CN118971920B - Beam forming method, system and computer readable medium for super surface antenna - Google Patents
Beam forming method, system and computer readable medium for super surface antenna Download PDFInfo
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Abstract
The invention provides a beam forming method of a super-surface antenna, which comprises the steps of S1, constructing a beam forming model of the super-surface antenna, wherein the target in the beam forming model is a state code word corresponding to N antenna units respectively included in the super-surface antenna, the decision variable in the beam forming model is the state code word corresponding to the N antenna units respectively included in the super-surface antenna, the state code word is represented by b bits, S2, solving the beam forming model based on a binary bat algorithm to obtain a corresponding current optimal solution, in the solving process of the binary bat algorithm, individuals in a population are integral code words formed by sequentially arranging the state code words corresponding to the N antenna units respectively, the optimal solution comprises the optimal value of the state code word of each antenna unit, and S3, deploying the super-surface antenna according to the current optimal solution.
Description
Technical Field
The present disclosure relates to the field of wireless communications technologies, and in particular, to a beamforming method, a beamforming system, and a computer readable medium for a super surface antenna.
Background
In order to continuously improve the performance of wireless communication, new information technologies with lower cost, low power consumption and intellectualization are required to be studied. In current communication front-end field research, super surface antenna technology (RIS/Large Intelligent Surface/Reconfigurable Intelligent Surface/Software Defined Surface/Metasurface/IRS/Intelligent Reflecting Surface /Reconfigurable Meta Surfaces/ Holographic MIMO, etc.) is meeting the above requirements. The ultra-surface antenna unit consists of low-cost and low-power-consumption devices, the phase of the unit can be freely regulated and controlled, and the wireless propagation environment can be reconfigured in a software control mode, so that the wireless environment becomes more intelligent.
Super surface antennas offer significant advantages over other existing technologies in many respects. Compared with the traditional phased array antenna, the super-surface antenna does not need any transmitting Radio Frequency (RF) chain, so that the hardware cost and the energy consumption can be greatly reduced. The programmable ultra-surface antenna can also rapidly control the beam direction, electromagnetic waves can be regulated and controlled through the electric signals output by the control circuit, the radiation pattern can be reconstructed, and the beam direction can be regulated and controlled more rapidly and more conveniently according to requirements. Moreover, the super-surface antenna has the advantages of simple manufacturing process, low cost and the like, has the potential of gradually replacing the traditional phased array, and can be used for amplifying the wonderful colors in future engineering and practical application.
Disclosure of Invention
In a first aspect, the present invention provides a beamforming method of a super-surface antenna, where the beamforming includes:
S1, constructing a beam forming model of the super-surface antenna, wherein the target in the beam forming model is a user side cluster and the communication quality index between the super-surface antennas is maximum, decision variables in the beam forming model are state code words respectively corresponding to N antenna units included in the super-surface antenna, the state code words are represented by b bits, b is a positive integer, and the user side cluster comprises at least one user side;
In the binary bat algorithm solving process, individuals in a population are integral code words formed by sequentially arranging state code words corresponding to N antenna units respectively, the fitness of the individuals in the population is the communication quality index between a user end cluster and the super-surface antenna when the integral code word corresponding to the individual is deployed on the super-surface antenna, and the optimal solution comprises the optimal value of the state code words of each antenna unit;
And step S3, deploying the super-surface antenna according to the current optimal solution.
Optionally, the step S2 includes:
Step S201, initializing algorithm parameters of the binary bat algorithm, wherein the algorithm parameters comprise sound wave frequency, pulse emission rate and pulse loudness;
Step S202, generating an initialized population comprising a plurality of individuals, and using the initialized population as an original population corresponding to the 1 st iteration;
Step S203, judging whether the original population corresponding to the current iteration processing meets a preset iteration termination condition;
if the original population corresponding to the current iteration process is judged not to meet the iteration termination condition, executing a step S204, and if the original population corresponding to the current iteration process is judged to meet the iteration termination condition, executing a step S206;
Step S204, according to the sound wave frequency, pulse emission rate and pulse loudness Updating the integral code words of the individuals in the original population corresponding to the iterative processing to obtain the firstThe final updated population corresponding to the multiple iterative processes,And is an integer of the number of the times,The method comprises the steps of setting a preset maximum iteration number;
Step S205, the first The final updated population corresponding to the iterative process is taken as the firstThe original population corresponding to the iteration process is processed, and the current iteration times are comparedPerforming 1 adding treatment to update;
after the end of step S205, step S203 is executed again;
Step S206, output the first And obtaining the current optimal solution by the optimal fitness individuals in the original population of the secondary iteration.
Optionally, the initialized population includes 2 b groups, each group including a individuals, a being a positive integer;
in the initialization population, the first state code words of a individuals in the same group are the same, the first state code words of any two individuals in different groups are different, and the last N-1 state code words of each individual are randomly generated;
In step S204, for the first And in the process of updating the integral code words of the individuals in the original population corresponding to the iterative processing, the first state code word of each individual in the original population corresponding to each iterative processing is kept unchanged.
