CN115334519B - 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 user association and phase shift optimization method and a system in an unmanned aerial vehicle IRS network, wherein the optimization method comprises the following steps of S1: initializing network deployment, wherein the unmanned aerial vehicle flies 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 the full interference expansion scheme, and converting the user association and the phase shift optimization into a convex optimization problem to obtain an association scheme; s3: performing initial point optimization of IRS reflection coefficients according to the association scheme in the S2 to obtain an initial scheme; s4: traversing all IRS and reflection units on the IRS according to the initial scheme in the S3 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
[ field of technology ]
The invention relates to the technical field of unmanned aerial vehicle information processing, in particular to a method and a system for optimizing user association and phase shift in an unmanned aerial vehicle IRS network.
[ background Art ]
Whether the channel estimation and modulation coding in a small-scale fading channel or the network planning and optimization in a large-scale fading channel, the channel model is always an important basis for system design, theoretical analysis, performance evaluation, system optimization and deployment. Therefore, the channel characteristic analysis and modeling theoretical research of the wireless communication network are scientifically carried out, the development direction of the wireless communication core technology in the future can be considered, and the method is hopeful to become an important component for establishing a 6G wireless communication system. In wireless communication channels, network physical layer technology, while generally capable of accommodating spatial and temporal variations in the wireless environment, signal propagation is random in nature and largely uncontrollable. Research shows that IRS is used as a plane formed by a large number of passive reflecting elements with low cost, when the IRS is arranged between a transmitting end and a receiving end of a wireless channel, the IRS can independently regulate and control the phase (or/and) amplitude and even the frequency of an incident signal, and the problem that the directivity of high-frequency communication is strong but the coverage is insufficient is solved, so that the IRS is always selected as a potential communication technology capable of intelligently improving the radio propagation environment in the industry in recent years.
The IRS is made up of a set of intelligent reflective elements, each of which individually improves the signal quality of the original received signal, typically in terms of phase, frequency and amplitude. The IRS is currently studied mainly in view of improving the performance of the transmitted signal by phase shifting, so that the IRS does not need to consume additional transmission power, and has energy conservation, which is different from other technologies. For IRS deployments, it is often fixed to building exterior walls, moving vehicles, and indoor ceilings to help enhance information transmission between transmitters and receivers in cellular base station scenarios.
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 unmanned aerial vehicle IRS network, firstly, an unmanned aerial vehicle carries a base station to bear the signal transmitting work, and in order to enable the unmanned aerial vehicle to serve the user at a far place, a plurality of IRSs perform signal enhancement in the middle, and based on the setting, the user association and phase shift optimization work in a multi-IRS scene is developed to realize the maximization of the network rate.
[ invention ]
In view of the above, the invention provides a method and a system for optimizing user association and phase shift in an unmanned aerial vehicle IRS (inter-radio service) network, which aim at a multi-IRS wireless communication network assisted by unmanned aerial vehicles, and maximize the data rate of the network through the phase shift optimization of the user association and the IRS.
In one aspect, the invention provides a method for optimizing user association and phase shift in an unmanned aerial vehicle (IRS) network, which 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 a user, 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 IRS and users and optimizing the user association according to the unmanned aerial vehicle transmitting power, the IRS position and the user position in S1, decoupling the coupling relation between the user association and the decoding sequence of continuous interference elimination in the pre-deployment network according to the full interference expansion scheme, converting the optimization problem of the user association into a convex optimization problem, and acquiring the association scheme;
s3: performing initial point optimization of IRS reflection coefficients according to the association scheme in the S2 to obtain an initial scheme;
s4: traversing all IRS and reflection units on the IRS according to the initial scheme in the S3 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.
In the aspect and any possible implementation manner as described above, there is further provided an implementation manner, where S2 is specifically:
decoupling the user association and the decoding sequence according to the full interference expansion scheme, converting the user association problem into a convex optimization problem, and solving the problem to obtain the user association scheme, wherein the problem can be expressed as:
wherein,for the expression after using the full interference expansion scheme, n and n' represent users, m represents IRS, < >>The data rate representing user n on IRS m, ex represents the expression after transformation by the full interference expansion scheme, +.>An association factor representing IRS m with user n, equal to 1 indicates successful association, equal to 0 fails, vm,n representing a matrix of correlation factors. />Representing the number of users that one IRS can most associate with; p is p m,n Representing the transmit power, < > of user n on IRS m>Channel gain matrix representing unmanned aerial vehicle to IRS m, T represents transposed matrix, and Θ m Representing the reflection coefficient of IRS m, i.e. the phase shift matrix, g m,n Representing the channel gain matrix for IRS m to user n. N 'e N/N denotes that user N' is all user sets except user N, N denotes user set, sigma 2 Representing gaussian noise power.
