CN113726382B - Reconfigurable intelligent surface beamforming matrix generation method and related equipment - Google Patents
Reconfigurable intelligent surface beamforming matrix generation method and related equipment Download PDFInfo
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- H—ELECTRICITY
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- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
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- H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
- H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
- H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
- H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
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- H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
- H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
- H04B7/0842—Weighted combining
- H04B7/086—Weighted combining using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming
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Abstract
The disclosure provides a reconfigurable intelligent surface beamforming matrix generation method and related equipment, wherein the reconfigurable intelligent surface beamforming matrix generation method comprises the following steps: based on a particle swarm algorithm, randomly generating feasible solutions of M particles and speeds corresponding to the feasible solutions in a feasible domain of the reconfigurable intelligent surface, obtaining fitness values of the particles according to the feasible solutions and speeds corresponding to the feasible solutions, obtaining optimal fitness values in particle swarms by updating the feasible solutions of iterative particles and speeds corresponding to the feasible solutions, and finally finding a reconfigurable intelligent surface beamforming matrix corresponding to the optimal fitness values. The method has the advantages that the particle swarm algorithm is used for replacing a part with high complexity in the traditional scheme, the computational complexity is reduced, and meanwhile, the particle swarm algorithm is improved by applying a self-updating parameter method to enhance the accuracy of the proposed beam forming scheme, so that the method can be higher in accuracy, and meanwhile, the complexity of beam forming design is remarkably reduced.
Description
Technical Field
The disclosure relates to the technical field of wireless communication, and in particular relates to a reconfigurable intelligent surface beamforming matrix generation method and related equipment.
Background
In recent years, due to rapid development of radio frequency micro-electro-mechanical systems and wide application of super-surfaces, the concept of controlling wireless channel environment through programmable electromagnetic materials has been proposed and has become a research hotspot in the communication world. This concept is called a smart radio environment, and reconfigurable smart surfaces have emerged as key enabling technologies for implementing a smart radio environment. Specifically, the reconfigurable intelligent surface can be regarded as an infinitely thin two-dimensional plane, and is composed of a large number of scattering elements capable of manipulating electromagnetic waves, and the different elements can independently reflect signals by controlling the amplitude or phase of incident electromagnetic waves, so that directional signal enhancement or attenuation can be cooperatively realized, and the performance of wireless communication can be greatly improved. To achieve this, joint beamforming design is required for the base station and the reconfigurable intelligent surface, however, the difficulty of beamforming design is great due to the mutual coupling between the optimization variables and the unique non-convex constraints of the reconfigurable intelligent surface.
Disclosure of Invention
Accordingly, an object of the present disclosure is to provide a reconfigurable intelligent surface beamforming matrix generating method and related devices.
Based on the above objects, the present disclosure provides a reconfigurable intelligent surface beamforming matrix generation method, including:
randomly generating feasible solutions of M particles and speeds corresponding to the feasible solutions in a feasible domain of the reconfigurable intelligent surface, wherein M is an integer greater than 0;
generating an overall channel matrix according to the feasible solution and the speed corresponding to the feasible solution, and obtaining the fitness value of the particles according to the overall channel matrix;
updating the feasible solutions of the M particles and the speeds corresponding to the feasible solutions;
obtaining a particle swarm optimal fitness value of the whole M particles according to the feasible solutions of the M particles and the speeds corresponding to the feasible solutions;
and responding to the determination that the updated feasible solutions of the M particles and the speeds corresponding to the feasible solutions reach preset conditions, and obtaining a reconfigurable intelligent surface beam forming matrix corresponding to the optimal fitness value of the particle swarm.
Based on the same inventive concept, the present disclosure also provides a beamforming method, including,
performing truncated SVD decomposition on the integral channel matrix to obtain a base station beam forming matrix;
and carrying out beam forming design according to the reconfigurable intelligent surface beam forming matrix obtained in the reconfigurable intelligent surface beam forming matrix generating method and the base station beam forming matrix.
Based on the same inventive concept, the present disclosure further provides a reconfigurable intelligent surface beamforming matrix generating device, including:
an initialization module configured to randomly generate viable solutions of M particles and speeds corresponding to the viable solutions in a reconfigurable intelligent surface viable domain, wherein M is an integer greater than 0;
the computing module is configured to generate an overall channel matrix according to the feasible solution and the speed corresponding to the feasible solution, and obtain the fitness value of the particles according to the overall channel matrix;
an updating module configured to update feasible solutions of the M particles and speeds corresponding to the feasible solutions;
the selection module is configured to obtain a particle swarm optimal fitness value of the whole M particles according to the feasible solutions of the M particles and the speeds corresponding to the feasible solutions;
and the return module is configured to obtain a reconfigurable intelligent surface beamforming matrix corresponding to the particle swarm optimal fitness value in response to determining that the feasible solutions of the M particles are updated and the speeds corresponding to the feasible solutions reach preset conditions.
