CN115334519A - User association and phase shift optimization method and system in unmanned aerial vehicle IRS network - Google Patents
User association and phase shift optimization method and system in unmanned aerial vehicle IRS network Download PDFInfo
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Abstract
The invention provides a method and a system for optimizing user association and phase shift in an unmanned aerial vehicle IRS network, wherein the optimization method comprises the following steps of S1: initializing network deployment, flying the unmanned aerial vehicle to a service area, and initializing the transmitting power, the IRS position and the user position of the unmanned aerial vehicle, wherein the IRS represents an intelligent reflecting surface; s2: decoupling the user association and the decoding sequence according to a full interference expansion scheme, converting the user association and phase shift optimization into a convex optimization problem, and acquiring an association scheme; s3: performing initial point optimization of IRS reflection coefficient according to the association scheme in S2 to obtain an initial scheme; s4: according to the initial scheme in the S3, traversing all IRSs and reflection units thereon to optimize the reflection coefficient; s5: and judging whether the output result of the S4 is converged, if so, ending, and if not, returning to the S2 for circular optimization until convergence.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of unmanned aerial vehicle information processing, in particular to a user association and phase shift optimization method and system in an unmanned aerial vehicle IRS network.
[ background of the invention ]
Whether channel estimation and modulation coding in small-scale fading channels or network planning and optimization in large-scale fading channels, a channel model is always an important base stone for system design, theoretical analysis, performance evaluation, system optimization and deployment. Therefore, the method scientifically develops the channel characteristic analysis and modeling theory research of the wireless communication network, can give consideration to the development direction of the future wireless communication core technology, and is expected to become an important component part for establishing the 6G wireless communication system. In a wireless communication channel, although the network physical layer technology can generally adapt to the change of a wireless environment in space and time, the signal propagation is random in nature and is largely uncontrollable. Research shows that, when the IRS is placed between the transmitting end and the receiving end of a wireless channel, it can independently adjust and control the phase (or/and) amplitude and even frequency of an incident signal, and solves the problem of strong directivity but insufficient coverage of high-frequency band communication, so in recent years, the IRS has been selected by the industry as a potential communication technology capable of intelligently improving the radio propagation environment.
IRS consists of a set of intelligent reflective elements, each of which can individually improve the signal quality of the initially received signal, typically in terms of phase, frequency and amplitude. The current research on IRS mainly considers improving the performance of transmitting signals by phase shift, so that IRS does not need to consume additional transmitting power, and is energy-saving, unlike other technologies. For IRS deployments, it is common to fix on building exterior walls, moving vehicles and indoor ceilings to help enhance information transfer between transmitters and receivers in a cellular base station scenario.
In order to maximize the user data rate in the IRS network, the invention designs a user association and phase shift optimization method in the IRS network of the unmanned aerial vehicle.
[ summary of the invention ]
In view of this, the present invention provides a method and a system for optimizing user association and phase shift in an IRS network of an unmanned aerial vehicle, which maximizes a data rate of the network through the phase shift optimization of user association and IRS for an IRS-assisted multi-IRS wireless communication network.
In one aspect, the invention provides a method for optimizing user association and phase shift in an IRS network of an unmanned aerial vehicle, wherein the method for optimizing comprises the following steps:
s1: initializing network deployment, enabling the unmanned aerial vehicle to fly to a service area, enabling the unmanned aerial vehicle to serve as a base station to communicate with users, pre-deploying multiple IRSs to enhance communication, and initializing the transmitting power, the IRS position and the user position of the unmanned aerial vehicle;
s2: establishing user association between the IRS and the user and optimizing the user association according to the unmanned aerial vehicle transmitting power, the IRS position and the user position in the S1, decoupling the coupling relation between the user association and a decoding sequence of continuous interference elimination in a pre-deployment network according to a full interference expansion scheme, converting the optimization problem of the user association into a convex optimization problem, and acquiring an association scheme;
s3: performing initial point optimization of IRS reflection coefficient according to the association scheme in S2 to obtain an initial scheme;
s4: according to the initial scheme in the S3, traversing all IRSs and reflection units thereon to optimize the reflection coefficient;
s5: and judging whether the output result of the S4 is converged, if so, ending, and if not, returning to the S2 for circular optimization until convergence.
