CN114666803B - Deployment and control method and system of mobile edge computing system - Google Patents
Deployment and control method and system of mobile edge computing system Download PDFInfo
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Abstract
The application discloses a deployment and control method of a mobile edge computing system and a system thereof, wherein the deployment and control method of the mobile edge computing system specifically comprises the following steps: initializing state information; acquiring an optimal signal detection result of a user; acquiring an optimal transmitting beam; obtaining an optimal reflection phase; obtaining optimal unmanned aerial vehicle power allocation and calculation resource allocation results; acquiring and outputting an optimal unmanned aerial vehicle flight track; judging whether convergence is achieved to preset precision or the iteration number reaches the maximum iteration number; and if the convergence is up to the preset precision or the iteration number reaches the maximum iteration number, outputting an optimal result. According to the unmanned aerial vehicle and intelligent reflecting surface joint design method in the mobile edge computing system, the purpose of joint design of the unmanned aerial vehicle and the intelligent reflecting surface in the mobile edge computing system can be achieved.
Description
Technical Field
The present application relates to the field of mobile communications, and in particular, to a method for deploying and controlling a mobile edge computing system and a system thereof.
Background
At present, in a large-scale internet of things scene oriented to main services such as smart city, smart transportation, smart agriculture, environment monitoring and the like, ultra-large-scale sensor node data needs to be collected, which provides a serious challenge for the existing communication technology. In addition, under the drive of a series of artificial intelligence technology applications such as virtual/augmented reality, face recognition, automatic driving, intelligent factories and the like, the terminal equipment of the internet of things in the future generates computation-intensive business demands, and the demands generally have the characteristics of low time delay and high calculation power requirements, and the terminal equipment cannot locally complete the computation task by means of limited calculation power and energy. Thus, it is ensured that these tasks can be efficiently calculated, moving edge calculation has resulted. The mobile edge computing system assisted by the unmanned aerial vehicle can excite the computing potential of the network, and the ground terminal equipment is provided with computing task unloading service by using the maneuvering performance of the unmanned aerial vehicle and in a mode of carrying an edge computing server, which is a current research hot spot. The smart reflective surface (Reconfigurable Intelligent Surface, IRS) is a passive planar reflective array having a plurality of reconfigurable elements (reconfigurable element) aligned on the surface, each element being capable of performing a separate phase shift and amplitude control on the incident signal, thereby altering the transmission characteristics of the incident signal. According to the number of antennas at a transmitting end, the beam forming research of the intelligent reflecting surface is divided into the following two types: 1) Single passive beamforming is generally aimed at SISO systems, where both the transmitting end and the receiving end are equipped with only one antenna. After the propagation signal reaches the IRS, the IRS adjusts the amplitude and the phase of the reflected signal in a software programming mode, so that the reflected signal is constructively added with signals of other paths, the power of the expected signal of a receiving end is enhanced, and the process is the passive beam forming of the IRS; 2) The passive and active beam forming is suitable for a multi-antenna system, namely, a transmitting end is provided with an antenna array consisting of a plurality of antennas. Before signal transmission, the signal may be precoded at the transmitting end to form a beam with directivity, a process also known as active beam forming. By combining the active wave beam forming and the passive wave beam forming, the transmission signal can be obviously enhanced, and the communication quality can be improved.
In the existing unmanned aerial vehicle edge computing system, although an unmanned aerial vehicle can establish a sight distance transmission path with a ground terminal in a large probability to communicate, for urban environments with dense obstacle distribution or outdoor environments with frequent altitude changes, signal fading between the unmanned aerial vehicle and the ground is serious, and signal transmission faces the risk of strong blockage. At this time, the wireless coverage of the unmanned aerial vehicle is unstable, and even coverage blind areas may occur. In this regard, although the influence of signal strength against channel fading can be enhanced by means of the conventional large-scale multi-antenna technology, the communication energy consumption of the transceiver end signal processing complexity and the unmanned aerial vehicle is additionally increased, which affects the expandability and the application range of the system for the internet of things terminal powered by the battery and the unmanned aerial vehicle with limited energy in a specific application scene.
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.