Based on the same inventive concept, the present disclosure also provides a beam forming apparatus, including: a decomposition module, an implementation module, and a reconfigurable intelligent surface beamforming matrix generation apparatus as described previously, wherein:
the decomposition module is configured to perform truncated SVD decomposition on the whole channel matrix to obtain a base station beam forming matrix;
the implementation module is configured to receive the reconfigurable intelligent surface beamforming matrix obtained by the reconfigurable intelligent surface beamforming matrix generating device and combine with the base station beamforming matrix to perform beamforming design.
Based on the same inventive concept, the present disclosure also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method according to any one of the above when executing the program.
Based on the same inventive concept, the present disclosure also provides a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of the above.
As can be seen from the foregoing, the reconfigurable intelligent surface beamforming matrix generating method and related device provided by the present disclosure randomly generate feasible solutions of M particles and speeds corresponding to the feasible solutions in a feasible domain of a reconfigurable intelligent surface based on a particle swarm algorithm, obtain fitness values of the particles according to the feasible solutions and speeds corresponding to the feasible solutions, obtain optimal fitness values in a particle swarm by updating the feasible solutions of iterative particles and speeds corresponding to the feasible solutions, and finally find a reconfigurable intelligent surface beamforming matrix corresponding to the optimal fitness values. The method has the advantages that the particle swarm algorithm is used for replacing a part with high complexity in the traditional scheme, the computational complexity is reduced, and meanwhile, the particle swarm algorithm is improved by applying a self-updating parameter method to enhance the accuracy of the proposed beam forming scheme, so that the method can be higher in accuracy, and meanwhile, the complexity of beam forming design is remarkably reduced.
Drawings
In order to more clearly illustrate the technical solutions of the present disclosure or related art, the drawings required for the embodiments or related art description will be briefly described below, and it is apparent that the drawings in the following description are only embodiments of the present disclosure, and other drawings may be obtained according to these drawings without inventive effort to those of ordinary skill in the art.
FIG. 1 is a schematic diagram of a reconfigurable intelligent surface-assisted peer-to-peer communication system in accordance with an embodiment of the present disclosure;
FIG. 2 is a flow chart of a reconfigurable intelligent surface beamforming matrix generation method in accordance with an embodiment of the present disclosure;
FIG. 3 is a flowchart of updating the optimal fitness value of a particle swarm according to an embodiment of the present disclosure;
fig. 4 is a flow chart of a beamforming design based on a particle swarm algorithm according to an embodiment of the present disclosure;
fig. 5 is a flow chart of a beamforming method of an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of a reconfigurable intelligent surface beamforming matrix generating apparatus according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of a beamforming device according to an embodiment of the present disclosure;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
For the purposes of promoting an understanding of the principles and advantages of the disclosure, reference will now be made to the embodiments illustrated in the drawings and specific language will be used to describe the same.
It should be noted that unless otherwise defined, technical or scientific terms used in the embodiments of the present disclosure should be given the ordinary meaning as understood by one of ordinary skill in the art to which the present disclosure pertains. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items.
As described in the background section, the difficulty of existing base station and reconfigurable intelligent surface joint beamforming designs is great. The applicant has found in implementing the present disclosure that there are a plurality of beamforming schemes for solving the problem of communication between a single antenna user and a multi-antenna base station, however, for multi-antenna users, there are two methods of beamforming schemes commonly used at present, the first is to solve the problem by using a conventional optimization tool such as convex optimization, and the second is to solve the problem by using machine learning. For traditional tools such as convex optimization, the concept of alternating optimization is generally used to design the beamforming at the base station and the reconfigurable intelligent surface respectively, singular value decomposition and water injection power allocation can be used for the beamforming at the base station to solve, and the similar simplified sub-problem is generally used for the beamforming of the reconfigurable intelligent surface to solve. For methods that utilize machine learning, the beamforming matrix at the base station and reconfigurable intelligent surface is typically obtained by predicting the channel matrix through unsupervised learning.