As to the above-mentioned aspect and any possible implementation manner, there is further provided an implementation manner, where S2 specifically is:
according to the full interference expansion scheme, decoupling is carried out on user association and a decoding sequence, a user association problem is converted into a convex optimization problem, a user association scheme is obtained through solving the problem, and the problem can be expressed as follows:
wherein the content of the first and second substances,
for the expression after using the full interference extension scheme, n and n' represent users, m represents IRS,
representing the data rate of user n at IRS m, the ex representation is the expression after transformation by the full interference extension scheme,
representing the association factor of IRS m with user n, which equals 1 to indicate successful association, equals 0 to fail, vm,n a matrix of correlation factors is represented.
Indicating the number of users which can be associated by an IRS at most; p is a radical of m,n Representing the transmit power of user n on IRS m,
channel gain matrix representing drone to IRS mT denotes the transpose matrix, Θ m Reflection coefficient, i.e. phase-shift matrix, g representing IRS m m,n Representing the channel gain matrix from IRS m to user n. N 'is the same as N/N to indicate that the user N' is the set of all users except the user N, N indicates the set of users, and sigma indicates the set of users 2 Representing the gaussian noise power.
The above-mentioned aspect and any possible implementation further provide an implementation, and the starting point scheme of the IRS reflection coefficient in S3 is obtained by the following formula:
wherein, the first and the second end of the pipe are connected with each other,
the value of the variable at which delta causes the objective function f to satisfy the maximum value is expressed,
denotes the initial reflection coefficient of the IRS, delta denotes the step size introduced,
when the reflection angle is
The data rate of user n on IRSm at that time, ε represents the approximate gradient, which can be expressed as
Wherein, the iota 0 Representing a single column vector of elements all 1, with a number of dimensions equal to the number of reflection units on the IRS, N m For the set of all associated users on IRSm, N m,n Is the set of remaining users on IRS m with which user n is associated.
The above-mentioned aspect and any possible implementation further provides an implementation, and the reflection coefficient optimization in S4 can be obtained by the following formula:
therein, ζ m,k Expressed as an auxiliary variable introduced by the kth reflecting element on IRSm,
the reflection coefficient matrix, Δ, representing IRSm after the j-th iteration k Representing the kth column of the matrix delta, wherein the matrix delta is a diagonal matrix with the main diagonal element number of 1 and the dimension number of the diagonal matrix is equal to the number of reflection units on the IRS; the above procedure for optimizing one variable at a time, whereby first all IRSs are traversed and then all reflection units are traversed on each IRS, the value ζ of each reflection unit being determined m,k Updating the reflection coefficient matrix on the IRS once
Thus, the optimization is finished after all the reflection units are traversed.
As to the above-mentioned aspect and any possible implementation manner, an implementation manner is further provided, where the convergence criterion in S5 is that convergence is determined if a variation of the objective function in two iteration processes before and after is smaller than a set threshold, otherwise, the loop iteration is continued without convergence.
In accordance with the foregoing aspect and any possible implementation manner, there is further provided an implementation manner, in S1, the drone plays a role of a base station in the network, and the transmission signal enhancement of the drone is delivered to the remote user through a plurality of IRS.
The above-mentioned aspects and any possible implementations further provide an implementation, where initializing content in S1 includes initializing drone transmit power, an IRS location, and a user location, where the IRS represents an intelligent reflective surface.