Disclosure of Invention
The application provides a deployment and control method of a mobile edge computing system, which specifically comprises the following steps: initializing state information; responding to the initialization state information, and acquiring an optimal signal detection result of a user; acquiring an optimal transmitting beam in response to acquiring an optimal signal detection result of a user; in response to acquiring the optimal transmit beam, acquiring an optimal reflection phase; acquiring optimal unmanned aerial vehicle power allocation and calculation resource allocation results in response to acquiring the optimal transmission beam; 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 convergence is achieved to preset precision or the iteration number reaches the maximum iteration number; and if the convergence is up to the preset precision or the iteration number reaches the maximum iteration number, outputting an optimal result.
As described above, the initialization status information includes, initializing the number of ground user devices, the number of intelligent reflection surface reflection units, the number of unmanned aerial vehicle antennas, the total time slot of the system, the length of each time slot, the unmanned aerial vehicle track representation, the maximum user transmission power, the maximum unmanned aerial vehicle transmission power, the maximum calculation frequency of unmanned aerial vehicle and ground wireless access points, the system communication bandwidth and white noise power, and the convergence accuracy and the maximum iteration number.
As above, wherein the determined signal detection resultThe concrete steps are as follows:
wherein I is L Representing a matrix in the L x L dimension,h ur [n]respectively representing 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, sigma 2 Representing white noise power.
As above, wherein the best transmit beam w k [n]The concrete steps are as follows:
h ur [n]respectively representing 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, +.>Phase shift matrix representing intelligent reflection plane in nth time slot of service subscriber k +.>Representing the nth time slot of the service user k, for the phase theta of the mth intelligent unit of the intelligent reflecting surface k,m [n]And controlling.
As above, wherein the optimum reflection phase shift θ k，m [n]The concrete steps are as follows:
wherein the method comprises the steps ofRepresentation->Mth element, < >>Represents h ur [n]M-th row vector, w k [n]Indicating the best transmit beam.
As above, the method for obtaining the optimal unmanned aerial vehicle power allocation and the computing resource allocation result specifically includes the following sub-steps: acquiring first input information; acquiring optimal unmanned aerial vehicle power allocation and calculation resource allocation results according to the first input information; wherein the first input information includes initialization state information, an optimal signal detection result, an optimal transmit beam, and an optimal reflection phase.
As above, the method for acquiring and outputting the optimal unmanned aerial vehicle flight trajectory specifically includes the following sub-steps: acquiring second input information; according to the second input information, solving and outputting an optimal flight trajectory of the unmanned aerial vehicle by utilizing a convex optimization tool box; wherein the second input information includes initialization state information, an optimal signal detection result, an optimal transmit beam, an optimal reflection phase, and a step optimal drone power allocation and computing resource allocation result.
As above, the method further comprises updating the calculation capacity of the system and the acquired optimal signal detection result, optimal transmitting beam, optimal reflection phase, and optimal unmanned aerial vehicle power allocation and calculation resource allocation result.
If the calculation capacity of the system converges to the specified precision or if the iteration number reaches the maximum iteration number, 1 is added to the iteration number, and the optimal signal detection result of the user is obtained again.
The deployment and control system of the mobile edge computing system specifically comprises an initialization unit, an optimal signal detection result acquisition unit, an optimal transmitting beam acquisition unit, an optimal reflection phase acquisition unit, an optimal allocation acquisition unit, an optimal track acquisition unit, a judgment unit and an output unit; an initializing unit for initializing state information; the optimal signal detection result acquisition unit is used for acquiring the optimal signal detection result of the user; an optimal transmit beam acquisition unit configured to acquire an optimal transmit beam; an optimal reflection phase acquisition unit configured to acquire an optimal reflection phase; the optimal allocation acquisition unit is used for acquiring 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 preset precision is converged or the iteration number reaches the maximum iteration number; if the calculation capacity of the system is not converged to the specified precision, or if the iteration number is not up to the maximum iteration number, the optimal signal detection result of the user is obtained again; and the output unit is used for outputting the optimal result if the calculation capacity of the system is converged to the specified precision or if the iteration number reaches the maximum iteration number.