For traditional tools such as convex optimization, matrix inversion and other complex operations exist in the solving process, so that algorithm complexity is rapidly increased along with the increase of the number of the reconfigurable intelligent surface units, solving time is too long, and optimization solving of the reconfigurable intelligent surface with a large number of units is difficult. Although the algorithm complexity can be reduced to a certain extent by using the machine learning method, a great amount of training overhead is generated at the same time, and the optimization accuracy is reduced for the reconfigurable intelligent surface with a large unit number.
Referring to fig. 1, a schematic diagram of a reconfigurable intelligent surface-assisted point-to-point communication system is shown. Wherein the base station is provided withBy having->Reconfigurable intelligent surface with individual reflection units and having +.>Users of the antennas communicate. Let the base station transmit signal be +.>And the beamforming matrix at the base station is +.>Wherein->. Use->，/>And->Representing from base station to user, from base station to reconfigurable intelligent surface and from reconfigurable, respectivelyConstructing the intelligent surface to user channel, the received signal can be expressed as:
wherein the method comprises the steps ofRepresenting the reflection coefficient at the reconfigurable smart surface, i.e. the beamforming matrix +.>Is zero-mean additive gaussian noise. Defining the overall channel matrix from base station to user as +.>The spectral efficiency of the system is therefore expressed as:
the beamforming problem to be solved can be modeled as follows:
wherein P is max For the maximum transmit power of the base station,v(i) Representing vectorsvFrom the i-th element of the model, it can be obtained by observing the model, and the beam forming is realized due to the non-convex objective function and the non-convex constraint conditionThe design becomes particularly difficult in view of this, the applicant converts the joint beamforming problem into two sub-problems of base station beamforming and reconfigurable smart surface beamforming by means of the idea of alternating optimizations. For the reconfigurable intelligent surface beam forming, specifically, based on a particle swarm algorithm, randomly generating feasible solutions of M particles and speeds corresponding to the feasible solutions in a feasible domain of the reconfigurable intelligent surface, obtaining fitness values of the particles according to the feasible solutions and speeds corresponding to the feasible solutions, obtaining optimal fitness values in particle swarm by updating the feasible solutions of iterative particles and speeds corresponding to the feasible solutions, and finally finding a reconfigurable intelligent surface beam forming matrix corresponding to the optimal fitness values.
Therefore, the method and the device solve the problem by using the particle swarm algorithm, compared with the beam forming method in which the complexity is mainly concentrated at the reconfigurable intelligent surface by using the simple operation of the matrix (because the number of the units of the reconfigurable intelligent surface is more, the processing of the beam forming method and the device is equivalent to complex inversion operation of the matrix with large number of rows and columns) in the traditional method, the complexity is greatly reduced, and meanwhile, the solution precision of the particle swarm algorithm is improved by using the adaptive algorithm parameters.
The technical solutions of one or more embodiments of the present specification are described in detail below by means of specific embodiments.
Referring to fig. 2, a reconfigurable intelligent surface beamforming matrix generation method according to one embodiment of the present disclosure includes the following steps:
step S201, randomly generating feasible solutions of M particles and speeds corresponding to the feasible solutions in a feasible domain of the reconfigurable intelligent surface, wherein M is an integer greater than 0.
In this step, a feasible solution [ x ] of M particles is first randomly generated in the feasible domain] l m (j) M=0, 1,2, …, x denotes the initial position of each particle, superscriptlRepresents the number of current update iterations, M represents the first of M particlesm and j represent the j-th element of the m-th particle. Randomly generating the velocity of each particle [ a ]] l m The range of speeds should also be less than the range of feasible regions.
Step S202, generating an overall channel matrix according to the feasible solution and the speed corresponding to the feasible solution, and obtaining the fitness value of the particles according to the overall channel matrix.
In this step, due toAnd->，/>And->Data (channel matrix from base station to user, base station to reconfigurable smart surface and reconfigurable smart surface to user) are known, +.>Can be obtained according to the feasible solution and the speed corresponding to the feasible solution, thus obtaining the whole channel matrix corresponding to the current feasible solution and the speed corresponding to the feasible solution +.>。
Further, the whole channel matrix is subjected to truncated SVD decomposition, and the beam forming matrix of the base station can be obtained as followsWherein->Is->Left singular value matrix,/, of>The power distribution matrix is a diagonal matrix, and can be obtained through water injection power distribution, so that singular values of the whole channel matrix are obtained. Then, the fitness value of the particle is calculated by the following formula:
wherein,ns is the number of singular values of the overall channel matrix; />White noise power; />And obtaining according to the base station beam forming matrix, and representing the transmitting power allocated to the data stream corresponding to the ith singular value.
Step S203, updating the feasible solutions of the M particles and the speeds corresponding to the feasible solutions.