Optionally, step S204 specifically includes:
step S2041, determining the first based on the following equation The sound wave frequency of each volume in the original population corresponding to the iterative processing is carried out for a plurality of times:
;
Represent the first The first iteration process corresponds to the original populationThe frequency of the sound wave of the individual,And is an integer of the number of the times,AndRespectively representing a preset minimum sonic frequency and maximum sonic frequency,Representing a random number with a value within a range [0,1 ];
step S2042, while maintaining the first of said state codewords for each individual in the original population unchanged, pairs of the first based on the following equation The individuals in the original population corresponding to the iterative processing are updated to obtain the firstThe primary updating population corresponding to the secondary iteration processing:
;
Wherein, Represent the firstThe first iteration process corresponds to the first updating populationIndividual firstThe value of the one bit is taken,And is a positive integer of the number of the components,Represent the firstThe first iteration process corresponds to the original populationIndividual firstThe value of the one bit is taken,Representation pairAs a result of the negation process,Representing a first random number having a value within the range 0,1,Representing a transfer function of a binary location update,Represent the firstThe first iteration process corresponds to the first updating populationIndividual firstThe speed of movement of the bits of a single bit,Represent the firstThe first iteration process corresponds to the first iteration process to update the individuals with optimal fitness in the populationThe value of the individual bits;
Step S2043, while maintaining the first of the status codewords of each individual in the preliminary updated population Each group in the preliminary updating population corresponding to the iterative processing is updated by adopting the following formula to obtain the first groupThe population is further updated corresponding to the iterative processing:
;
Represent the first The iteration process corresponds to further updating the population to be positioned at the first positionWithin the first groupIndividual firstThe value of the one bit is taken,Represent the firstThe first iteration process corresponds to the first updating populationWithin the first groupIndividual firstThe value of the one bit is taken,Represent the firstThe first iteration process corresponds to the first updating populationThe best fitness individuals within the individual group are at the firstThe value of the one bit is taken,Indicating that the value is within the rangeA second random number within the first random number,Representing in advance the firstThe first group is atPulse transmission rates configured during the multiple iteration process,And is a positive integer of the number of the components,And is a positive integer;
Step S2044, for each packet, detect the first The first group is atSecond optimal fitness corresponding to the iterative processWhether or not it is greater than the corresponding first optimal fitnessAnd detection is directed to the firstThe first packet is the first packetThe values generated in the iterative process are in a rangeThird random number inWhether or not to be less than the firstThe first group is atPulse loudness configured in a multiple iteration processFirst optimal fitnessIs the firstThe first iteration process corresponds to the first initial updating populationFitness of the individual with optimal fitness within the group, a second optimal fitnessIs the firstThe iteration process corresponds to further updating the first populationFitness of the optimal fitness individuals within the group;
step S2045, according to the detection result of each packet in step S2044, determining that each packet is in the first packet Pulse emission rate and pulse loudness configured in the process of iterative processing;
Wherein, for the first Grouping, if the firstSecond best fitness corresponding to each packetIs greater than the corresponding first optimal fitnessAnd (1)The first group is atThird random number in iterative processLess than pulse loudnessThen the pulse transmission rate is increasedAnd takes the processing result as the firstThe first group is atPulse transmission rate configured during multiple iteration processAnd reducing pulse loudnessAnd takes the processing result as the firstThe first group is atPulse loudness configured in a multiple iteration process;
For the firstGrouping, if the firstSecond best fitness corresponding to each packetLess than or equal to the corresponding first optimal fitnessOr (b)The first group is atThird random number in iterative processEqual to or greater than pulse loudnessPulse transmission rateAs the firstThe first group is atPulse transmission rate configured during multiple iteration processAnd loudness the pulsesAs the firstThe first group is atConfigured during the process of multiple iterations;
Step S2046, according to the firstFurther updating the population corresponding to the iterative process to determine the firstAnd finally updating the population corresponding to the iterative processing.
Optionally, in step S2045, the pulse transmission rate is increasedAnd takes the processing result as the firstThe first group is atPulse transmission rate configured during multiple iteration processAnd reducing pulse loudnessAnd takes the processing result as the firstThe first group is atPulse loudness configured in a multiple iteration processThe method specifically comprises the following steps:
The following equation determines the first The first group is atPulse transmission rate configured during multiple iteration processSum pulse loudness:
;
Wherein, Representing the initial value of the pulse transmission rate configured in the initialization phase,AndAre all a constant which is set in advance,And is also provided with。
Optionally, step S2046 specifically includes incorporating a third step into the processEach group in the further updated population corresponding to the iterative process is sequentially and respectively used as a target group, and the following steps are adopted for the first groupUpdating the target group in the further updated population corresponding to the iterative processing:
Step S20461 to bring the first Each group of 2 b -1 other groups except the target group in the further updating group corresponding to the iterative processing is used as a target other group respectively, and an equivalent code word corresponding to the optimal fitness individual in the target other group when the optimal fitness individual in the target other group is transferred into the target group is obtained, wherein the equivalent code word refers to an updated integral code word obtained by superposing the same conversion code word on each state code word in the optimal fitness individual in the target other group, and the conversion code word is equal to the difference between the first state code word of the individual in the target group and the first state code word of the optimal fitness individual in the target other group;
step S20462, the first And further updating the optimal fitness individuals in the target group in the population corresponding to the iterative processing, taking each of 2 b -1 optimal fitness individuals corresponding to 2 b -1 other groups as the target other optimal fitness individuals as an initial temporary optimal fitness individual in the target group, and sequentially performing the following processing:
Judging whether the fitness of other target optimal fitness individuals is greater than the fitness of the target grouping temporary optimal fitness individuals or not;
If the fitness of the target other optimal fitness individuals is judged to be greater than the fitness of the target grouping temporary optimal fitness individuals, randomly covering the whole code word of the target grouping temporary optimal fitness individuals with one other whole code word in the target grouping, covering the whole code word of the target other optimal fitness individuals with the equivalent code word of the target other optimal fitness individuals with the temporary optimal fitness individuals, and taking the individual corresponding to the equivalent code word of the target other optimal fitness individuals in the target grouping as a new temporary optimal fitness individual of the target grouping;
If the fitness of the target other optimal fitness individuals is judged to be smaller than or equal to the fitness of the target grouping temporary optimal fitness individuals, randomly covering the whole code words of one other individual except the temporary optimal fitness individuals in the target grouping with the equivalent code words of the target other optimal fitness individuals, wherein the temporary optimal fitness individuals in the target grouping are kept unchanged;
the population obtained after the end of step S2046 is used as the first And finally updating the population corresponding to the iterative processing.