In the aspect and any possible implementation manner as described above, there is further provided an implementation manner, where the starting point scheme of the IRS reflection coefficient in S3 is obtained by the following formula:
wherein,representing delta such that the objective function f satisfies the variable value at maximum,/>Representing the initialized reflection coefficient of the IRS, delta representing the step size introduced, < >>Indicating when the reflection angle is +.>The data rate of user n on IRSm at the time, ε, represents the approximate gradient, can be expressed as
Wherein iota is 0 A single column vector representing all 1's elements, the number of dimensions of which is equal to the number of reflecting elements on the IRS, N m For the set of all associated users on IRSm, N m,n A collection of remaining users on IRS m associated with user n.
In the aspect and any possible implementation manner as described above, there is further provided an implementation manner, where the reflection coefficient optimization in S4 may be obtained by the following formula:
wherein ζ m,k Denoted as an auxiliary variable introduced by the kth reflecting unit on IRSm,representing the reflection coefficient matrix, delta, of IRSm after the jth iteration k A kth column of the matrix delta is represented, the matrix delta is a diagonal matrix with the number of main diagonal elements being 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, thus traversing all IRSs first, then traversing all reflection units again on each IRS, each finding the value ζ of one reflection unit m,k Then update the reflection coefficient matrix on the IRS once>This optimization ends when all reflection units have been traversed.
In the aspect and any possible implementation manner described above, there is further provided an implementation manner, where the convergence criterion in S5 is that the target function variation in the two previous and subsequent iteration processes is smaller than a set threshold, and the convergence is determined, and if not, the loop iteration is continued to return.
In the aspect and any possible implementation manner described above, there is further provided an implementation manner, where the unmanned aerial vehicle in S1 plays a role of a base station in a network, and the transmission signal enhancement of the unmanned aerial vehicle is transferred to a remote user through multiple IRSs.
In the aspect and any possible implementation manner described above, there is further provided an implementation manner, where the initializing content in S1 includes initializing a drone transmitting power, an IRS location, and a user location, where the IRS represents a smart reflecting surface.
Aspects and any possible implementation manner as described above further provide a system for optimizing user association and phase shift in an unmanned aerial vehicle IRS network, including the optimizing method, where the optimizing system includes:
the initialization network deployment module is used for 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 a user, pre-deploying multiple IRS (inter-radio interference system) to enhance communication, and initializing and setting the transmitting 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, the IRS position and the user position of the unmanned aerial vehicle, decoupling the coupling relation between the user association and the decoding sequence of continuous interference elimination in the pre-deployment network according to the full interference expansion scheme, converting the optimization problem of the user association into a convex optimization problem, and acquiring the association scheme;
the starting point optimization module is used for optimizing the starting point 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 IRS and reflection units on the IRS according to the initial scheme to optimize the reflection coefficient;
and the judging output module is used for judging whether the result output by the reflection coefficient optimizing module is converged or not, if so, ending and outputting the optimizing 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, and provides a method based on full interference expansion and approximate univariate optimization, which converges through loop iteration 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 of the products embodying the invention to achieve all of the technical effects described above at the same time.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed 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 that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart 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 present invention;
fig. 3 is a diagram of an electronic device provided in one embodiment of the invention.
[ detailed description ] of the invention
For a better understanding of the technical solution of the present invention, the following detailed description of the embodiments of the present invention refers to the accompanying drawings.
It should be understood that the described embodiments are merely some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the 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 this application 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, which can be realized by electronic equipment, wherein the electronic equipment can be a terminal or a server.