And step S204, obtaining the particle swarm optimal fitness value of the whole M particles according to the feasible solutions of the M particles and the speeds corresponding to the feasible solutions.
In this embodiment, the feasible solutions of the M particles and the speeds corresponding to the feasible solutions are updated by the following formula:
wherein,representing the speed corresponding to said feasible solution, < >>Representing a feasible solution of the M particles;represents the mth particle;lrepresenting the number of update iterations; p is p best Representing the optimal fitness value of an individual, and comparing the fitness values before and after updating to obtain the optimal fitness value; g best The optimal fitness value of the particle swarm is represented, the optimal fitness value is obtained by comparing individual optimal fitness values of M particles, and when the feasible solution and the speeds corresponding to the feasible solution are not updated yet, the fitness value obtained by the initial feasible solution and the speeds corresponding to the feasible solution is regarded as the optimal fitness value; omega l Is the firstlThe inertia weight of the secondary update, c 1 And c 2 The individual learning factor and the group learning factor, respectively, and the function rand () returns random numbers uniformly distributed between 0 and 1.
Further, after updating the feasible solutions of the M particles and the speeds corresponding to the feasible solutions, in the step S202, the fitness values of the M particles after updating are obtained according to the feasible solutions of the M particles after updating and the speeds corresponding to the feasible solutions, and compared with the fitness values before updating, so as to obtain the individual optimal fitness values of the M particles and the overall particle swarm optimal fitness values. Finally updating the inertia weightWherein->Is the damping coefficient.
Step 205, in response to determining that the updated feasible solutions of the M particles and the speeds corresponding to the feasible solutions reach preset conditions, a reconfigurable intelligent surface beamforming matrix corresponding to the optimal fitness value of the particle swarm is obtained.
It can be seen that, in this embodiment, based on the particle swarm algorithm, feasible solutions of M particles and speeds corresponding to the feasible solutions are randomly generated in a feasible domain of the reconfigurable intelligent surface, fitness values of the particles are obtained according to the feasible solutions and speeds corresponding to the feasible solutions, optimal fitness values in the particle swarm are obtained by updating the feasible solutions of iterative particles and speeds corresponding to the feasible solutions, and finally, a reconfigurable intelligent surface beamforming matrix corresponding to the optimal fitness values is found. Compared with the beam forming with the complexity mainly concentrated at the reconfigurable intelligent surface in the traditional method, complex inversion operation and the like are needed to be carried out on a matrix with large number of rows and columns, the complex part with high complexity in the traditional scheme is replaced by the particle swarm algorithm, the computational complexity is reduced, and meanwhile, the particle swarm algorithm is improved by a method of applying self-updating parameters to enhance the precision of the proposed beam forming scheme, so that the method has higher precision and meanwhile, the complexity of beam forming design is obviously reduced.
As an alternative embodiment, referring to fig. 3, for step S204 in the foregoing embodiment, it may further include the following steps:
step 301, generating an updated overall channel matrix according to the updated feasible solutions of the M particles and the speeds corresponding to the feasible solutions, and obtaining updated fitness values of the M particles according to the updated overall channel matrix;
step S302, comparing the fitness values of the M particles before and after updating to obtain individual optimal fitness values of the M particles respectively;
and step S303, comparing the individual optimal fitness values of the M particles to obtain the particle swarm optimal fitness value of the whole M particles.
It will be appreciated that for pre-update and post-update fitness values, it is possible that the pre-update fitness value is the optimal fitness value, and it is also possible that the post-update fitness value is the optimal fitness value, due to the feasible solution of the particles and the corresponding change in velocity, which is not necessarily better than the pre-update fitness value.
As an alternative embodiment, after step S203 in the foregoing embodiment, it may further include, in response to determining that the feasible solution of the updated particle is not in the feasible domain, randomly re-generating the feasible solution of the updated particle and the velocity of the updated particle in the feasible domain.
In this embodiment, invalid data that is not within the feasible region can be cleared by this step.
Referring to fig. 4, in the present embodiment, the position (feasible solution) and the velocity of each particle are initialized first, then a power distribution matrix is calculated according to SVD decomposition, and the fitness value of each particle is calculated according to the power distribution matrix; and further updating the position and the speed of the particles, judging whether the updated position and the speed of the particles are in a feasible domain, if not, regenerating the position and the speed of the particles, then calculating the fitness value of the updated power distribution matrix and the particles, updating the optimal fitness value of an individual and a particle swarm according to the fitness value before and after updating, and finally updating the parameters of the calculation process for the next round until the termination condition is met, so as to obtain the beam forming matrix of the reconfigurable intelligent surface.