The above-mentioned aspect and any possible implementation manner further provide a user association and phase shift optimization system in an IRSs network, including the optimization method, where the optimization system includes:
the network deployment initialization module is used for initializing network deployment, the unmanned aerial vehicle flies to a service area, the unmanned aerial vehicle serves as a base station to communicate with a user, multiple IRSs are pre-deployed to enhance communication, and the transmitting power, the IRS position and the user position of the unmanned aerial vehicle are initialized;
the decoupling module is used for establishing user association between the IRS and the user and optimizing the user association according to the transmitting power of the unmanned aerial vehicle, the IRS position and the user position, decoupling the coupling relation between the user association and a decoding sequence for eliminating continuous interference in a pre-deployment network according to a full-interference expansion scheme, converting the optimization problem of the user association into a convex optimization problem and acquiring an association scheme;
the initial point optimization module is used for carrying out initial point optimization on the IRS reflection coefficient according to the association scheme to obtain an initial scheme;
the reflection coefficient optimization module is used for traversing all IRSs and reflection units thereon to optimize the reflection coefficient according to the initial scheme;
and the judgment output module is used for judging whether the result output by the reflection coefficient optimization module is converged, if so, ending and outputting the optimization scheme, and if not, returning to the decoupling module for circular optimization until convergence.
Compared with the prior art, the invention can obtain the following technical effects:
the invention maximizes the user data rate of the multi-IRS network, provides a method based on full interference expansion and approximate univariate optimization, and achieves convergence through loop iteration so as to improve the user data rate of the multi-IRS network assisted by the unmanned aerial vehicle.
Of course, it is not necessary for any product to achieve all of the above-described technical effects simultaneously in the practice of the invention.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow diagram of user association and phase shift optimization provided by one embodiment of the present invention;
FIG. 2 is a network scenario deployment diagram provided by one embodiment of the invention;
fig. 3 is a diagram of an electronic device provided by an embodiment of the invention.
[ detailed description ] embodiments
For better understanding of the technical solutions of the present invention, the following detailed descriptions of the embodiments of the present invention are provided with reference to the accompanying drawings.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The invention provides a user association and phase shift optimization method in an unmanned aerial vehicle IRS network.
As shown in fig. 1, the method for optimizing user association and phase shift provided by the present invention specifically includes:
s1: initializing network deployment, enabling the unmanned aerial vehicle to fly to a service area, enabling the unmanned aerial vehicle to serve as a base station to communicate with users, pre-deploying multiple IRSs to enhance communication, and initializing the transmitting power, the IRS position and the user position of the unmanned aerial vehicle;
s2: establishing user association between the IRS and the user and optimizing the user association according to the unmanned aerial vehicle transmitting power, the IRS position and the user position in the S1, decoupling the coupling relation between the user association and a decoding sequence of continuous interference elimination in a pre-deployment network according to a full interference expansion scheme, converting the optimization problem of the user association into a convex optimization problem, and acquiring an association scheme;
s3: performing initial point optimization of IRS reflection coefficient according to the association scheme in S2 to obtain an initial scheme;
s4: according to the initial scheme in the S3, traversing all IRSs and reflection units thereon to optimize the reflection coefficient;
s5: and judging whether the output result of the S4 is converged, if so, ending, and if not, returning to the S2 for circular optimization until convergence.
The optimization method of the embodiment of the invention decouples the coupling relation between the user association and the decoding sequence of continuous interference elimination in the NOMA network by using a full interference expansion scheme, so that the user association problem is converted into a convex optimization problem only containing association coefficients, and then the phase shift optimization of multiple IRSs is carried out by a univariate search method based on gradient approximation.
In this embodiment, an initialization network deployment is performed first, the unmanned aerial vehicle flies to a service area, and the transmission power, the IRS position, and the user position of the unmanned aerial vehicle are initialized, where the IRS represents an intelligent reflecting surface.
The method specifically comprises the following steps: as shown in fig. 2, that is, the drone flies to a region needing service, a user in a remote region performs enhanced communication through a plurality of IRS pre-deployed between the user and the drone, the drone plays a role of a base station in a network, the transmitted signal of the drone is enhanced and transmitted to a remote user through the plurality of IRS, and the initialization content includes the drone transmitting power, the IRS location and the user location.