Optionally, the value range of a is more than or equal to 8.
Optionally, the communication quality index is:
The sum of communication rates between each user terminal and the super-surface antenna in the user terminal cluster;
Or the weighted summation of the communication rate between each user end and the super-surface antenna in the user end cluster;
Or the minimum value in the communication rate between each user end and the super-surface antenna in the user end cluster;
or the weighted summation of the signal-to-interference-and-noise ratio of the communication between each user terminal and the super-surface antenna in the user terminal cluster;
Or a weighted summation of the reference information received powers communicated between each user side and the super surface antenna in the user side cluster.
In a second aspect, an embodiment of the present disclosure provides a beamforming system of a super-surface antenna, the beamforming system being configured to enable the beamforming method as provided in the first aspect, the beamforming system comprising:
The system comprises a construction module, a beam forming module and a processing module, wherein the construction module is configured to construct a beam forming model of the super-surface antenna, a target in the beam forming model is a state code word which is the largest in communication quality index between a user side cluster and the super-surface antenna, decision variables in the beam forming model are N antenna units respectively corresponding to the super-surface antenna, the state code word is represented by b bits, b is a positive integer, and the user side cluster comprises at least one user side;
In the binary bat algorithm solving process, the individuals in the population are integral code words formed by arranging N state code words corresponding to the antenna units in sequence, the fitness of the individuals in the population is the communication quality index between a user side cluster and the super-surface antenna when the integral code word corresponding to the individual is deployed on the super-surface antenna, and the optimal solution comprises the optimal value of the state code words of each antenna unit;
and the deployment module is configured to deploy the super-surface antenna according to the current optimal solution.
In a third aspect, the disclosed embodiments also provide a computer readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps in the beamforming method as provided in the first aspect.
Drawings
FIG. 1 is a schematic diagram of a structure of a subsurface antenna assisted multi-user wireless communication system;
FIG. 2 is a schematic diagram of channel modeling for a subsurface antenna;
fig. 3 is a flowchart of a beamforming method of a super-surface antenna according to an embodiment of the present disclosure;
FIG. 4 is a flowchart of an alternative implementation method of step S2 in an embodiment of the disclosure;
FIG. 5 is a flow chart of an alternative implementation of step S204 in an embodiment of the present disclosure;
FIG. 6 is a schematic modeling diagram of the simulation performed in the present disclosure;
FIG. 7 is a schematic diagram of the definition of spatial angles in a simulation;
FIG. 8 is a comparative schematic of simulation and rate change curves under scenario 1 of the present disclosure;
FIG. 9 is a front view of a simulated 3D pattern under scenario 1 of the present disclosure;
FIG. 10 is a top view of a simulated 3D pattern under scenario 1 of the present disclosure;
FIGS. 11 and 12 are 2D tangential plane patterns of two simulated beams in scenario 1 of the present disclosure;
FIG. 13 is a comparative schematic of simulation and rate change curves under scenario 2 of the present disclosure;
FIG. 14 is a front view of a simulated 3D pattern under scenario 2 of the present disclosure;
FIG. 15 is a top view of a simulated 3D pattern under scenario 2 of the present disclosure;
FIGS. 16 and 17 are 2D tangential plane patterns of two simulated beams in scenario 2 of the present disclosure;
Fig. 18 is a block diagram of a beamforming system of a super-surface antenna according to an embodiment of the present disclosure;
Fig. 19 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
In order that those skilled in the art will better understand the technical solutions of the present disclosure, the present disclosure will be described in further detail with reference to the accompanying drawings and detailed description.
Unless defined otherwise, technical or scientific terms used in this disclosure should be given the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure belongs. The terms "first," "second," and the like, as used in this disclosure, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. Likewise, the terms "a," "an," or "the" and similar terms do not denote a limitation of quantity, but rather denote the presence of at least one. 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.
Like elements are denoted by like reference numerals throughout the various figures. For purposes of clarity, the various features of the drawings are not drawn to scale. Furthermore, some well-known portions may not be shown in the drawings.
Numerous specific details of the disclosure are set forth below in order to provide a more thorough understanding of the disclosure. However, as will be understood by those skilled in the art, the present disclosure may be practiced without these specific details.
