CN115277567B - Intelligent reflecting surface-assisted Internet of vehicles multi-MEC unloading method - Google Patents

Intelligent reflecting surface-assisted Internet of vehicles multi-MEC unloading method Download PDF

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CN115277567B
CN115277567B CN202210747007.4A CN202210747007A CN115277567B CN 115277567 B CN115277567 B CN 115277567B CN 202210747007 A CN202210747007 A CN 202210747007A CN 115277567 B CN115277567 B CN 115277567B
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user
unloading
rsu
irs
mec
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CN115277567A (en
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陈月云
邓韬玉
陈广
杜利平
韩双双
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University of Science and Technology Beijing USTB
Shunde Graduate School of USTB
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Shunde Graduate School of USTB
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/10—Flow control; Congestion control
    • H04L47/12—Avoiding congestion; Recovering from congestion
    • H04L47/125—Avoiding congestion; Recovering from congestion by balancing the load, e.g. traffic engineering
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0893—Assignment of logical groups to network elements
    • 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
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
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Abstract

The invention discloses an intelligent reflection surface-assisted internet of vehicles multi-MEC unloading method, which comprises the following steps: constructing a multi-mobile-edge computing MEC collaborative Internet of vehicles scene introducing an intelligent reflection surface IRS; based on a multi-MEC cooperative internet of vehicles scene, constructing a multi-MEC diversity unloading model, and modeling the internet of vehicles user performance, IRS state and unloading selection as an optimization problem; maximizing the total number of processing bits by jointly optimizing the local computing power, the user transmit power, the IRS phase, and the selection of the offload RSU; and calculating an optimal unloading scheme for the unloading model based on the decoupling idea. The technical scheme of the invention can solve the problems of poor unloading link and low unloading efficiency caused by frequent movement of the Internet of vehicles users to the cell edge with poor signal quality.

Description

Intelligent reflecting surface-assisted Internet of vehicles multi-MEC unloading method
Technical Field
The invention relates to the technical field of Internet of vehicles, in particular to an intelligent reflection surface-assisted Internet of vehicles multi-mobile-edge computing MEC unloading method.
Background
The internet of vehicles (IoV) users frequently experience a situation of moving to the cell edge due to their high mobility. The signal quality at the center and edge of a cell covered by a conventional cell is very different, which results in a fluctuating or even service interruption of the task offload traffic of Mobile Edge Computing (MEC) enabled internet of vehicles users. In order to solve this problem, the existing research mainly focuses on improving the signal quality of the cell edge by the physical layer technology, such as the 6G cell-free technology, where the 6G desires to achieve stable and seamless handover quality of service.
In a car networking task offloading scenario where multiple MECs are introduced, the current research focus on car networking mobility is mostly focused on trajectory prediction or load balancing to avoid performing handover tasks at cell edges or reducing MEC pressure. However, the traffic volume of the internet of vehicles is further increased, and various safety and entertainment services cover every minute/second of vehicle travel, and the task offloading is stopped when the vehicle moves to the cell edge, so that the service quality is frequently fluctuated. Therefore, while improving the signal quality, if a diversity offloading policy is designed according to the characteristics of communication and calculation resources at the cell edge, a roadside unit (RSU) with better channel state is selected for offloading, so that the spectrum resources can be more fully utilized, and the service quality is further improved.
The prior art has the following defects:
1) The prior art ignores the unloading service of the cell edge users, and cannot guarantee the stability and the continuity of the unloading service of the high-mobility internet of vehicles users.
2) In the prior art, the performance and reliability of task unloading are improved through Intelligent Reflection Surface (IRS) auxiliary unloading, but a novel multi-MEC cooperative unloading strategy is not provided aiming at the characteristics of the intelligent reflection surface, so that the IRS cannot exert the optimal performance in task unloading.
3) The prior art is fixed in the cell unloaded by the Internet of vehicles user, and the selection of roadside units (RSUs) and MECs for assisting in calculation is not concerned, so that the fluctuation of an unloading link along with the movement of the user is obvious.
Disclosure of Invention
The invention provides an intelligent reflection surface-assisted internet of vehicles multi-MEC unloading method, which aims to solve the technical problems that an unloading link is poor and unloading efficiency is reduced due to the fact that internet of vehicles users often move to the edge of a cell with poor signal quality.