In a specific embodiment of the foregoing method for optimizing user association and phase shift, the S2: establishing user association between the IRS and the user and optimizing the user association by using the transmitting power, the IRS position and the user position of the unmanned aerial vehicle in the S1, decoupling the coupling relation between the user association and a decoding sequence for eliminating continuous interference in a pre-deployment network according to a full-interference expansion scheme, converting the optimization problem of the user association into a convex optimization problem, and acquiring an association scheme specifically comprises the following steps:
and decoupling the user association and the decoding sequence according to the full interference expansion scheme, so that the problem of association optimization can be converted into a convex optimization problem as follows, and then the convex optimization problem is directly solved through a convex optimization tool box. The problem is expressed as:
wherein the content of the first and second substances,
to use the full interference augmented expression, n and n' represent users, m represents IRS,
representing the data rate of user n at IRS m, the ex representation is the expression after transformation by the full interference extension scheme,
representing the association factor of IRS m with user n, which equals 1 to indicate successful association, equals 0 to fail, vm,n a matrix of relevance factors is represented.
Indicating the number of users that an IRS can associate with at most. p is a radical of m,n Representing the transmit power of user n on IRS m,
denotes the channel gain matrix from drone to IRS m, T denotes the transpose matrix, Θ m Reflection coefficient, i.e. phase-shift matrix, g representing IRS m m,n Representing the channel gain matrix IRS m to user n. N 'is the same as N/N to indicate that the user N' is the set of all users except the user N, N indicates the set of users, and sigma indicates the set of users 2 Representing the gaussian noise power.
In this embodiment, the step S3: performing initial point optimization of the IRS reflection coefficient according to the association scheme in the S2, and obtaining an initial scheme specifically comprises the following steps:
and (4) optimizing a reflection coefficient matrix of the IRS according to the correlation factor matrix obtained in the step (2). The starting point optimization is first performed by the following formula to obtain a better initial effect.
Wherein, iota 0 Representing a single column vector with elements all 1, the number of dimensions of which is equal to the number of reflection units, N, on the IRS m Is the set of all associated users on IRSm, N m,n The rest physical quantities are the same as above for the set of rest users on the IRS m associated with the user n.
In this embodiment, the step S4: according to the initial scheme in S3, traversing all IRS and the reflection units thereon to optimize the reflection coefficient specifically includes:
and optimizing the reflection coefficient on each IRS based on the result obtained in the step 3. The optimization is performed by the following formula:
therein, ζ m,k Expressed as an auxiliary variable introduced by the kth reflecting element on IRSm,
reflection coefficient matrix, Δ, representing IRSm after the j-th iteration k Represents the kth column of matrix delta, and matrix delta is a diagonal matrix with a main diagonal element number of 1, with a number of dimensions equal to the number of reflection elements on the IRS. The above procedure for optimizing one variable at a time, therefore, first traverses all IRSs, then traverses all reflection units on each IRS, and finds the value ζ of one reflection unit each time m,k Updating the reflection coefficient matrix on the IRS once
Thus, the optimization is finished after all the reflection units are traversed.
In this embodiment, the step S5: judging whether the result output by the S4 is converged, if so, ending, and if not, returning to the S2 for circular optimization until the convergence is specifically:
and (4) judging whether the numerical value obtained in the step (4) is converged, wherein the convergence standard is whether the forward and backward variation amplitude of the target function is smaller than a specific value, if so, convergence is carried out, and the optimization is finished, and if not, returning to the step (2) to continue iterative optimization until convergence.
In this embodiment, the initial value of the reflection coefficient used in S4 is the value obtained in S3, S4 needs to optimize all reflection units on all IRS sequentially on the basis of this initial value to obtain the variation value corresponding to each reflection unit, and optimize the next reflection unit on the basis of the updated coefficient of the previous reflection unit, so that the result of completing the optimization after traversing all the reflection units is the result of completing the optimization.
In this embodiment, in S5, the criterion for determining convergence is whether a difference between the target function and the target function in the last iteration process is smaller than a specific value, and if so, convergence is determined.