As an optional embodiment, the disclosure further provides a reconfigurable intelligent surface beamforming matrix generating method, including:
initializing: random generation in the feasible domainThe feasible solution [ x ]] l m (j) M=0, 1,2, …, x is taken as the initial position of each particle, the superscript indicates the current iteration number, and the total iteration number is set as +.>And is less than or equal to 0 [ x ]] l m (j) Less than or equal to 2 pi, wherein [ x ]] l m (j) The j-th element of the mth particle in the particle group is represented. Randomly generating the velocity of each particle [ a ]] l m The range of speeds should be less than the range of feasible regions.
Calculating a fitness value: first to channel matrixTruncated SVD decomposition is performed to obtain an optimal precoding matrix at the base station of +.>Wherein->Is->Left singular value matrix,/, of>The power distribution matrix is a diagonal matrix, can be obtained through water injection power distribution, and can calculate the fitness value through the following formula:
wherein,is->Singular values of>White noise power, < >>Representing the transmit power allocated to the i-th data stream. Preserving the optimal fitness value in the particle swarm +.>And the corresponding position thereofSimultaneously preserving the individual optimal fitness value of each particle +.>And the corresponding position +.>。
Updating the particle state: velocity of particlesAnd location update +.>The update formula is as follows:
wherein the method comprises the steps ofRepresenting the current iteration number, +.>Is inertial weight, ++>And->Individual learning factors and group learning factors, respectively, function ≡>Returning random numbers uniformly distributed between 0 and 1.
Updating group-related parameters: by updatedAnd an objective function for solving the fitness value according to the fitness value solving formula and updating +.>And->Update inertial weight +.>Wherein->Is the damping coefficient.
End condition: and judging whether a termination condition is reached, if so, returning to the optimal particle. Otherwise, go to step update particle state.
The final result of the method returns to the optimal valueBase station beamforming matrix corresponding to the value +.>And a beamforming matrix at the reconfigurable smart surface +.>。
It should be noted that the method of the embodiments of the present disclosure may be performed by a single device, such as a computer or a server. The method of the embodiment can also be applied to a distributed scene, and is completed by mutually matching a plurality of devices. In the case of such a distributed scenario, one of the devices may perform only one or more steps of the methods of embodiments of the present disclosure, the devices interacting with each other to accomplish the methods.
It should be noted that the foregoing describes some embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments described above and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
Based on the same inventive concept, corresponding to any of the above embodiments of the reconfigurable intelligent surface beamforming matrix generation method, the present disclosure also provides a beamforming method, including,
performing truncated SVD decomposition on the integral channel matrix to obtain a base station beam forming matrix;
the reconfigurable intelligent surface beamforming matrix and the base station beamforming matrix obtained in the reconfigurable intelligent surface beamforming matrix generation method according to any of the foregoing embodiments are subjected to beamforming design.
As an alternative embodiment, referring to fig. 5, a beamforming method provided by the present disclosure includes:
step S501, performing truncated SVD decomposition on the whole channel matrix to obtain a base station beam forming matrix;
step S502, randomly generating feasible solutions of M particles and speeds corresponding to the feasible solutions in a feasible domain of the reconfigurable intelligent surface, wherein M is an integer greater than 0;
step S503, generating an overall channel matrix according to the feasible solution and the speed corresponding to the feasible solution, and obtaining the fitness value of the particles according to the overall channel matrix;
step S504, updating the feasible solutions of the M particles and the speeds corresponding to the feasible solutions;
step S505, obtaining a particle swarm optimal fitness value of the whole M particles according to the feasible solutions of the M particles and the speeds corresponding to the feasible solutions;
step S506, a reconfigurable intelligent surface beam forming matrix corresponding to the optimal fitness value of the particle swarm is obtained in response to the fact that the feasible solutions of the M particles obtained through updating and the speeds corresponding to the feasible solutions reach preset conditions;
and step S507, carrying out beam forming design according to the reconfigurable intelligent surface beam forming matrix and the base station beam forming matrix.
Based on the same inventive concept, the present disclosure also provides a reconfigurable intelligent surface beamforming matrix generating device corresponding to the method of any embodiment.
Referring to fig. 6, the reconfigurable intelligent surface beamforming matrix generating apparatus includes:
an initialization module 601 configured to randomly generate a feasible solution of M particles and a speed corresponding to the feasible solution in a reconfigurable intelligent surface feasible domain, wherein M is an integer greater than 0;
in this module, first a feasible solution [ x ] of M particles is randomly generated in the feasible domain] l m (j) M=0, 1,2, …, x denotes the initial position of each particle, superscriptlRepresenting the number of current update iterations, M represents the mth element of the mth particle, and j represents the jth element of the mth particle. Randomly generating the velocity of each particle [ a ]] l m The range of speeds should also be less than the range of feasible regions.