Fig. 1 is a schematic diagram of a structure of a super-surface antenna assisted multi-user wireless communication system. As shown in fig. 1, in practical application, the role of the super-surface antenna in the communication system can be divided according to purposes, and mainly, the interference suppression at the receiving end and the beamforming at the transmitting end can be realized, while the invention mainly focuses on the application of the super-surface antenna as the beamforming of the transmitting antenna.
In the beam forming research, the ultra-surface antenna auxiliary multi-user wireless communication system can be firstly analyzed and modeled, as shown in figure 1, wherein the ultra-surface antenna units are uniformly distributed in rows and columns, and the number of the rows is the number of the rowsThe column number isBy the following constitutionThe antenna units form an array, and the vertical spacing between the units isThe horizontal distance isIs positioned at the firstLine 1The phase shift of the antenna elements of a column is。
Assuming that the number of phase quantization bits of the antenna unit is b (b is a positive integer), the super-surface antenna arrayThe phase shift matrix of (a) may be expressed as follows:
;
Wherein the phase shift value of each antenna unit is in the b-bit quantization interval, so the full search space for the whole antenna array is as follows . To control the beam direction of the transmitted electromagnetic wave, it is the phase shift of each antenna element that is determined by the phase shift matrix of the super-surface antenna array。
Fig. 2 is a schematic diagram of channel modeling for a subsurface antenna. As shown in fig. 2, in terms of channel modeling, the channel between the subsurface antenna and the kth user is described as using a far field model of communicationAnd only Los direct paths are considered here, the subsurface antenna communication model is analyzed in a spatial coordinate system for ease of description.
The ultra-surface antenna array units are uniformly distributed, an array of N z rows and N y columns is placed on a yoz plane, and the origin of coordinates is arranged at the position of the lower left corner unit of the array.For the spacing between adjacent 2 cells (the vertical distance and the horizontal distance are equal),To orient the vector in azimuth relative to the hypersurface (the angle between the projection of the steering vector on the xoy plane and the x-axis),For the pitch angle of the steering vector relative to the hypersurface (the angle of the steering vector to the z axis),Is the wavelength of electromagnetic waves. The steering vector of the super-surface antenna at point aExpressed as:
;
channel matrix Can be expressed as follows:
;
Wherein, Representing the path loss coefficient.
Further, a signal-to-interference-and-noise ratio (SINR) of the user terminal can be obtainedAnd total rate of communication:
The problem P1 to be solved by the present invention at this time can be described as follows:
;
The objective function and constraint are both non-convex for the problem P1 and the channel information is unknown, so it is very difficult to solve this problem. For such problems with discrete constraints, researchers have proposed relaxing the constraints to a continuous space for solving and quantifying the resulting phase results onto a discrete domain, and in particular to the solution of the problem, a method of fractional programming can be employed. Researchers have proposed an iterative algorithm based on Lagrangian dual transformation and quadratic transformation by converting the original problem into several convex problems and deriving a closed solution for each sub-problem. The method has higher computational complexity, namely And the performance of the result obtained by solving is greatly reduced after quantization.
In addition, the precondition of using the above solution is that channel estimation is required, and subsequent calculation can be performed after obtaining the channel information. The current channel estimation method mainly comprises blind estimation and non-blind estimation, wherein the blind estimation refers to that the pilot frequency information is not used, and the estimated value of the channel is obtained through some information processing technologies. Therefore, the currently mainstream channel estimation method is still a traditional pilot frequency-based non-blind estimation method, and the method needs to process information of a baseband obtained through demodulation, but the super-surface antenna has no information processing capability and only has one radio frequency chain, so that the channel estimation is very difficult and difficult to realize, and the technology is difficult to practically land.
In addition to this, researchers have focused on direct optimization of discrete phases and proposed some beamforming algorithms based on discrete phases and not requiring channel information. The simplest method is to traverse the phase state combinations of all antenna units, calculate the received interference-to-noise ratio and sum rate of the user terminal corresponding to each phase matrix, and compare the received interference-to-noise ratio and sum rate to obtain the antenna unit phase state matrix with the largest sum rate. This approach, while simple, has a temporal complexity ofThe time overhead is large and the complexity grows exponentially with the number of units and is therefore not applicable to larger scale arrays. Researchers have also proposed a random maximum sampling method and a sample mean method based on the idea of mathematical statistics, and the method does not need channel information, but needs a large amount of sampling to ensure that a better result is obtained, and has larger randomness and unstable performance from the aspect of the result.
In summary, it is found that in the related art, in the research of beam forming of the super-surface antenna, one scheme is to estimate a channel first, perform mathematical operation on the basis of the existing channel, and have higher calculation complexity and higher system implementation difficulty, and the other method is a blind beam forming method based on discrete phase, which has the problems of higher time complexity and poorer performance.
In order to effectively improve and even fully solve at least one technical problem existing in the related art described above, the present disclosure provides a beamforming method, system and computer-readable medium of a super-surface antenna, which will be described below with reference to specific examples.
Fig. 3 is a flowchart of a beamforming method of a super-surface antenna according to an embodiment of the present disclosure. As shown in fig. 3, the beamforming includes:
S1, constructing a beam forming model of the super-surface antenna.
The objective in the beam forming model is that the communication quality index between the user side cluster and the super-surface antenna is the largest. The decision variables in the beam forming model are state code words respectively corresponding to N antenna units included in the super-surface antenna, and the state code words are represented by b bits, wherein b is a positive integer. The user terminal cluster comprises at least one user terminal, and wireless communication can be carried out between the user terminal and the super-surface antenna in a Bluetooth mode, a WIFI mode and the like.