In an exemplary embodiment, there is further provided a user association and phase shift optimization system in an IRSs network, including the optimization method, the optimization system including:
the system comprises an initialization network deployment module, a network deployment module and a network deployment module, wherein the initialization network deployment module is used for initializing network deployment, enabling an unmanned aerial vehicle to fly to a service area, enabling the unmanned aerial vehicle to serve as a base station to communicate with a user, pre-deploying multiple IRSs to enhance communication, and initializing the emission power, the IRS position and the user position of the unmanned aerial vehicle;
the decoupling module is used for establishing user association between the IRS and the user and optimizing the user association according to the transmitting power of the unmanned aerial vehicle, the IRS position and the user position, decoupling the coupling relation between the user association and a decoding sequence for eliminating continuous interference in a pre-deployment network according to a full-interference expansion scheme, converting the optimization problem of the user association into a convex optimization problem and acquiring an association scheme;
the initial point optimization module is used for performing initial point optimization of the IRS reflection coefficient according to the association scheme to obtain an initial scheme;
the reflection coefficient optimization module is used for traversing all IRSs and reflection units thereon to optimize the reflection coefficient according to the initial scheme;
and the judgment output module is used for judging whether the result output by the reflection coefficient optimization module is converged, if so, ending and outputting the optimization scheme, and if not, returning to the decoupling module for circular optimization until convergence.
Fig. 3 is a schematic structural diagram of an electronic device 600 according to an embodiment of the present invention, where the electronic device 600 may generate a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 601 and one or more memories 602, where the memory 602 stores at least one instruction, and the at least one instruction is loaded and executed by the processor 601 to implement the method for user association and phase shift optimization in the unmanned aerial vehicle IRS network.
In an exemplary embodiment, a computer-readable storage medium, such as a memory, is also provided that includes instructions executable by a processor in a terminal to perform the method for user association and phase shift optimization in a drone IRS network described above. For example, the computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
The method and the system for user association and phase shift optimization in the IRS network of the unmanned aerial vehicle provided by the embodiment of the application are introduced in detail. The above description of the embodiments is only for the purpose of helping to understand the method of the present application and its core idea; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
As used in the specification and claims, certain terms are used to refer to particular components. As one skilled in the art will appreciate, manufacturers may refer to a component by different names. The present specification and claims do not intend to distinguish between components that differ in name but not function. In the following description and in the claims, the terms "include" and "comprise" are used in an open-ended fashion, and thus should be interpreted to mean "include, but not limited to. "substantially" means within an acceptable error range, and a person skilled in the art can solve the technical problem within a certain error range to substantially achieve the technical effect. The description which follows is a preferred embodiment of the present application, but is made for the purpose of illustrating the general principles of the application and not for the purpose of limiting the scope of the application. The scope of the present application is to be construed in accordance with the substance defined by the following claims.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a good or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such good or system. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in articles of commerce or systems including such elements.
It should be understood that the term "and/or" as used herein is merely one type of association that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter associated objects are in an "or" relationship.
The foregoing description shows and describes several preferred embodiments of the present application, but as aforementioned, it is to be understood that the application is not limited to the forms disclosed herein, but is not to be construed as excluding other embodiments and is capable of use in various other combinations, modifications, and environments and is capable of changes within the scope of the application as described herein, commensurate with the above teachings, or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the application, which is to be protected by the claims appended hereto.
Claims (8)
1. A method for optimizing user association and phase shift in an IRS network of an Unmanned Aerial Vehicle (UAV), the method for optimizing the user association and phase shift comprises the following steps:
s1: initializing network deployment, enabling the unmanned aerial vehicle to fly to a service area, enabling the unmanned aerial vehicle to serve as a base station to communicate with users, pre-deploying multiple IRSs to enhance communication, and initializing and setting the transmitting power, the IRS position and the user position of the unmanned aerial vehicle;
s2: establishing user association between the IRS and the user and optimizing the user association according to the unmanned aerial vehicle transmitting power, the IRS position and the user position in the S1, decoupling the coupling relation between the user association and a decoding sequence of continuous interference elimination in a pre-deployment network according to a full interference expansion scheme, converting the optimization problem of the user association into a convex optimization problem, and acquiring an association scheme;
s3: performing initial point optimization of IRS reflection coefficient according to the association scheme in S2 to obtain an initial scheme;
s4: traversing all IRSs and reflection units thereon to optimize the reflection coefficient according to the initial scheme in the S3;
s5: and judging whether the output result of the S4 is converged, if so, ending, and if not, returning to the S2 for circular optimization until convergence.