A calculation module 602, configured to generate an overall channel matrix according to the feasible solution and the speed corresponding to the feasible solution, and obtain an fitness value of the particle according to the overall channel matrix;
in the present module, due toAnd->，/>Anddata (channel matrix from base station to user, base station to reconfigurable smart surface and reconfigurable smart surface to user) are known, +.>Can be obtained according to the feasible solution and the speed corresponding to the feasible solution, thus obtaining the whole channel matrix corresponding to the current feasible solution and the speed corresponding to the feasible solution +.>。
Further, the whole channel matrix is subjected to truncated SVD decomposition, and the beam forming matrix of the base station can be obtained as followsWherein->Is->Left singular value matrix,/, of>The power distribution matrix is a diagonal matrix, and can be obtained through water injection power distribution, so that singular values of the whole channel matrix are obtained. Then, the fitness value of the particle is calculated by the following formula:
wherein,ns is the number of singular values of the overall channel matrix; />White noise power; />And obtaining according to the base station beam forming matrix, and representing the transmitting power allocated to the data stream corresponding to the ith singular value.
An updating module 603 configured to update the feasible solutions of the M particles and the speeds corresponding to the feasible solutions;
in the module, the feasible solutions of the M particles and the speeds corresponding to the feasible solutions are updated by the following formula:
wherein,representing the speed corresponding to said feasible solution, < >>Representing a feasible solution of the M particles;represents the mth particle;lrepresenting the number of update iterations; p is p best Representing the optimal fitness value of an individual, and comparing the fitness values before and after updating to obtain the optimal fitness value; g best The optimal fitness value of the particle swarm is represented, the optimal fitness value is obtained by comparing individual optimal fitness values of M particles, and when the feasible solution and the speeds corresponding to the feasible solution are not updated yet, the fitness value obtained by the initial feasible solution and the speeds corresponding to the feasible solution is regarded as the optimal fitness value; omega l Is the firstlThe inertia weight of the secondary update, c 1 And c 2 The individual learning factor and the group learning factor, respectively, and the function rand () returns random numbers uniformly distributed between 0 and 1.
A selection module 604 configured to obtain a particle swarm optimal fitness value of the M particles as a whole according to the feasible solutions of the M particles and the speeds corresponding to the feasible solutions;
in the present module, after updating the feasible solutions of the M particles and the speeds corresponding to the feasible solutions, the foregoing step S202 is performed to obtain the fitness values of the M particles after updating according to the feasible solutions of the M particles after updating and the speeds corresponding to the feasible solutions, and compare the fitness values with the fitness values before updating to obtain the individual optimal fitness values and the whole fitness values of the M particlesAnd (3) the particle swarm optimal fitness value of the body. Finally updating the inertia weightWherein->Is the damping coefficient.
And a return module 605 configured to obtain a reconfigurable intelligent surface beamforming matrix corresponding to the particle swarm optimal fitness value in response to determining that the feasible solutions for updating the M particles and the speeds corresponding to the feasible solutions reach a preset condition.
As an alternative embodiment, for the selection module 604 in the foregoing embodiment, it may further include the following units:
the operation unit 6041 is configured to generate an updated overall channel matrix according to the updated feasible solutions of the M particles and the speeds corresponding to the feasible solutions, and obtain updated fitness values of the M particles according to the updated overall channel matrix;
the individual unit 6042 is configured to compare the fitness values of the M particles before and after updating to obtain individual optimal fitness values of the M particles respectively;
the particle swarm unit 6043 is configured to compare the individual optimal fitness values of the M particles, and obtain a particle swarm optimal fitness value of the entire M particles.
As an alternative embodiment, for the update module 603 in the foregoing embodiment, it may further include a correction unit 6031: is configured to re-randomly generate a viable solution of the updated particle and a velocity of the updated particle within a viable domain in response to determining that the viable solution of the updated particle is not within the viable domain.
For convenience of description, the above devices are described as being functionally divided into various modules, respectively. Of course, the functions of the various modules may be implemented in the same one or more pieces of software and/or hardware when implementing the present disclosure.
The device of the foregoing embodiment is configured to implement the corresponding reconfigurable intelligent surface beamforming matrix generation method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described herein.