In order to solve the technical problems, the invention provides the following technical scheme:
in one aspect, the invention provides an intelligent reflection-surface-assisted internet of vehicles multi-MEC unloading method, which comprises the following steps:
constructing a multi-mobile-edge computing MEC collaborative Internet of vehicles scene introducing an intelligent reflection surface IRS;
based on the multi-MEC cooperative internet of vehicles scene, constructing a multi-MEC diversity unloading model, and modeling the internet of vehicles user performance, IRS state and unloading selection as an optimization problem; maximizing the total number of processing bits by jointly optimizing the local computing power, the user transmit power, the IRS phase, and the selection of the offload RSU;
and calculating an optimal unloading scheme for the unloading model based on the decoupling idea.
Further, in a multi-MEC cooperative Internet of vehicles scene of introducing IRS, a plurality of roadside units RSU provided with MEC servers are arranged, and a plurality of intelligent vehicle users are randomly distributed in a certain range at the juncture of the coverage areas of the two RSUs; the user's tasks may be performed locally or offloaded to the RSU's MEC server; an intelligent reflection plane is arranged at the midpoint of the connecting line of the two RSUs; the user's task offloading phase employs time division multiple access.
Further, the roadside unit RSU includes a first RSU and a second RSU.
Further, the problem of maximizing the total number of processing bits is expressed as:
s.t.
C2:0≤p k,m ≤p max
C3:0≤f k,m ≤f max
wherein C is total For the total number of bits that the kth user can handle in the mth slot, C total ＝C local +C offload ，C local The total number of bits that can be handled locally in the mth slot for the kth user, C offload The number of bits that can be processed by task offloading in the mth slot for the kth user; p is p k,m ，f k,m Respectively representing the transmitting power and the local computing power of the kth user in the mth time slot, wherein K is the total number of intelligent vehicle users randomly distributed in a certain range at the juncture of the coverage areas of the two RSUs; m is the total number of time slots allocated to each user; e (E) max An upper limit of energy is consumed in a single time slot for each user,for offloading selection factors->When->At the same time, the user is shown to offload tasks to the first RSU when +.>When the user is represented to offload tasks to the second RSU; theta (theta) k.m Is an IRS phase shift matrix; c1 represents the total energy limit for locally calculating and transmitting the offloading task in each time slot of the user, τ is the length of each time slot, α is the calculation complexity of the task, and ζ is the calculation energy consumption parameter of the user; c2 and C3 are respectively the transmission power p k,m And user local computing power f k,m Is defined by the domain, p max Representing the maximum value of the transmission power, f max Representing a user local computing power maximum; c4 denotes the off-load selection factor->The value of (2) is 0 or 1; c5 and C6 define the phase shift matrix and its constraint of the intelligent reflecting surface, N is the total number of reflecting units of the intelligent reflecting surface, phi k,m,n Is the phase of the kth reflection unit at the nth time slot of the mth user.
Further, for the unloading model, an optimal unloading scheme is obtained based on decoupling thought calculation, which comprises the following steps:
for an unloading model, based on the decoupling idea, firstly optimizing an IRS phase shift matrix; then substituting the phase shift matrix into the original problem and then solving the transmitting power and the local computing capacity of the user; for offloading the selection factors, the problem is thatAnd->Respectively solving, and selecting an unloading scheme with larger maximum processing bit number after comparison.
Further, the optimizing the IRS phase shift matrix includes:
the optimal phase for each IRS unit is derived from the triangle inequality:
φ k,m,n ＝arg(h k,m )-arg(b k,m [n])
wherein phi is k,m,n For the phase of the kth reflection unit at the nth slot of the mth user, arg (x) denotes the phase factor, h k,m B is the current direct path channel state k,m [n]For the nth reflection sheet of IRSElement, b k,m ＝diag(G k,m )h r,k,m Wherein h is r,k,m For the channel state information of kth user to IRS, G k,m Representing channel state information from the IRS to the RSU.
Further, solving the transmitting power and the local computing power of the user after substituting the phase shift matrix into the original problem comprises the following steps:
after the optimization result of the phase shift matrix is obtained, setting S= |h k,m +G k,m Θ k,m h r,k,m | 2 Substituting S into the original problem to obtain:
s.t.
C2:0≤p k,m ≤p max
C3:0≤f k,m ≤f max
wherein B, sigma 2 Channel bandwidth and noise power, respectively;
let variable p k,m By the variable f k,m After representation, this problem is equivalent to the following formula:
s.t.