It should be noted that, the communication quality index in the present disclosure is an index for reflecting the communication quality between the entire ue cluster and the super-surface antenna, and the larger the value, the better the communication quality.
And S2, solving a beam forming model based on a binary bat algorithm to obtain a corresponding current optimal solution.
In the binary bat algorithm solving process, individuals in the population are integral code words formed by sequentially arranging the state code words corresponding to the N antenna units respectively, and the optimal solution comprises the optimal value of the state code words of each antenna unit.
Bat Algorithm (BA for short) is a heuristic Algorithm for global optimization inspired by Bat echo positioning behavior, which has more superior performance than other heuristic algorithms (such as genetic Algorithm, particle swarm Algorithm, etc.). The bat algorithm has good convergence and global searching capability, and shows certain advantages in solving the high-dimensional complex problem. Therefore, the scheme provided by the invention is developed on the basis of a binary bat algorithm, and the original problem of maximum sum rate is converted into the problem of N-dimensional maximum search.
In the embodiment of the disclosure, the fitness of the individuals in the population is a communication quality index between the user side cluster and the super-surface antenna when the integral code word corresponding to the individual is deployed on the super-surface antenna. That is, the corresponding fitness function may be preset based on the targets in the beamforming model.
In some embodiments, the quality of communication between the ue cluster and the super-surface antenna may be one of the following parameters:
1) The communication quality index is the sum of communication rates between each user terminal and the super-surface antenna in the user terminal cluster.
2) The communication quality index is a weighted summation of communication rates between each user side and the super surface antennas in the user side cluster.
3) The communication quality index is the minimum value of communication rates between each user terminal and the super-surface antenna in the user terminal cluster.
4) The communication quality index is a weighted sum of signal-to-interference-and-noise ratios (Signal to Interference plus Noise Ratio, abbreviated SINR) of the communications between each user side and the super-surface antennas in the user side cluster.
5) The communication quality index is a weighted sum of reference information received powers (REFERENCE SIGNAL RECEIVING Power, RSRP) of communication between each user terminal and the super-surface antenna in the user terminal cluster.
In some embodiments, the fitness of any individual in the population is determined based on the steps of firstly, disposing an integral code word corresponding to the individual on a super-surface antenna (the integral code word records a status code word corresponding to each antenna unit, and the phase of the corresponding antenna unit can be determined based on the status code word), then, receiving feedback information sent by each user terminal respectively, wherein the feedback information comprises a signal-to-interference-and-noise ratio and/or reference information receiving power of communication between the corresponding user terminal and the super-surface antenna, and then, determining the fitness corresponding to the individual according to the feedback information fed back by all the user terminals.
When the communication quality index between the user terminal cluster and the super-surface antenna is shown in any one of the above listed (1) - (3), the corresponding communication rate may be determined based on the signal-to-interference-and-noise ratio between the user terminal and the super-surface antenna, and then the sum of the communication rates is calculated, or the weighted sum of the communication rates is calculated, or the minimum value of the communication rates is selected.
Wherein, the firstCommunication rate of communication between individual clients and a subsurface antenna:
;
And is a positive integer of the number of the components,Indicating the number of all clients,Represent the firstSignal-to-interference-and-noise ratio of communication between the individual user terminal and the super-surface antenna.
Taking the communication quality index as the sum of the communication rates between each user side and the super-surface antenna in the user side cluster as an example, the sum of the communication rates at this time:
;
Of course, the above-described five communication quality indicators are only some of the alternative embodiments in the present disclosure, and do not limit the technical solutions of the present disclosure. In practical applications, the corresponding parameters can be preselected or designed according to practical needs to be used as the communication quality index, for example, the communication quality index can also be designed based on the wireless signal strength between the user terminal and the super-surface antenna. And are not illustrated here.
And step S3, deploying the super-surface antenna according to the current optimal solution.
The proposal provided by the invention is mainly based on a heuristic algorithm such as bat algorithm, solves the problem of how to determine the phase matrix of the antenna unit when the super-surface antenna assists multi-user communication, and can be better applied to beam forming of the super-surface antenna multi-user. Compared with the traditional discrete array exhaustive search algorithm, the time complexity is greatly reduced from the originalIs reduced toPopulation size and maximum number of iterations, respectively). In addition, the scheme does not need channel estimation, has simple flow and high universality, and therefore has good practical value.
Fig. 4 is a flowchart of an alternative implementation method of step S2 in an embodiment of the disclosure. As shown in fig. 4, in some embodiments, step S2 includes:
step S201, initializing algorithm parameters of a binary bat algorithm.
The algorithm parameters comprise sound wave frequency, pulse emission rate and pulse loudness.
In step S201, the range of the acoustic wave frequency, the initial value of the pulse emission rate, and the initial value of the pulse loudness may be set accordingly for use in the subsequent iterative process. Of course, in step S201, the maximum iteration number in the subsequent iteration process and the number of individuals included in the population may also be set.
Step S202, generating an initialized population comprising a plurality of individuals, and using the initialized population as an original population corresponding to the 1 st iteration.
Step S203, judging whether the original population corresponding to the current iteration process meets the preset iteration termination condition.