Based on the same inventive concept, referring to fig. 7, corresponding to the method of any embodiment described above, the present disclosure further provides a beamforming apparatus, including a decomposition module, an implementation module, and a reconfigurable intelligent surface beamforming matrix generating apparatus as described above, wherein:
a decomposition module 701, configured to perform truncated SVD decomposition on the entire channel matrix to obtain a base station beamforming matrix;
an implementation module 703, configured to receive the reconfigurable intelligent surface beamforming matrix obtained by the reconfigurable intelligent surface beamforming matrix generating device 702, and combine with the base station beamforming matrix to perform beamforming design. .
Based on the same inventive concept, the present disclosure also provides an electronic device corresponding to the method of any embodiment, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor, where the processor implements the reconfigurable intelligent surface beamforming matrix generation method or the beamforming method according to any embodiment when executing the program.
Fig. 8 shows a more specific hardware architecture of an electronic device according to this embodiment, where the device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 implement communication connections therebetween within the device via a bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit ), microprocessor, application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc. for executing relevant programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory ), static storage device, dynamic storage device, or the like. Memory 1020 may store an operating system and other application programs, and when the embodiments of the present specification are implemented in software or firmware, the associated program code is stored in memory 1020 and executed by processor 1010.
The input/output interface 1030 is used to connect with an input/output module for inputting and outputting information. The input/output module may be configured as a component in a device (not shown) or may be external to the device to provide corresponding functionality. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various types of sensors, etc., and the output devices may include a display, speaker, vibrator, indicator lights, etc.
Communication interface 1040 is used to connect communication modules (not shown) to enable communication interactions of the present device with other devices. The communication module may implement communication through a wired manner (such as USB, network cable, etc.), or may implement communication through a wireless manner (such as mobile network, WIFI, bluetooth, etc.).
Bus 1050 includes a path for transferring information between components of the device (e.g., processor 1010, memory 1020, input/output interface 1030, and communication interface 1040).
It should be noted that although the above-described device only shows processor 1010, memory 1020, input/output interface 1030, communication interface 1040, and bus 1050, in an implementation, the device may include other components necessary to achieve proper operation. Furthermore, it will be understood by those skilled in the art that the above-described apparatus may include only the components necessary to implement the embodiments of the present description, and not all the components shown in the drawings.
The electronic device of the foregoing embodiment is configured to implement the corresponding reconfigurable intelligent surface beamforming matrix generation method or beamforming method of any of the foregoing embodiments. And has the beneficial effects of the corresponding method embodiments, which are not described in detail herein.
Based on the same inventive concept, corresponding to any of the above embodiments of the method, the present disclosure further provides a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the reconfigurable intelligent surface beamforming matrix generation method or the beamforming method according to any of the above embodiments.
The computer readable media of the present embodiments, including both permanent and non-permanent, removable and non-removable media, may be used to implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device.
The storage medium of the above embodiment stores computer instructions for causing the computer to perform the reconfigurable intelligent surface beamforming matrix generation method or beamforming method as described in any of the above embodiments. And has the beneficial effects of the corresponding method embodiments, which are not described in detail herein.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the disclosure, including the claims, is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined under the idea of the present disclosure, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in details for the sake of brevity.
Additionally, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown within the provided figures, in order to simplify the illustration and discussion, and so as not to obscure the embodiments of the present disclosure. Furthermore, the devices may be shown in block diagram form in order to avoid obscuring the embodiments of the present disclosure, and this also accounts for the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform on which the embodiments of the present disclosure are to be implemented (i.e., such specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the disclosure, it should be apparent to one skilled in the art that embodiments of the disclosure can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative in nature and not as restrictive.
While the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of those embodiments will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.
The disclosed embodiments are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Accordingly, any omissions, modifications, equivalents, improvements, and the like, which are within the spirit and principles of the embodiments of the disclosure, are intended to be included within the scope of the disclosure.
Claims (9)
1. A reconfigurable intelligent surface beamforming matrix generation method, comprising:
randomly generating feasible solutions of M particles and speeds corresponding to the feasible solutions in a feasible domain of the reconfigurable intelligent surface, wherein M is an integer greater than 0;
generating an overall channel matrix according to the feasible solution and the speed corresponding to the feasible solution, and obtaining the fitness value of the particles according to the overall channel matrix;
updating the feasible solutions of the M particles and the speeds corresponding to the feasible solutions;
obtaining a particle swarm optimal fitness value of the whole M particles according to the feasible solutions of the M particles and the speeds corresponding to the feasible solutions;
responding to the determination that the feasible solutions of the M particles obtained by updating and the speeds corresponding to the feasible solutions reach preset conditions, and obtaining a reconfigurable intelligent surface beam forming matrix corresponding to the optimal fitness value of the particle swarm;
wherein, the obtaining the fitness value of the particle according to the overall channel matrix includes:
performing truncated SVD decomposition on the integral channel matrix to obtain a base station beam forming matrix and singular values of the integral channel matrix;
calculating the fitness value of the particles according to the following formula:
wherein,ns is the number of singular values of the overall channel matrix; />White noise power; p is p i And (2) obtaining according to the base station beam forming matrix, wherein the obtained data represents the transmitting power allocated to the data stream corresponding to the ith singular value.