As shown in fig. 1, the method for optimizing user association and phase shift provided by the 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 a user, 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 IRS and users and optimizing the user association according to the unmanned aerial vehicle transmitting power, the IRS position and the user position in S1, decoupling the coupling relation between the user association and the decoding sequence of continuous interference elimination in the pre-deployment network according to the full interference expansion scheme, converting the optimization problem of the user association into a convex optimization problem, and acquiring the association scheme;
s3: performing initial point optimization of IRS reflection coefficients according to the association scheme in the S2 to obtain an initial scheme;
s4: traversing all IRS and reflection units on the IRS according to the initial scheme in the S3 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 uses a full interference expansion scheme to decouple the coupling relation between the user association and the decoding sequence of continuous interference elimination in the NOMA network, so that the user association problem is converted into a convex optimization problem only comprising association coefficients, and then the phase shift optimization of multiple IRSs is carried out by a single variable search method based on gradient approximation.
In this embodiment, an initialization network deployment is performed first, and the unmanned aerial vehicle flies to a service area, and the unmanned aerial vehicle transmitting power, IRS position and user position are initialized, where IRS represents an intelligent reflection surface.
The method comprises the following steps: as shown in fig. 2, i.e. the unmanned aerial vehicle flies to the area to be served, the user in the remote area performs enhanced communication through the pre-deployed multiple IRSs between the user and the unmanned aerial vehicle, the unmanned aerial vehicle bears the role of a base station in the network, the transmitting signal enhancement of the unmanned aerial vehicle is transmitted to the remote user through the multiple IRSs, and the initialization content includes the transmitting power of the unmanned aerial vehicle, the IRS position and the user position.
In a specific embodiment of the foregoing method for optimizing user association and phase shift, the step S2: establishing user association between IRS and user and optimizing the user association by using the unmanned aerial vehicle transmitting power, the IRS position and the user position in S1, decoupling the coupling relation between the user association and the decoding sequence of continuous interference elimination in the pre-deployment network according to the full interference expansion scheme, converting the optimization problem of the user association into a convex optimization problem, and acquiring the 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 the following convex optimization problem, and then the problem is directly solved through a convex optimization tool box. The problems are expressed as:
wherein,to use the full interference extended expression, n and n' represent the user, m represents IRS, < >>The data rate representing user n on IRS m, ex represents the expression after transformation by the full interference expansion scheme, +.>An association factor representing IRS m with user n, equal to 1 indicates successful association, equal to 0 fails, vm,n representing a matrix of correlation factors. />Representing the number of users that one IRS can most associate with. P is p m,n Representing the transmit power, < > of user n on IRS m>Channel gain matrix representing unmanned aerial vehicle to IRS m, T represents transposed matrix, and Θ m Representing the reflection coefficient of IRS m, i.e. the phase shift matrix, g m,n Representing the channel gain matrix for IRS m to user n. N '∈N/N denotes that user N' is all user sets except user N, N denotes user setClosing sigma 2 Representing gaussian noise power.
In this embodiment, the step S3: and (3) optimizing the starting point of the IRS reflection coefficient according to the association scheme in the S2, wherein the initial scheme is obtained specifically as follows:
and (3) performing reflection coefficient matrix optimization of the IRS according to the correlation factor matrix obtained in the step (2). The starting point optimization is performed by the following formula to obtain a better initial effect.
Representing delta such that the objective function f satisfies the variable value at maximum,/>Representing the initialized reflection coefficient of the IRS, delta representing the step size introduced, < >>Indicating when the reflection angle is +.>The data rate of user n on IRSm at the time, ε, represents the approximate gradient, can be expressed as
Wherein iota is 0 A single column vector representing all 1's elements, the number of dimensions of which is equal to the number of reflecting elements on the IRS, N m For the set of all associated users on IRSm, N m,n The rest of the physical quantities are the same as above for the set of rest of the users on IRS m associated with user n.
In this embodiment, the step S4: according to the initial scheme in S3, the reflection coefficient optimization by traversing all IRS and the reflection units thereon is specifically as follows:
and (3) optimizing the reflection coefficient on each IRS based on the result obtained in the step (3). Optimization is performed by the following formula:
wherein ζ m,k Denoted as an auxiliary variable introduced by the kth reflecting unit on IRSm,representing the reflection coefficient matrix, delta, of IRSm after the jth iteration k The kth column of the matrix delta is represented, and the matrix delta is a diagonal matrix with a number of main diagonal elements of 1, the number of dimensions of which is equal to the number of reflection units on the IRS. The above procedure for optimizing one variable at a time, thus traversing all IRSs first, then traversing all reflection units again on each IRS, each finding the value ζ of one reflection unit m,k Then update the reflection coefficient matrix on the IRS once>This optimization ends when all reflection units have been traversed.