The application has the following beneficial effects:
according to the unmanned aerial vehicle and intelligent reflecting surface joint design method in the mobile edge computing system, the purpose of joint design of the unmanned aerial vehicle and the intelligent reflecting surface in the mobile edge computing system can be achieved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description will briefly introduce the drawings that are required to be used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings may also be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is an internal block diagram of a deployment, control system for a mobile edge computing system provided in accordance with an embodiment of the present application;
fig. 2 is a flowchart of a deployment, control method of a mobile edge computing system provided according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application, taken in conjunction with the accompanying drawings, clearly and completely describes the technical solutions of the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
The invention provides a deployment and control method of a mobile edge computing system and a system thereof, wherein the deployment and control method of the mobile edge computing system is used for optimizing the design of the deployment of an unmanned aerial vehicle through controlling an uplink signal detection vector, a beam forming vector, a reflection unit phase shift and the unmanned aerial vehicle transmitting power in the system, so as to achieve the aim of joint design of the unmanned aerial vehicle and an intelligent reflection surface and realize the maximum computing capacity of the system.
Scene assumption: k ground users are arranged in the target area, and one ground wireless access point and one unmanned plane can simultaneously provide MEC computing service. The ground user needs to transmit the calculation task to the unmanned aerial vehicle, and the unmanned aerial vehicle receives the calculation task and then decides to forward part of the task to the ground wireless receiving point for calculation according to the situation. Assuming that the total time slot of the system is N, each time slot is delta in length t The method comprises the steps of carrying out a first treatment on the surface of the The number of the intelligent reflecting surface reflecting units is M; the flight trajectory of the nth slot of the unmanned aircraft is denoted q [ n ]]. The time for transmitting the user k to the unmanned plane in each time slot isThe time for the unmanned aerial vehicle to transmit user k data to the wireless access point is +.>In addition, use->Nth time slot representing the service-th user kFor the phase theta of the mth intelligent unit of the intelligent reflecting surface k,m [n]Performing control; the user transmitting power is a fixed value p t The method comprises the steps of carrying out a first treatment on the surface of the The beamforming vector and transmit power w for user k in each slot of the drone k [n]And p k [n]The method comprises the steps of carrying out a first treatment on the surface of the The uplink signal detection vector in each time slot of the unmanned aerial vehicle is T k [n]The method comprises the steps of carrying out a first treatment on the surface of the The system communication bandwidth and white noise power are B and sigma respectively 2 。
Example 1
As shown in fig. 1, the system is a deployment and control system of a mobile edge computing system provided by the application.
Based on the above scenario assumption, the computation capacity C of the definition system is specifically expressed as:
wherein the method comprises the steps ofThe amount of off-load tasks to the drone at time slot n for ground user k is shown. Wherein->The concrete steps are as follows:
wherein h is u,k [n]Representing the channel coefficient of user k to the drone at time slot n, (-) H Representing the conjugate transpose of the matrix or vector.Expressed as the time of transmission of user k to the drone in each time slot, B expressed as the system communication bandwidth, p t Indicating that the user transmit power is a fixed value,/-, for example>Representation determinationIs a result of signal detection.
Furthermore, the drone transmits the data quantity of user k to the wireless access point in time slot nThe concrete steps are as follows:
wherein the method comprises the steps ofh ur [n]Respectively representing 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, +.>Representing the phase shift matrix of the intelligent reflecting surface in the nth slot of service user k, diag (x) represents changing vector x into a square matrix, wherein diagonal elements respectively correspond to each element of vector x, and other elements are all 0./>Representing time, sigma, of transmission of user k data by a drone to a wireless access point 2 Representing white noise power.
The calculated amount of the unmanned plane to the user k in the time slot n is as followsWherein f k [n]Computing resources allocated to user k for drone, c u Representing the number of CPU cycles, delta, required per calculation of 1bit data t Representing the length of each slot. In the whole process of system operation, the data volume transmitted to the wireless access point by the unmanned aerial vehicle cannot exceed the data volume received by the unmanned aerial vehicle, namely the following causal constraint relation needs to be satisfied:
wherein the method comprises the steps ofThe task load of ground user k to the unmanned aerial vehicle during time slot n is shown, +.> Indicating the amount of data of user k transmitted by the drone to the wireless access point in time slot n+1,/->The calculated amount of the unmanned plane to the user k in the time slot n+1 is represented.
Wherein the system of the present application specifically comprises the following units: an initializing unit 110, an optimal signal detection result acquiring unit 120, an optimal transmit beam acquiring unit 130, an optimal reflection phase acquiring unit 140, an optimal allocation acquiring unit 150, an optimal trajectory acquiring unit 160, a judging unit 170, and an output unit 180.