2. The optimization method according to claim 1, wherein S2 is specifically:
according to the full interference expansion scheme, decoupling is carried out on user association and a decoding sequence, a user association problem is converted into a convex optimization problem, a user association scheme is obtained through solving the problem, and the problem can be expressed as follows:
C1:
C2:
C3:
wherein the content of the first and second substances,
for the expression after using the full interference extension scheme, n and n' represent users, m represents IRS,
representing the data rate of user n on IRSm, the ex representation is the expression after transformation by the full interference extension scheme,
representing the correlation factor of IRSm with user n, where 1 means the correlation is successful, 0 means the correlation is failed, v m,n A matrix of relevance factors is represented. Theta represents the number of users which can be associated by one IRS at most; p is a radical of m,n Representing the transmit power of user n on IRSm,
representing the channel gain matrix from drone to IRSm, T represents the transpose matrix, Θ m Reflection coefficient, i.e. phase shift matrix, g representing IRSm m,n Representing the channel gain matrix IRSm to user n. N 'is the set of all users except the user N and represents the user N', and sigma 2 Representing the gaussian noise power.
3. The optimization method according to claim 2, wherein the starting point pattern of the IRS reflection coefficients in S3 is obtained by the following formula:
wherein, the first and the second end of the pipe are connected with each other,
expressing the value of the variable at which delta makes the objective function f satisfy the maximum value，
Denotes the initial reflection coefficient of the IRS, delta denotes the step size introduced,
when the reflection angle is
The data rate of user n on IRSm at that time, ε represents the approximate gradient, which can be expressed as
Wherein, the iota 0 Representing a single column vector with elements all 1, the number of dimensions of which is equal to the number of reflection units, N, on the IRS m For the set of all associated users on IRSm, N m,n Is the set of remaining users on the IRSm with which user n is associated.
4. The optimization method according to claim 3, wherein the reflection coefficient optimization in step 4 is obtained by the following formula:
therein, ζ m,k Expressed as an auxiliary variable introduced by the kth reflecting element on IRSm,
reflection coefficient matrix, Δ, representing IRSm after the j-th iteration k Representing the kth column of the matrix delta, wherein the matrix delta is a diagonal matrix with the main diagonal element number of 1 and the dimension number of the diagonal matrix is equal to the number of reflection units on the IRS; the above procedure optimizes one variable at a time, so all IRS are traversed first, thenThen, traversing all the reflection units on each IRS, and calculating the value zeta of each reflection unit m,k Then the reflection coefficient matrix on this IRS is updated once
Thus, the optimization is finished after all the reflection units are traversed.
5. The optimization method according to claim 4, wherein the convergence criterion in S5 is that convergence is determined if a variation of the objective function in two iterations is smaller than a set threshold, otherwise, the loop iteration is continued without convergence.
6. The optimization method according to claim 1, wherein in S1, the drone plays a role of a base station in the network, and the transmission signal enhancement of the drone is delivered to the remote user through a plurality of IRS.
7. The optimization method according to claim 1, wherein initializing the content in S1 includes initializing drone transmitting power, IRS position, and user position, wherein IRS represents a smart reflective surface.