C3:0≤f k,m ≤f max
the solution to the equivalence problem is obtained as:
wherein,
further, selecting an offloading scheme with a larger maximum number of processing bits after comparison, including:
respectively solving the total processing bit number when the first RSU and the second RSU are unloaded, comparing the total processing bit number when the first RSU and the second RSU are unloaded, wherein a larger value is the maximum total processing bit number, and determining an unloading decision factor according to the unloading conditionTo obtain the offloading scheme.
In yet another aspect, the present invention also provides an electronic device including a processor and a memory; wherein the memory stores at least one instruction that is loaded and executed by the processor to implement the above-described method.
In yet another aspect, the present invention also provides a computer readable storage medium having at least one instruction stored therein, the instruction being loaded and executed by a processor to implement the above method.
The technical scheme provided by the invention has the beneficial effects that at least:
1) According to the invention, the intelligent reflection surface IRS is introduced, and a novel multi-MEC cooperative unloading strategy is provided according to the characteristics of the IRS, compared with the prior art, the IRS performance can be more fully exerted, the Internet of vehicles unloading strategy is enriched, and the vehicle user unloading efficiency is improved.
2) Compared with the prior art, the method can improve the quality and stability of the unloading link and improve the quality of the unloading service of the cell-edge vehicle users by selecting the RSU to be unloaded according to the channel state.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a multi-MEC collaborative Internet of vehicles scenario incorporating intelligent reflective surfaces;
FIG. 2 is a graphical illustration of the change in the total number of bits processed by a user with respect to the number of IRS reflection units.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings.
First embodiment
Aiming at the problems that an unloading link is poor and the unloading efficiency is reduced due to frequent movement of a user of the internet of vehicles to the edge of a cell with poor signal quality, the embodiment provides an intelligent reflection surface-assisted internet of vehicles multi-MEC unloading method which can be realized by electronic equipment. Modeling the user performance of the Internet of vehicles, the IRS state and the selection of the unloading RSU as an optimization problem aims to increase the total processing bit number of the user to the greatest extent by jointly optimizing the selection of the local computing capacity, the user transmitting power, the IRS phase and the unloading RSU. The execution flow of the method comprises the following steps:
s1, constructing a multi-mobile-edge computing MEC collaborative Internet of vehicles scene introducing an intelligent reflection surface IRS;
specifically, in the present embodiment, a multi-MEC collaborative internet of vehicles scenario incorporating an intelligent reflective surface is shown in fig. 1. The system comprises two roadside units provided with MEC servers, and K intelligent vehicle users are randomly distributed in a certain range at the juncture of coverage areas of the two RSUs. The user's tasks may be performed locally or offloaded to the roadside MEC server. However, these users are far away from transmission and have the situation of obstacle occlusion, etc. no matter which RSU is unloaded to, the location of the cell edge, the channel condition is poor. Thus, we deploy at the midpoint of the two RSU linksAn intelligent reflection plane is provided, and a user can improve the channel state through superposition of the direct path and the IRS path. To avoid interference between users during computational offloading, the task offloading phase of the users employs time division multiple access (Time Division Multiple Access, TDMA), each time slot having a length τ. Each user is assigned M time slots to transmit or perform tasks locally. Wherein h is 1,k,m ，h 2,k,m Direct path channel state information to RSU-1 and RSU-2 in the mth slot of the kth user, respectively. h is a r,k,m For the kth user to IRS channel state information, the channel state information from IRS to RSU-1 and RSU-2 is G 1,k,m And G 2,k,m 。
Let p k,m ，f k,m Representing the transmit power and local computing power of the kth user in the mth slot, respectively. And E is max The upper limit of energy consumed in a single time slot for each user is then the energy constraint in a single time slot for each user is:wherein alpha is the computational complexity (cycles/bit) of the task, and xi is the energy consumption parameter calculated by the user and is related to the chip.
The total number of bits that the kth user can locally process in the mth slot is:
by introducing the intelligent reflecting surface, the transmission channel from the user to the RSU is overlapped with the reflecting path passing through the IRS on the basis of the direct path, and the channel state is improved. Thus, the uplink transmission rates for user k to offload to RSU-1 and RSU-2 in the mth slot are:
wherein B, sigma 2 Channel bandwidth and noise power, respectively, Θ 1 ，Θ 2 IRS phase shift matrices when offloaded to RSU-1 and RSU-2, respectively. Wherein the method comprises the steps of
The intelligent reflecting surface has N reflecting units phi n Is the phase of the nth reflection unit. By varying phi n The intelligent reflecting surface can adjust the phase of the reflecting path so as to achieve the effect of superposition or offset with the direct path.