The initialization unit 110 is used for initializing state information.
The optimal signal detection result obtaining unit 120 is connected to the initializing unit 110, and is configured to obtain a signal detection result optimal for a user.
The optimal transmit beam acquisition unit 130 is connected to the optimal signal detection result acquisition unit 120, and is configured to acquire an optimal transmit beam.
The optimal reflection phase acquisition unit 140 is connected to the optimal transmission beam acquisition unit 130 for acquiring an optimal reflection phase.
The optimal allocation acquiring unit 150 is connected to the optimal reflection phase acquiring unit 140, and is configured to acquire an optimal unmanned aerial vehicle power allocation and a calculation resource allocation result.
The optimal trajectory acquisition unit 160 is connected to the optimal allocation acquisition unit 150, and is configured to acquire and output an optimal unmanned aerial vehicle flight trajectory.
The judging unit 170 is connected to the optimal track acquiring unit 160 and the optimal signal detection result acquiring unit 120, respectively, and is configured to judge whether the preset accuracy is reached or whether the iteration number reaches the maximum iteration number. 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-performs obtaining the optimal signal detection result of the user.
The output unit 180 is connected to the judging unit 170, and is configured to output an optimal result if the calculation capacity of the system converges to the specified precision, or if the iteration number reaches the maximum iteration number.
Example two
As shown in fig. 2, the deployment and control method of the mobile edge computing system provided in the present application specifically includes the following steps:
step S210: initializing state information.
The initialization state information comprises the number of ground user equipment, the number of intelligent reflecting surface reflecting units, the number of unmanned aerial vehicle antennas, the total time slot of the system, the length of each time slot, the track representation of the unmanned aerial vehicle, the maximum transmitting power of a user, the maximum transmitting power of the unmanned aerial vehicle, the maximum calculating frequency of unmanned aerial vehicle and ground wireless access points, the communication bandwidth of the system and white noise power, and the convergence precision and the maximum iteration number.
Step S220: and responding to the initialization state information, and acquiring the optimal signal detection result of the user.
Specifically, uplink signal detection control is performed, and an optimal signal detection result of a user is obtained at the unmanned aerial vehicle end, so that the receiving performance is optimal.
Determined signal detection resultsThe concrete steps are as follows:
wherein I is L Representing a matrix in the L x L dimension,h ur [n]respectively representing 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, sigma 2 Representing white noise power.
Step S230: and acquiring the optimal transmitting beam in response to acquiring the optimal signal detection result of the user.
And performing unmanned aerial vehicle beam forming control to acquire an optimal transmitting beam.
Optimal transmit beam w k [n]The concrete steps are as follows:
h ur [n]respectively representing 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, +.>Phase shift matrix representing intelligent reflection plane in nth time slot of service subscriber k +.>Representing the nth time slot of the service user k, for the phase theta of the mth intelligent unit of the intelligent reflecting surface k,m [n]And controlling.
Step S240: in response to acquiring the optimal transmit beam, an optimal reflection phase is acquired.
Wherein an optimal reflection phase of the reflection unit of the agent is determined, and an optimal reflection phase shift θ k，m [n]The concrete steps are as follows:
wherein the method comprises the steps ofRepresentation->Mth element, < >>Represents h ur [n]M-th row vector, w k [n]Indicating the best transmit beam.
Step S250: and in response to acquiring the optimal transmitting beam, acquiring the optimal unmanned aerial vehicle power allocation and calculation resource allocation results.
And performing unmanned aerial vehicle power and calculation control, and obtaining an optimal unmanned aerial vehicle power distribution value and calculation resource distribution through a CCCP (control-control procedure) method.
Wherein step S250 specifically includes the following sub-steps:
step S2501: first input information is acquired.
Specifically, the first input information includes initialization state information in step S210, an optimal signal detection result in step S220, an optimal transmit beam in step S230, and an optimal reflection phase in step S240.
Step S2502: and acquiring optimal unmanned aerial vehicle power allocation and calculation resource allocation results according to the first input information.
