ur[n]respectively representing the channel coefficients between the intelligent reflecting surface and the wireless access point, between the unmanned aerial vehicle and the wireless access point and between the unmanned aerial vehicle and the intelligent reflecting surface,

a phase shift matrix representing the intelligent reflecting surface in the nth time slot of the serving user k,

indicating the phase theta of the nth slot of the k-th service user to the mth intelligent unit of the intelligent reflector_k,m[n]And (5) controlling.

5. The method for deploying and controlling a mobile edge computing system according to claim 1, wherein the optimal reflection phase shift θ_k，m[n]The concrete expression is as follows:

wherein

Represent

The m-th element of the first group,

represents h_ur[n]M-th row vector, w_k[n]Representing the best transmit beam.

6. The deployment and control method of the mobile edge computing system according to claim 1, wherein the obtaining of the optimal unmanned aerial vehicle power allocation and computing resource allocation result specifically comprises the following substeps:

acquiring first input information;

acquiring optimal unmanned aerial vehicle power distribution and calculation resource distribution results according to the first input information;

wherein the first input information includes initialization state information, an optimal signal detection result, an optimal transmission beam, and an optimal reflection phase.

7. The deployment and control method of the mobile edge computing system according to claim 1, wherein the obtaining and outputting the optimal unmanned aerial vehicle flight trajectory specifically comprises the following sub-steps:

acquiring second input information;

solving and outputting the optimal flight track of the unmanned aerial vehicle by using a convex optimization toolbox according to the second input information;

wherein the second input information includes initialization state information, optimal signal detection results, optimal transmit beams, optimal reflection phases, and optimal drone power allocation and computational resource allocation results.

8. The method of deployment and control of a mobile edge computing system of claim 1, further comprising updating the computing capacity of the system and the obtained optimal signal detection results, optimal transmit beams, optimal reflection phases, and optimal drone power allocation and computing resource allocation results.

9. The deployment and control method of mobile edge computing system according to claim 8, wherein if the computing capacity of the system converges to a specified accuracy or if the number of iterations reaches the maximum number of iterations, the number of iterations is increased by 1, and the user's best signal detection result is obtained again.

10. A deployment and control system of a mobile edge computing system is characterized by specifically comprising an initialization unit, an optimal signal detection result acquisition unit, an optimal transmission beam acquisition unit, an optimal reflection phase acquisition unit, an optimal distribution acquisition unit, an optimal track acquisition unit, a judgment unit and an output unit;

an initialization unit for initializing state information;

an optimal signal detection result acquisition unit for acquiring an optimal signal detection result of a user;

an optimal transmission beam obtaining unit for obtaining an optimal transmission beam;

an optimal reflection phase acquisition unit for acquiring an optimal reflection phase;

the optimal allocation obtaining unit is used for obtaining optimal unmanned aerial vehicle power allocation and calculation resource allocation results;

the optimal track acquisition unit is used for acquiring and outputting an optimal unmanned aerial vehicle flight track;

the judging unit is used for judging whether the precision is converged to a preset precision or the iteration frequency reaches the maximum iteration frequency; if the calculated capacity of the system is not converged to the specified precision, or if the iteration times do not reach the maximum iteration times, the optimal signal detection result of the user is obtained again;

and the output unit is used for outputting the optimal result if the calculated capacity of the system converges to the specified precision or if the iteration frequency reaches the maximum iteration frequency.

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