2. The method of claim 1, wherein the obtaining a particle swarm optimal fitness value for the M particles as a whole from the feasible solutions of the M particles and the velocities corresponding to the feasible solutions comprises;
generating an updated overall channel matrix according to the updated feasible solutions of the M particles and the speeds corresponding to the feasible solutions, and obtaining updated fitness values of the M particles according to the updated overall channel matrix;
comparing the fitness values of the M particles before and after updating to obtain individual optimal fitness values of the M particles respectively;
and comparing the individual optimal fitness values of the M particles to obtain the particle swarm optimal fitness value of the whole M particles.
3. The method of claim 2, wherein the feasible solution of the M particles and the velocity corresponding to the feasible solution are updated by:
wherein,representing the speed corresponding to said feasible solution, < >>Representing a feasible solution of the M particles; m=0, 1, …, M, represents the mth particle; l represents the number of updates; g best Representing the optimal fitness value of the population of particles; p is p best Representing the individual optimal fitness value; omega l For the first updated inertial weight, c 1 And c 2 The individual learning factor and the group learning factor, respectively, and the function rand () returns random numbers uniformly distributed between 0 and 1.
4. The method of claim 1, wherein updating the feasible solutions of the M particles and the speeds corresponding to the feasible solutions further comprises;
in response to determining that the viable solution of the updated particle is not within the viable domain, the viable solution of the updated particle and the velocity of the updated particle are re-randomly generated within the viable domain.
5. A method of beam forming, comprising,
performing truncated SVD decomposition on the integral channel matrix to obtain a base station beam forming matrix;
the reconfigurable intelligent surface beamforming matrix and the base station beamforming matrix obtained in the reconfigurable intelligent surface beamforming matrix generation method according to any of claims 1-4 are beamformed.
6. A reconfigurable intelligent surface beamforming matrix generation apparatus, comprising:
an initialization module configured to randomly generate viable solutions of M particles and speeds corresponding to the viable solutions in a reconfigurable intelligent surface viable domain, wherein M is an integer greater than 0;
the computing module is configured to generate an overall channel matrix according to the feasible solution and the speed corresponding to the feasible solution, and obtain the fitness value of the particles according to the overall channel matrix;
an updating module configured to update feasible solutions of the M particles and speeds corresponding to the feasible solutions;
the selection module is configured to obtain a particle swarm optimal fitness value of the whole M particles according to the feasible solutions of the M particles and the speeds corresponding to the feasible solutions;
the return module is configured to obtain a reconfigurable intelligent surface beamforming matrix corresponding to the particle swarm optimal fitness value in response to determining that a feasible solution for updating the M particles and a speed corresponding to the feasible solution reach a preset condition;
wherein, the obtaining the fitness value of the particle according to the overall channel matrix includes:
performing truncated SVD decomposition on the integral channel matrix to obtain a base station beam forming matrix and singular values of the integral channel matrix;
calculating the fitness value of the particles according to the following formula:
wherein,ns is the number of singular values of the overall channel matrix; />White noise power; p is p i And (2) obtaining according to the base station beam forming matrix, wherein the obtained data represents the transmitting power allocated to the data stream corresponding to the ith singular value.
7. A beamforming apparatus comprising: the reconfigurable intelligent surface beamforming matrix generation apparatus of claim 6, the decomposition module, and the implementation module, wherein:
the decomposition module is configured to perform truncated SVD decomposition on the whole channel matrix to obtain a base station beam forming matrix;
the implementation module is configured to receive the reconfigurable intelligent surface beamforming matrix obtained by the reconfigurable intelligent surface beamforming matrix generating device and combine with the base station beamforming matrix to perform beamforming design.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the reconfigurable intelligent surface beamforming matrix generation method of any of claims 1 to 4 or the beamforming method of claim 5 when the program is executed.
9. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the reconfigurable intelligent surface beamforming matrix generation method of any of claims 1 to 4 or the beamforming method of claim 5.
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