Specifically, the CCCP method is utilized to change the unmanned aerial vehicle power and calculation optimization problem into a convex problem, and then the convex optimization tool box is solved to obtain the optimal unmanned aerial vehicle power distribution and calculation resource distribution result.
Step S260: and 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.
Wherein the optimal unmanned aerial vehicle flight trajectory is obtained by an SCA (successive convex approximation, successive approximation) method, and step S260 specifically includes the following sub-steps:
step S2601: second input information is acquired.
The second input information includes the initialization state information in step S210, the best signal detection result in step S220, the best transmit beam acquired in step S230, the best reflection phase in step S240, and the best unmanned power allocation and computing resource allocation result in step S250.
Step S2602: and according to the second input information, solving by using the convex optimization tool box to obtain the optimal flight path of the unmanned aerial vehicle, and outputting the optimal flight path.
Step S270: judging whether convergence is achieved to preset precision or the iteration number reaches the maximum iteration number.
After each iteration is completed, the calculation capacity of the system obtained last time and the obtained optimal signal detection result, the optimal transmitting beam, the optimal reflecting phase, and the optimal unmanned aerial vehicle power distribution and calculation resource distribution result are updated.
If the calculation capacity of the system converges to the specified precision, or if the iteration number reaches the maximum iteration number, the iteration is terminated, and step S280 is executed. Step S280: and outputting the best result.
The best results represent the best signal detection results of the user, the best transmit beam, the best reflection phase, the best unmanned aerial vehicle power allocation and calculation resource allocation results, and the best unmanned aerial vehicle flight trajectory.
Wherein the specified precision can be preset according to the actual situation.
If the calculation capacity of the system converges to the specified precision or if the iteration number reaches the maximum iteration number, adding 1 to the iteration number, and re-executing the steps S220-S260 again until the calculation capacity of the system converges to the preset precision or the iteration number reaches the maximum iteration number.
The application has the following beneficial effects:
according to the unmanned aerial vehicle and intelligent reflecting surface joint design method in the mobile edge computing system, the purpose of joint design of the unmanned aerial vehicle and the intelligent reflecting surface in the mobile edge computing system can be achieved. Specifically, on one hand, the signal detection vector of the unmanned aerial vehicle can be designed, so that the strength of signal reception is improved; designing a beam forming vector of the unmanned aerial vehicle, and enhancing the signal transmission capacity of an unloading task; meanwhile, the flight path of the unmanned aerial vehicle is optimized, and the coverage performance is improved. On the other hand, the transmission characteristic of the channel is improved by carrying out phase shift design on the surface unit of the intelligent reflecting surface, so that the transmission performance of the signal is improved. Through the joint design of the unmanned plane and the intelligent reflecting surface, the calculation capacity of the system can be obviously improved finally.
Although the examples referred to in the present application are described for illustrative purposes only and not as limitations on the present application, variations, additions and/or deletions to the embodiments may be made without departing from the scope of the application.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. The deployment and control method of the mobile edge computing system is characterized by comprising the following steps of:
initializing state information;
responding to the initialization state information, and acquiring an optimal signal detection result of a user;
acquiring an optimal transmitting beam in response to acquiring an optimal signal detection result of a user;
in response to acquiring the optimal transmit beam, acquiring an optimal reflection phase;
acquiring optimal unmanned aerial vehicle power allocation and calculation resource allocation results in response to acquiring the optimal transmission beam;
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 convergence is achieved to preset precision or the iteration number reaches the maximum iteration number; if convergence is carried out to the preset precision or the iteration number reaches the maximum iteration number, outputting an optimal result;
the method comprises the following steps of:
acquiring first input information;
acquiring optimal unmanned aerial vehicle power allocation and calculation resource allocation results according to the first input information;
wherein the first input information includes initialization state information, an optimal signal detection result, an optimal transmit beam, and an optimal reflection phase;
if the convergence is reached to the preset precision or the iteration number reaches the maximum iteration number, the signal detection result is optimizedThe concrete steps are as follows:
wherein I is L Representing an L×L-dimensional matrix, h u,k [n]Representing the channel coefficient of user k to the drone at time slot n, (-) H Representing the conjugate transpose, sigma, of a matrix or vector 2 Representing white noise power.