Since the resulting data size is much smaller than the task data size and the transmit power of the RSU is much greater than the transmit power of the vehicle, the downlink transmission delay will be much smaller than the uplink transmission delay. Therefore, the downlink transmission delay can be ignored.
S2, constructing a multi-MEC diversity unloading model based on the multi-MEC cooperative internet of vehicles scene, and modeling the internet of vehicles user performance, the IRS state and the unloading selection as an optimization problem; maximizing the total number of processing bits by jointly optimizing the local computing power, the user transmit power, the IRS phase, and the selection of the offload RSU;
it should be noted that, in order to improve the stability and the service quality of task offloading and make full use of the characteristics of the user at the junction of two cells, we introduce an offloading selection factorSelecting an unloading target according to the channel state information, selecting a better RSU to carry out task unloading after comparing the current channel states of the user to the two RSUs, and when +.>Time indicationThe user offloads the task to RSU-1 when +.>Indicating that the user is offloading tasks to RSU-2. The total number of bits that can be transmitted to the offload target RSU in one slot is:
wherein R is 1,k,m ，R 2,k,m The uplink transmission rates for the kth user to offload to RSU-1 and RSU-2 at the mth time slot, respectively.
Because the MEC is computationally powerful and the amount of task uploaded in one slot is small, we ignore the time for the MEC to process the task. And because the downlink transmission delay can be ignored, the number of bits for completing the processing through task unloading in one time slot is as follows:
C offload ＝C transmit
on the basis, the user can also distribute limited energy between task unloading and local calculation by jointly optimizing the transmitting power and the local calculation capacity, so as to achieve the aim of maximizing the total processing bit number.
Thus, the problem of maximizing the total number of processing bits can be expressed as:
s.t.
C2:0≤p k,m ≤p max
C3:0≤f k,m ≤f max
wherein C is total For the total number of bits that the kth user can handle in the mth slot, C total ＝C local +C offload ；p k,m ，f k,m Respectively representing the transmitting power and the local computing capacity of the kth user in the mth time slot; e (E) max An upper limit to consume energy in a single time slot for each user; unloading the selection factor asWhen->When it means that the user offloads the task to RSU-1, when +.>Indicating that the user offloads the task to the RSU-2; theta (theta) k.m Is an IRS phase shift matrix; c1 represents the total energy limit for locally calculating and transmitting the offloading task in each time slot of the user, τ is the length of each time slot, α is the calculation complexity of the task, and ζ is the calculation energy consumption parameter of the user; c2 and C3 are respectively the transmission power p k,m And user local computing power f k,m Is defined by the domain, p max Representing the maximum value of the transmission power, f max Representing a user local computing power maximum; c4 denotes the off-load selection factor->Can take on a value of 0 or 1; c5 and C6 define the phase shift matrix and its constraint of the intelligent reflecting surface, N is the total number of reflecting units of the intelligent reflecting surface, phi k,m,n Is the phase of the kth reflection unit at the nth time slot of the mth user.
The change in the total number of processing bits for a user with respect to the number of IRS reflection units is shown in fig. 2.
And S3, calculating an optimal unloading scheme based on the decoupling idea for the unloading model.
Specifically, in this embodiment, S3 specifically includes: aiming at the unloading model, a novel alternating solution algorithm for time sequence decoupling in the scene is provided based on the decoupling idea. Each time slot in the TDMA is only allocated to one user, and resources allocated to the users are decoupled, so that only a single time slot of the proposed problem is required to be optimized respectively, and then all the time slots are summed to obtain the solution of the original problem. Because the proposed problem is characterized by decoupling and non-convexity of the time sequence, when solving the proposed optimization problem, the problem is firstly decoupled according to the time slot, each item of the decoupled problem is not jointly convex for each variable, and each item of the original problem is decomposed into two convex sub-problems and is solved alternately. The IRS phase shift matrix is optimized first, then the phase shift matrix is substituted into the original problem, and then the transmitting power and the local computing capacity of the user are solved. For offloading the selection factors, we will have the problem inAnd->Respectively solving, and selecting an unloading scheme with larger maximum processing bit number after comparison.
The specific optimization solving process is as follows:
s31, optimizing the intelligent reflecting surface phase shift matrix.