In this embodiment, the step S5: judging whether the result output by the S4 is converged, if yes, ending, and if not, returning to the S2 for cyclic optimization until the convergence is specifically:
and (3) judging whether the numerical value obtained in the step (4) is converged or not, if the convergence criterion is that whether the front-back variation amplitude of the objective function is smaller than a specific value, converging, and if so, finishing optimization, 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, and S4 needs to optimize all the reflection units on all IRSs in turn 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 all the reflection units are traversed to obtain the result of the end of optimization.
In this embodiment, in S5, it is determined whether the convergence criterion is whether the difference between the objective function and the objective function in the previous iteration process is smaller than a specific value, and if so, it is determined that the convergence is performed.
In an exemplary embodiment, a system for optimizing user association and phase shift in an unmanned aerial vehicle IRS network is further provided, including the optimizing method, where the optimizing system includes:
the initialization network deployment module is used for 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 a user, pre-deploying multiple IRS (inter-radio interference system) to enhance communication, and initializing and setting the transmitting 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, the IRS position and the user position of the unmanned aerial vehicle, decoupling the coupling relation between the user association and the decoding sequence of continuous interference elimination in the pre-deployment network according to the full interference expansion scheme, converting the optimization problem of the user association into a convex optimization problem, and acquiring the association scheme;
the starting point optimization module is used for optimizing the starting point 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 IRS and reflection units on the IRS according to the initial scheme to optimize the reflection coefficient;
and the judging output module is used for judging whether the result output by the reflection coefficient optimizing module is converged or not, if so, ending and outputting the optimizing 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 have 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 at least one instruction is stored in the memories 602, and the at least one instruction is loaded and executed by the processors 601 to implement the above method for optimizing user association and phase shift in an IRS network of an unmanned aerial vehicle.
In an exemplary embodiment, a computer readable storage medium, e.g. a memory comprising instructions executable by a processor in a terminal to perform the above described method of user association and phase shift optimization in an unmanned aerial vehicle IRS network is also provided. For example, the computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
The method and the system for optimizing the user association and the phase shift in the unmanned aerial vehicle IRS network provided by the embodiment of the application are described in detail. The above description of embodiments is only for aiding in understanding the method of the present application and its core ideas; meanwhile, as those skilled in the art will have modifications in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Certain terms are used throughout the description and claims to refer to particular components. Those of skill in the art will appreciate that a hardware manufacturer may refer to the same component by different names. The description and claims do not take the form of an element differentiated by name, but rather by functionality. As referred to throughout the specification and claims, the terms "comprising," including, "and" includes "are intended to be interpreted as" including/comprising, but not limited to. By "substantially" is meant that within an acceptable error range, a person skilled in the art is able to solve the technical problem within a certain error range, substantially achieving the technical effect. The description hereinafter sets forth the preferred embodiment for carrying out the present application, but is not intended to limit the scope of the present application in general, for the purpose of illustrating the general principles of the present application. The scope of the present application is defined by the appended 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 product 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 product or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a commodity or system comprising such elements.
It should be understood that the term "and/or" as used herein is merely one relationship describing the association of the associated objects, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
While the foregoing description illustrates and describes the preferred embodiments of the present application, it is to be understood that this application is not limited to the forms disclosed herein, but is not to be construed as an exclusive use of other embodiments, and is capable of many other combinations, modifications and environments, and adaptations within the scope of the teachings described herein, through the foregoing teachings or through the knowledge or skills of the relevant art. And that modifications and variations which do not depart from the spirit and scope of the present invention are intended to be within the scope of the appended claims.