2. The deployment, control method of mobile edge computing system of claim 1, wherein the initialization state information includes, initializing ground user equipment number, intelligent reflection plane reflection unit number, unmanned aerial vehicle antenna number, system total time slot, each time slot length, unmanned aerial vehicle trajectory representation, user maximum transmit power, unmanned aerial vehicle and ground wireless access point maximum computation frequency, system communication bandwidth and white noise power, and convergence accuracy and maximum number of iterations.
3. The deployment, control method of a mobile edge computing system of claim 1, wherein an optimal launch is initiatedBeam w k [n]The concrete steps are as follows:
h ur [n]respectively representing 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, +.>Phase shift matrix representing intelligent reflection plane in nth time slot of service subscriber k +.>Representing the nth time slot of the service user k, for the phase theta of the mth intelligent unit of the intelligent reflecting surface k,m [n]And controlling.
4. The deployment, control method of a mobile edge computing system of claim 1, wherein an optimal reflected phase shift θ k，m [n]The concrete steps are as follows:
wherein the method comprises the steps ofRepresentation->Mth element, < >>Represents h ur [n]M-th row vector, w k [n]Representing the best transmit beam, +.>Representing the channel coefficient between the drone and the wireless access point,/->Representing the channel coefficient between the smart reflective surface and the wireless access point.
5. The deployment and control method of a mobile edge computing system according to claim 1, wherein the obtaining of the best unmanned power allocation and computing resource allocation results comprises the following sub-steps:
acquiring first input information;
acquiring optimal unmanned aerial vehicle power allocation and calculation resource allocation results according to the first input information;
wherein the first input information includes initialization state information, an optimal signal detection result, an optimal transmit beam, and an optimal reflection phase.
6. The deployment and control method of a mobile edge computing system according to claim 1, wherein the method comprises the following sub-steps of:
acquiring second input information;
according to the second input information, solving and outputting an optimal flight trajectory of the unmanned aerial vehicle by utilizing a convex optimization tool box;
wherein the second input information includes initialization state information, an optimal signal detection result, an optimal transmit beam, an optimal reflection phase, and a step optimal drone power allocation and computing resource allocation result.
7. The deployment, control method of a mobile edge computing system of claim 1, further comprising updating computing capacity of the system and best signal detection results obtained, best transmit beams, best reflected phases, and best drone power allocation and computing resource allocation results.
8. The method for deploying and controlling a mobile edge computing system according to claim 7, wherein if the computing capacity of the system converges to a specified accuracy or if the number of iterations reaches a maximum number of iterations, the number of iterations is increased by 1, and the optimal signal detection result of the user is obtained again.
9. The deployment and control system of the mobile edge computing system is characterized by comprising an initialization unit, an optimal signal detection result acquisition unit, an optimal emission beam acquisition unit, an optimal reflection phase acquisition unit, an optimal allocation acquisition unit, an optimal track acquisition unit, a judgment unit and an output unit;
an initializing unit for initializing state information;
the optimal signal detection result acquisition unit is used for acquiring the optimal signal detection result of the user;
an optimal transmit beam acquisition unit configured to acquire an optimal transmit beam;
an optimal reflection phase acquisition unit configured to acquire an optimal reflection phase;
the optimal allocation acquisition unit is used for acquiring 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 preset precision is converged or the iteration number reaches the maximum iteration number; if the calculation capacity of the system is not converged to the specified precision, or if the iteration number is not up to the maximum iteration number, the optimal signal detection result of the user is obtained again;
the output unit is used for outputting the optimal result if the calculation capacity of the system converges to the specified precision or if the iteration number reaches the maximum iteration number;
the optimal allocation obtaining unit obtains the optimal unmanned aerial vehicle power allocation and calculation resource allocation results, and specifically comprises the following substeps:
acquiring first input information;
acquiring optimal unmanned aerial vehicle power allocation and calculation resource allocation results according to the first input information;
wherein the first input information includes initialization state information, an optimal signal detection result, an optimal transmit beam, and an optimal reflection phase;
if the convergence is reached to the preset precision or the iteration number reaches the maximum iteration number, the signal detection result is optimizedThe concrete steps are as follows:
wherein I is L Representing an L×L-dimensional matrix, h u,k [n]Representing the channel coefficient of user k to the drone at time slot n, (-) H Representing the conjugate transpose, sigma, of a matrix or vector 2 Representing white noise power.
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