If it is determined that the original population corresponding to the current iteration process does not satisfy the iteration termination condition, step S204 is executed, and if it is determined that the original population corresponding to the current iteration process satisfies the iteration termination condition, step S206 is executed.
In practical application, the iteration termination condition can be preset according to practical requirements, for example, when the original population of the current iteration treatment meets at least one of the following iteration termination conditions, the iteration process is ended:
1) Current iteration number Greater than a preset maximum number of iterations。
2) The fitness value of the optimal fitness individual in the original population processed by the current iteration is larger than or equal to a preset fitness threshold value.
In the present disclosure, the most suitable individual in the population refers to an individual having the greatest fitness value in the whole population, and the most suitable individual in the group (see later) refers to an individual having the greatest fitness value in the whole group.
Step S204, according to the sound wave frequency, pulse emission rate and pulse loudnessUpdating the integral code words of the individuals in the original population corresponding to the iterative processing to obtain the firstThe final updated population corresponding to the multiple iterative processes,And is an integer of the number of the times,Is the preset maximum iteration number.
Step S205, the firstThe final updated population corresponding to the iterative process is taken as the firstThe original population corresponding to the iteration process is processed, and the current iteration times are comparedThe add 1 process is performed to perform the update.
After step S205 ends, step S203 is executed.
Step S206, output the firstAnd obtaining the current optimal solution by the optimal fitness individuals in the original population of the secondary iteration.
The state codeword corresponding to each antenna unit is recorded in the overall codeword corresponding to the individual optimal fitness, and can be used as an optimal solution for the subsequent step S3 to deploy the super-surface antenna.
Optionally, the initialization population comprises 2 b groupings, each grouping comprising a individuals, a being a positive integer. In the initialization population, the first state code words of a individuals in the same group are the same, the first state code words of any two individuals in different groups are different, and the last N-1 state code words of each individual are randomly generated.
In step S204, for the firstIn the process of updating the integral code words of the individuals in the original population corresponding to the iterative processing, the first state code words of the individuals in the original population corresponding to each iterative processing are kept unchanged.
In some embodiments, a has a value in the range of a.gtoreq.8, e.g., a has a value of 8, where the number of individuals in the population is 8*2 b. The larger the value of a, the better the result is obtained relatively, but the longer the algorithm is run. For this reason, the value of a may be set and adjusted in advance according to actual needs in the present disclosure.
The value of b is set according to the phase adjustment accuracy of the antenna unit. In general, the larger the value of b is, the higher the phase adjustment accuracy for the antenna element is.
Fig. 5 is a flow chart of an alternative implementation of step S204 in an embodiment of the present disclosure. As shown in fig. 5, in some embodiments, step S204 specifically includes:
step S2041, determining the first based on the following equation The sound wave frequency of each volume in the original population corresponding to the iterative processing is carried out for a plurality of times:
;
Represent the first The first iteration process corresponds to the original populationThe frequency of the sound wave of the individual,And is an integer of the number of the times,AndRespectively representing a preset minimum sonic frequency and maximum sonic frequency,The random number with the value within the range of 0,1 is represented.
Step S2042, while maintaining the first status codeword of each individual in the original population unchanged, pairs of the first status codewords based on the following equationThe individuals in the original population corresponding to the iterative processing are updated to obtain the firstThe primary updating population corresponding to the secondary iteration processing:
;
Wherein, Represent the firstThe first iteration process corresponds to the first updating populationIndividual firstThe value of the one bit is taken,And is a positive integer of the number of the components,Represent the firstThe first iteration process corresponds to the original populationIndividual firstThe value of the one bit is taken,Representation pairAs a result of the negation process,Representing a first random number having a value within the range 0,1,Representing a transfer function of a binary location update,Represent the firstThe first iteration process corresponds to the first updating populationIndividual firstThe speed of movement of the bits of a single bit,Represent the firstThe first iteration process corresponds to the first iteration process to update the individuals with optimal fitness in the populationThe value of the individual bits.
Step S2043, under the condition of maintaining the first status code word of each individual in the initial updating population unchangedEach group in the preliminary updating population corresponding to the iterative processing is updated by adopting the following formula to obtain the first groupThe population is further updated corresponding to the iterative process.
;
Represent the firstThe iteration process corresponds to further updating the population to be positioned at the first positionWithin the first groupIndividual firstThe value of the one bit is taken,Represent the firstThe first iteration process corresponds to the first updating populationWithin the first groupIndividual firstThe value of the one bit is taken,Represent the firstThe first iteration process corresponds to the first updating populationThe best fitness individuals within the individual group are at the firstThe value of the one bit is taken,Indicating that the value is within the rangeA second random number within the first random number,Representing in advance the firstThe first group is atPulse transmission rates configured during the multiple iteration process,And is a positive integer of the number of the components,And is a positive integer.
Step S2044, for each packet, detect the firstThe first group is atSecond optimal fitness corresponding to the iterative processWhether or not it is greater than the corresponding first optimal fitnessAnd detection is directed to the firstThe first group is atThe values generated in the iterative process are in a rangeThird random number inWhether or not to be less than the firstThe first group is atPulse loudness configured in a multiple iteration process。
Wherein the first optimum fitnessIs the firstThe first iteration process corresponds to the first initial updating populationFitness of the individual with optimal fitness within the group, a second optimal fitnessIs the firstThe iteration process corresponds to further updating the first populationFitness of the best fitness individuals within the group.