Claims (5)
1. The user association and phase shift optimization method in the unmanned aerial vehicle IRS network is characterized by comprising 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 a user, 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 IRS and users and optimizing the user association according to the unmanned aerial vehicle transmitting power, the IRS position and the user position in S1, decoupling the coupling relation between the user association and the decoding sequence of continuous interference elimination in the pre-deployment network according to the full interference expansion scheme, converting the optimization problem of the user association into a convex optimization problem, and acquiring the association scheme;
s3: performing initial point optimization of IRS reflection coefficients according to the association scheme in the S2 to obtain an initial scheme;
s4: traversing all IRS and reflection units on the IRS according to the initial scheme in the S3 to optimize the reflection coefficient;
s5: judging whether the result output by the S4 is converged or not, if so, ending, and if not, returning to the S2 for circular optimization until convergence;
the step S2 is specifically as follows:
decoupling the user association and the decoding sequence according to the full interference expansion scheme, converting the user association problem into a convex optimization problem, and solving the problem to obtain the user association scheme, wherein the problem can be expressed as:
C1:
C2:
C3:
wherein,for the expression after using the full interference expansion scheme, n and n' represent users, m represents IRS, < >>The data rate representing user n on IRSm, ex represents the expression after transformation by the full interference expansion scheme, +.>Representing the correlation factor of IRSm and user n, wherein the correlation factor is equal to 1 and represents that the correlation is successful, and the correlation factor is equal to 0 and fails, v m,n Representing a matrix of correlation factors; θ represents the number of users that one IRS can most associate with; p is p m,n Representing the transmit power, < > of user n on IRSm>Representing the channel gain matrix from the unmanned aerial vehicle to IRSm, T representing the transposed matrix, Θ m Representing the reflection coefficient of IRSm, i.e. the phase shift matrix, g m,n Representing the channel gain matrix of IRSm to user n; n 'e N/N denotes that user N' is all user sets except user N, N denotes user set, sigma 2 Representing gaussian noise power;
the starting point scheme of the IRS reflection coefficient in the S3 is obtained by the following formula:
wherein,representing delta such that the objective function f satisfies the variable value at maximum,/>Representing the initialized reflection coefficient of the IRS, delta representing the step size introduced, < >>Indicating when the reflection angle is +.>The data rate of user n on IRSm at the time, ε, represents the approximate gradient, can be expressed as
Wherein iota is 0 A single column vector representing all 1's elements, the number of dimensions of which is equal to the number of reflecting elements on the IRS, N m For the set of all associated users on IRSm, N m,n A set of remaining users on IRSm associated with user n;
the reflection coefficient optimization in S4 can be obtained by the following formula:
wherein ζ m,k Denoted as an auxiliary variable introduced by the kth reflecting unit on IRSm,representing the reflection coefficient matrix, delta, of IRSm after the jth iteration k A kth column of the matrix delta is represented, the matrix delta is a diagonal matrix with the number of main diagonal elements being 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, thus traversing all IRSs first, then traversing all reflection units again on each IRS, each finding the value ζ of one reflection unit m,k Then update the reflection coefficient matrix on the IRS once>Thus, after traversing all the reflecting units, the optimization is finished;
wherein IRS represents the smart reflective surface.
2. The optimization method according to claim 1, wherein the convergence criterion in S5 is that the change amount of the objective function in the two previous and subsequent iterations is smaller than a set threshold, and the convergence is determined, otherwise, the loop iteration is continued without convergence.
3. The optimization method according to claim 1, wherein the unmanned aerial vehicle in S1 plays a role of a base station in a network, and the transmission signal enhancement of the unmanned aerial vehicle is transmitted to a remote user through a plurality of IRSs.
4. The optimization method according to claim 1, wherein initializing content in S1 includes initializing unmanned aerial vehicle transmit power, IRS location, and user location.
5. User association and phase shift optimization system in an unmanned aerial vehicle IRS network, implemented on the basis of the optimization method according to one of the preceding claims 1-4, characterized in that the optimization system comprises:
the initialization network deployment module is used for 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 a user, pre-deploying multiple IRS (inter-radio interference system) to enhance communication, and initializing and setting the transmitting 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, the IRS position and the user position of the unmanned aerial vehicle, decoupling the coupling relation between the user association and the decoding sequence of continuous interference elimination in the pre-deployment network according to the full interference expansion scheme, converting the optimization problem of the user association into a convex optimization problem, and acquiring the association scheme;
the starting point optimization module is used for optimizing the starting point 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 IRS and reflection units on the IRS according to the initial scheme to optimize the reflection coefficient;
and the judging output module is used for judging whether the result output by the reflection coefficient optimizing module is converged or not, if so, ending and outputting the optimizing scheme, and if not, returning to the decoupling module for circular optimization until convergence.
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