8. A system for user association and phase shift optimization in an IRS network for a drone, the system comprising:
the system comprises an initialization network deployment module, a network deployment module and a network deployment module, wherein the initialization network deployment module is used for initializing network deployment, enabling an unmanned aerial vehicle to fly to a service area, enabling the unmanned aerial vehicle to serve as a base station to communicate with a user, pre-deploying multiple IRSs to enhance communication, and initializing the emission power, the IRS position and the user position of the unmanned aerial vehicle;
the decoupling module is used for establishing user association between the IRS and the user and optimizing the user association according to the transmitting power of the unmanned aerial vehicle, the IRS position and the user position, decoupling the coupling relation between the user association and a decoding sequence for eliminating continuous interference in a pre-deployment network according to a full-interference expansion scheme, converting the optimization problem of the user association into a convex optimization problem and acquiring an association scheme;
the initial point optimization module is used for performing initial point optimization of the IRS reflection coefficient according to the association scheme to obtain an initial scheme;
the reflection coefficient optimization module is used for traversing all IRSs and reflection units thereon to optimize the reflection coefficient according to the initial scheme;
and the judgment output module is used for judging whether the result output by the reflection coefficient optimization module is converged, if so, ending and outputting the optimization scheme, and if not, returning to the decoupling module for circular optimization until convergence.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113162679A (en) * | 2021-04-01 | 2021-07-23 | 南京邮电大学 | DDPG algorithm-based IRS (inter-Range instrumentation System) auxiliary unmanned aerial vehicle communication joint optimization method |
| CN113259836A (en) * | 2021-04-26 | 2021-08-13 | 北京科技大学 | IRS-assisted unmanned aerial vehicle communication network joint optimization method |
| CN113364495A (en) * | 2021-05-25 | 2021-09-07 | 西安交通大学 | Multi-unmanned aerial vehicle track and intelligent reflecting surface phase shift joint optimization method and system |
| US20210288698A1 (en) * | 2020-03-10 | 2021-09-16 | University Of Electronic Science And Technology Of China | Method for Intelligent Reflecting Surface Aided Terahertz Secure Communication System |
| WO2021207748A2 (en) * | 2020-08-13 | 2021-10-14 | Futurewei Technologies, Inc. | Methods and apparatus for channel reconstruction in intelligent surface aided communications |
| CN113645635A (en) * | 2021-08-12 | 2021-11-12 | 大连理工大学 | Design method of intelligent reflector-assisted high-energy-efficiency unmanned aerial vehicle communication system |
| CN113873575A (en) * | 2021-10-12 | 2021-12-31 | 大连理工大学 | Intelligent reflector assisted non-orthogonal multiple access unmanned aerial vehicle air-ground communication network energy-saving optimization method |
| CN113973305A (en) * | 2021-10-26 | 2022-01-25 | 西安电子科技大学 | Intelligent reflecting surface position and beam joint optimization method carried on unmanned aerial vehicle |
-
2022
- 2022-06-30 CN CN202210763154.0A patent/CN115334519B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20210288698A1 (en) * | 2020-03-10 | 2021-09-16 | University Of Electronic Science And Technology Of China | Method for Intelligent Reflecting Surface Aided Terahertz Secure Communication System |
| WO2021207748A2 (en) * | 2020-08-13 | 2021-10-14 | Futurewei Technologies, Inc. | Methods and apparatus for channel reconstruction in intelligent surface aided communications |
| CN113162679A (en) * | 2021-04-01 | 2021-07-23 | 南京邮电大学 | DDPG algorithm-based IRS (inter-Range instrumentation System) auxiliary unmanned aerial vehicle communication joint optimization method |
| CN113259836A (en) * | 2021-04-26 | 2021-08-13 | 北京科技大学 | IRS-assisted unmanned aerial vehicle communication network joint optimization method |
| CN113364495A (en) * | 2021-05-25 | 2021-09-07 | 西安交通大学 | Multi-unmanned aerial vehicle track and intelligent reflecting surface phase shift joint optimization method and system |
| CN113645635A (en) * | 2021-08-12 | 2021-11-12 | 大连理工大学 | Design method of intelligent reflector-assisted high-energy-efficiency unmanned aerial vehicle communication system |
| CN113873575A (en) * | 2021-10-12 | 2021-12-31 | 大连理工大学 | Intelligent reflector assisted non-orthogonal multiple access unmanned aerial vehicle air-ground communication network energy-saving optimization method |
| CN113973305A (en) * | 2021-10-26 | 2022-01-25 | 西安电子科技大学 | Intelligent reflecting surface position and beam joint optimization method carried on unmanned aerial vehicle |
Non-Patent Citations (3)
| Title |
|---|
| M. AL-JARRAH: "Capacity Analysis of IRS-Based UAV Communications With Imperfect Phase Compensation", < IEEE WIRELESS COMMUNICATIONS LETTERS> * |
| 姚建文;王楠;: "智能反射面――大有前景的6G技术", 电信快报, no. 07 * |
| 李亚博: "无人机辅助通信的无线网络资源优化研究", 《北京科技大学》 * |
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