Each reflection unit of the intelligent reflection surface can adjust the phase of an input IRS signal, and when the phase of the IRS path signal finally reaching the RSU is the same as that of the direct path signal, the amplitude of the two paths of signals are overlapped, and the channel state is optimal. Thus, the optimization problem of the phase shift matrix when offloaded to RSU-1 is as follows:
to solve the above problem, we let
|h 1,k,m +G 1,k,m Θ 1,k,m h r,k,m |＝|h 1,k,m +θ 1,k,m b 1,k,m |
Wherein b 1,k,m ＝diag(G 1,k,m )h r,k,m ，|θ k,m,n |=1. Then, from the triangle inequality, the upper bound of the problem can be obtained
Wherein b 1,k,m [n]B is 1,k,m Is a reflection unit n. The upper bound of the phase factor is:
φ k,m,n ＝arg(h 1,k,m )-arg(b 1,k,m [n])
wherein arg refers to the phase factor. Will phi k,m,n Substituting θ 1,k,m Obtaining the optimal IRS channel state information theta when unloading to RSU-1 1,k,m b 1,k,m And |h 1,k +θ 1,k b 1,k The value of i. The phase shift matrix optimization when offloaded to RSU-2 is the same and will not be described in detail here.
S32, optimizing the user transmitting power and the computing power.
After the optimization result of the phase shift matrix is obtained, S is set 1 ＝|h 1,k,m +G 1,k,m Θ 1,k,m h r,k,m | 2 ，S 2 ＝|h 2,k,m +G 2,k,m Θ 2,k,m h r,k,m | 2 . Taking the unloading to RSU-1 as an example, substituting S1 into the original objective function can result in:
s.t.
C2:0≤p k,m ≤p max
C3:0≤f k,m ≤f max
by observing this problem, it can be found that the constraint C1 must take an equal sign when the objective function takes its maximum value, i.eTherefore, according to +.>Let variable p k,m By the variable f k,m After representation, the problem can be equivalent to the following formula:
s.t.
C3:0≤f k,m ≤f max
the objective function of the above problem is set to F (F k,m ) Order-making The first and second derivatives of the objective function are respectively
Wherein F "(F) k,m ) Constant is less than or equal to 0So the objective function F (F k,m ) Is a convex function. Also because the s.t. condition C2, C3 is obviously convex, the problem can be found in F' (F k,m ) Optimal solution is obtained when=0:
s33, based on the above, for each time slot of each user, according to the channel state from the user to the RSU-1, firstly, obtaining an intelligent reflecting surface phase shift matrix through phase shift matrix optimization, substituting the intelligent reflecting surface phase shift matrix into the original problem, and then, obtaining the total processing bit number in the time slot through solving the convex optimization problem to optimize the user transmitting power and the computing capacity. The total processing bit number when the task is unloaded to the RSU-2 is calculated by the same way, the total processing bit numbers of the two conditions of unloading to the RSU-1 and unloading to the RSU-2 are compared, wherein a larger value is the maximum total processing bit number, and meanwhile, the unloading decision factor can be determined according to the unloading conditionThe value of (2) thus->Dynamically selecting an offload RSU.
In summary, in order to solve the problem that the internet of vehicles users often move to the cell edge with poor signal quality, resulting in poor unloading link and reduced unloading efficiency, the embodiment provides an intelligent reflection surface-assisted internet of vehicles multi-MEC diversity unloading strategy. And the quality and stability of the unloading link of the cell-edge Internet of vehicles user can be improved by actively regulating and controlling the signal and the unloading selection.
Second embodiment
The embodiment provides an electronic device, which comprises a processor and a memory; wherein the memory stores at least one instruction that is loaded and executed by the processor to implement the method of the first embodiment.
The electronic device may vary considerably in configuration or performance and may include one or more processors (central processing units, CPU) and one or more memories having at least one instruction stored therein that is loaded by the processors and performs the methods described above.
Third embodiment
The present embodiment provides a computer-readable storage medium having stored therein at least one instruction that is loaded and executed by a processor to implement the method of the first embodiment described above. The computer readable storage medium may be, among other things, ROM, random access memory, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. The instructions stored therein may be loaded by a processor in the terminal and perform the methods described above.
Furthermore, it should be noted that the present invention can be provided as a method, an apparatus, or a computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the invention may take the form of a computer program product on one or more computer-usable storage media having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or terminal device comprising the element.
It is finally pointed out that the above description of the preferred embodiments of the invention, it being understood that although preferred embodiments of the invention have been described, it will be obvious to those skilled in the art that, once the basic inventive concepts of the invention are known, several modifications and adaptations can be made without departing from the principles of the invention, and these modifications and adaptations are intended to be within the scope of the invention. It is therefore intended that the following claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the embodiments of the invention.