Step S2045, according to the detection result of each packet in step S2044, determining that each packet is in the first packetPulse emission rate and pulse loudness configured during the multiple iteration process.
Wherein, for the firstGrouping, if the firstSecond best fitness corresponding to each packetIs greater than the corresponding first optimal fitnessAnd (1)The first group is atThird random number in iterative processLess than pulse loudnessThen the pulse transmission rate is increasedAnd takes the processing result as the firstThe first group is atPulse transmission rate configured during multiple iteration processAnd reducing pulse loudnessAnd takes the processing result as the firstThe first group is atPulse loudness configured in a multiple iteration process。
In some embodiments, the adjustments to the pulse firing rate and the pulse loudness may be implemented based on the following equation, the followingThe first group is atPulse transmission rate configured during multiple iteration processSum pulse loudness:
;
Wherein, Representing the initial value of the pulse transmission rate configured in the initialization phase,AndAre all a constant which is set in advance,And is also provided with。
For the firstGrouping, if the firstSecond best fitness corresponding to each packetLess than or equal to the corresponding first optimal fitnessOr (b)The first group is atThird random number in iterative processEqual to or greater than pulse loudnessPulse transmission rateAs the firstThe first group is atPulse transmission rate configured during multiple iteration processAnd loudness the pulsesAs the firstThe first group is atConfigured during the process of multiple iterations。
That is, whenAnd correspondingly generates a third random numberWhen this occurs, the pulse transmission rate of the corresponding packet is increased and the pulse loudness is decreased. When (when)Or isWhen the pulse transmission rate of the corresponding packet is maintained and the pulse loudness is reduced.
Step S2046, according to the firstFurther updating the population corresponding to the iterative process to determine the firstAnd finally updating the population corresponding to the iterative processing.
In some embodiments, the firstThe further updated population corresponding to the iterative process is directly used as the firstAnd finally updating the population corresponding to the iterative processing.
In other embodiments, the first and second stepsThe further updated population corresponding to the iterative process is subjected to further updating process (for example, a cross process, a mutation process, a selection process and the like), and the processing result is used as a final updated population.
As an alternative example, step S2046 specifically includes incorporating a third phase into the processEach group in the further updated population corresponding to the iterative process is sequentially and respectively used as a target group, and the following steps S20461-S20462 are adopted for the first groupUpdating the target group in the further updated population corresponding to the iterative processing:
Step S20461 to bring the first And (3) further updating each group of 2 b -1 other groups except the target group in the group corresponding to the iterative processing, respectively serving as the target other groups, and acquiring equivalent code words corresponding to the optimal fitness individuals in the target other groups when transferring to the target group, wherein the equivalent code words refer to updated whole code words obtained by superposing the same conversion code words on each state code word in the optimal fitness individuals in the target other groups, and the conversion code words are equal to the difference between the first state code words of the individuals in the target group and the first state code words of the optimal fitness individuals in the target other groups.
It should be noted that, for the antenna element array, if the status codewords corresponding to the same antenna element in the two whole codewords have the same difference value (i.e., the transformed codeword in the present disclosure), it means that when the two whole codewords are respectively deployed in the antenna array, the phase of each antenna element is different by the same phase, and the sum rate obtained by calculation according to the fitness function (i.e., the sum of communication rates, also referred to as "sum rate") is the same. That is, the individual's state codeword has the same fitness as its corresponding equivalent code.
Step S20462, the firstFurther updating optimal fitness individuals in the target group in the population corresponding to the iterative processing, taking the optimal fitness individuals as initial temporary optimal fitness individuals in the target group, taking each of 2 b -1 optimal fitness individuals corresponding to 2 b -1 other groups as target other optimal fitness individuals, and sequentially performing the following processing:
and judging whether the fitness of other target optimal fitness individuals is larger than the fitness of the target grouping temporary optimal fitness individuals.
If the fitness of the target other optimal fitness individuals is judged to be greater than the fitness of the target grouping temporary optimal fitness individuals, randomly covering the whole code word of the target grouping temporary optimal fitness individuals with one other whole code word in the target grouping, covering the whole code word of the target other optimal fitness individuals with the equivalent code word of the target other optimal fitness individuals, and taking the individual corresponding to the equivalent code word of the target other optimal fitness individuals in the target grouping as a new temporary optimal fitness individual of the target grouping;
and if the fitness of the target other optimal fitness individuals is less than or equal to the fitness of the target grouping temporary optimal fitness individuals, the equivalent code words of the target other optimal fitness individuals are randomly covered with the integral code words of one other individuals except the temporary optimal fitness individuals in the target grouping, and the temporary optimal fitness individuals in the target grouping are kept unchanged.
The population obtained after the end of step S2046 is used as the firstAnd finally updating the population corresponding to the iterative processing.
The technical scheme of the invention is improved on the basis of a binary bat algorithm, wherein the first is the adjustment of algorithm parameters, the optimization of parameters is carried out according to the problems presented by the specific scene of the current super-surface antenna, so that the algorithm is more in line with the solving of the problems, and the second is the modification of the algorithm by combining the characteristics of the super-surface antenna. Because each unit of the super-surface antenna array cannot influence the final result after overlapping the same phase, in the scheme of the invention, the population is divided into 2 b groups, each group is independently updated in an iteration mode, and the optimal code words in different groups are added into other groups through the concept of equivalent code words, so that the diversity of individuals is enhanced, the situation that the algorithm converges in advance is prevented, the iteration result among different groups is shared, and the final performance is improved.