Claims (1)

1. An intelligent reflector-assisted internet of vehicles multi-MEC unloading method is characterized by comprising the following steps of:
constructing a multi-mobile-edge computing MEC collaborative Internet of vehicles scene introducing an intelligent reflection surface IRS;
based on the multi-MEC cooperative internet of vehicles scene, constructing a multi-MEC diversity unloading model, and modeling the internet of vehicles user performance, IRS state and unloading selection as an optimization problem; maximizing the total number of processing bits by jointly optimizing the local computing power, the user transmit power, the IRS phase, and the selection of the offload RSU;
for the unloading model, calculating to obtain an optimal unloading scheme based on a decoupling idea;
a plurality of roadside units (RSUs) provided with MEC servers are arranged in a multi-MEC cooperative Internet of vehicles scene of introducing IRS, and a plurality of intelligent vehicle users are randomly distributed in a certain range at the juncture of coverage areas of the two RSUs; the user's tasks may be performed locally or offloaded to the RSU's MEC server; an intelligent reflection plane is arranged at the midpoint of the connecting line of the two RSUs; the task unloading stage of the user adopts time division multiple access;
the roadside unit RSU comprises a first RSU and a second RSU;
the problem of maximizing the total number of processing bits is expressed as:
wherein C is total For the total number of bits that the kth user can handle in the mth slot, C total ＝C local +C offload ，C local Can be in the mth time slot for the kth userTotal number of bits processed locally, C offload The number of bits that can be processed by task offloading in the mth slot for the kth user; p is p k,m ，f k,m Respectively representing the transmitting power and the local computing power of the kth user in the mth time slot, wherein K is the total number of intelligent vehicle users randomly distributed in a certain range at the juncture of the coverage areas of the two RSUs; m is the total number of time slots allocated to each user; e (E) max An upper limit of energy is consumed in a single time slot for each user,for offloading selection factors->When->At the time, the user is shown to offload tasks to the first RSU whenWhen the user is represented to offload tasks to the second RSU; theta (theta) k.m Is an IRS phase shift matrix; c1 represents the total energy limit for locally calculating and transmitting the offloading task in each time slot of the user, τ is the length of each time slot, α is the calculation complexity of the task, and ζ is the calculation energy consumption parameter of the user; c2 and C3 are respectively the transmission power p k,m And user local computing power f k,m Is defined by the domain, p max Representing the maximum value of the transmission power, f max Representing a user local computing power maximum; c4 denotes the off-load selection factor->The value of (2) is 0 or 1; c5 and C6 define the phase shift matrix and its constraint of the intelligent reflecting surface, N is the total number of reflecting units of the intelligent reflecting surface, phi k,m,n The phase of the kth reflection unit at the nth time slot of the mth user;
for the unloading model, based on the decoupling idea, calculating to obtain an optimal unloading scheme, including:
for an unloading model, based on the decoupling idea, firstly optimizing an IRS phase shift matrix; then substituting the phase shift matrix into the original problem and then solving the transmitting power and the local computing capacity of the user; for offloading the selection factors, the problem is thatAnd->Respectively solving, and selecting an unloading scheme with larger maximum processing bit number after comparison;
the optimizing IRS phase shift matrix comprises:
the optimal phase for each IRS unit is derived from the triangle inequality:
φ k,m,n ＝arg(h k,m )-arg(b k,m [n])
wherein phi is k,m,n For the phase of the kth reflection unit at the nth slot of the mth user, arg (x) denotes the phase factor, h k,m B is the current direct path channel state k,m [n]N-th reflecting unit for IRS, b k,m ＝diag(G k,m )h r,k,m Wherein h is r,k,m For the channel state information of kth user to IRS, G k,m Representing channel state information from the IRS to the RSU;
substituting the phase shift matrix into the original problem and then solving the transmitting power and the local computing capacity of the user, wherein the method comprises the following steps:
after the optimization result of the phase shift matrix is obtained, setting S= |h k,m +G k,m Θ k,m h r,k,m | 2 Substituting S into the original problem to obtain:
wherein B, sigma 2 Channel bandwidth and noise power, respectively;
let variable p k,m By the variable f k,m After representation, this problem is equivalent to the following formula:
the solution to the equivalence problem is obtained as:
wherein,
selecting an offloading scheme with a larger maximum number of processing bits after comparison, comprising:
respectively solving the total processing bit number when the first RSU and the second RSU are unloaded, comparing the total processing bit number when the first RSU and the second RSU are unloaded, wherein a larger value is the maximum total processing bit number, and determining an unloading decision factor according to the unloading conditionTo obtain the offloading scheme.
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