In order to verify the effect of the technical scheme of the invention, the technical scheme of the invention is simulated. Fig. 6 is a schematic modeling diagram of the simulation performed in the present disclosure. Fig. 7 is a schematic diagram of the definition of the spatial angle in the simulation. As shown in fig. 6 and 7, the simulation parameters are as follows:
Super-surface antenna parameters, number of rows Number of columnsTotal number of antenna elementsThe antenna units in the array are uniformly distributed in rows and columns, and the row spacing of the antenna unitsColumn spacing of antenna elements。
In the space coordinate system, the antenna is placed at (0, 0) (taking the lower left corner unit of the antenna unit as a reference point), the unit length is 1m, the super-surface antenna array is placed on a yoz plane, the projection of each user on the horizontal plane is distributed on a circle with the radius of 4m at (5, 1, 0) on the horizontal plane, and the height of the user is 0-4 m.
Transmitted signal frequency。
Channel model-far field model.
Noise level-noise power is-20 dBm.
Antenna element parameters 2bit quantization.
The number of users is 2.
Algorithm related parameters, population size 32, maximum iteration number 50, acoustic wave frequency F range 0,2, initial value of pulse emission rate 0.1, initial value of pulse loudness 1.5, pulse emission rate and parameters involved in the process of updating pulse loudnessThe value is 0.1,The value is 0.8.
In simulation, in order to verify the performance of the algorithm provided by the invention, a beam forming algorithm based on fractional programming (Fractional Programing, FP) is designed as a comparison scheme, and the scheme is researched and discussed more on the intelligent super-surface multi-user beam forming problem at present, but calculation iteration (assuming that the channel is known in simulation and an accurate channel is directly used) is carried out after channel information is acquired, and when the method is applied to an antenna unit with discrete phase, the influence of the process of quantifying the closest point projection (Nearest Point Projection, NPP) on the performance of the antenna unit is larger. In contrast, the algorithm provided by the invention does not need channel estimation, and has better performance on a super-surface antenna array with discrete phases.
Due to algorithm limitations in electromagnetic simulation software, azimuth angles specified in the softwareIs the angle between the beam and the x-axis (normal to the plane of the antenna), pitch angleIs the angle between the projection of the beam onto the yoz plane (the plane of the antenna) and the z-axis. Referring to fig. 7, the angle definition is inconsistent with the previous channel calculation, so for convenience of comparison, the direction angle and pitch angle in the following simulation data are calculated according to the angle defined in the simulation software.
Fig. 8 is a comparative schematic of simulation and rate change curves under scenario 1 of the present disclosure. Fig. 9 is a front view of a simulated 3D pattern under scenario 1 in the present disclosure. Fig. 10 is a top view of a simulated 3D pattern under scenario 1 in the present disclosure. Fig. 11 and 12 are 2D tangential plane patterns of two simulation beams in scene 1 in the present disclosure. Fig. 13 is a comparative schematic of simulation and rate change curves under scenario 2 in the present disclosure. Fig. 14 is a front view of a simulated 3D pattern under scenario 2 in the present disclosure. Fig. 15 is a top view of a simulated 3D pattern under scenario 2 in the present disclosure. Fig. 16 and 17 are 2D tangential plane patterns of two simulation beams in scene 2 in the present disclosure. As shown in fig. 8 to 17, 2 groups of scenes are set for simulation (parameters see table 1 and table 2 below), and since the algorithm provided by the invention has randomness in the selection of initial values, for convenience of comparison, a sum rate (the communication quality index is the sum of communication rates between each user side and the super-surface antenna in the user side cluster) change curve is drawn by taking the average value of 10 running results, and the pattern of the antenna array is simulated in electromagnetic simulation software.
TABLE 1 scene 1 simulation parameter table
TABLE 2 Scenario 2 simulation parameter Table
Referring to fig. 8, in the performance of multi-user and rate optimization, the proposed algorithm is improved by about 2bps/Hz after 50 iterations and rate, by about 15.7% on the basis of the comparison scheme, compared to the beamforming algorithm based on the fractional programming.
Referring to fig. 9 and 10, it is apparent from the 3D pattern of the scene 1 simulation experiment that there are two stronger beam main lobes, i.e., the two beam directions identified by arrows in the figure.
Referring to fig. 11 and 12, in order to verify the direction of the beam main lobe, the section processing was performed at the peak of the 3D pattern in the simulation, and the directions of the two beam main lobes (in terms ofExpressed) as The deviation is smaller and the directions coincide than the angles preset in table 1.
Referring to fig. 13, in case 2, after 50 iterations and rate improvement of about 5bps/Hz, an improvement of about 35% on the basis of the comparison scheme, was seen in the multi-user and rate optimized performance, compared to the fractional programming based beamforming algorithm.
Referring to fig. 14 and 15, it is apparent from the 3D pattern of the scene 1 simulation experiment that there are two stronger beam main lobes, i.e., the two beam directions identified by arrows in the figure.
Referring to fig. 16 and 17, in order to verify the directions of the main lobes of the beams, as in the simulation of the scene 1, the directions of the two main lobes of the beams (in terms ofExpressed) asThe deviation is within 10 ° and the directions coincide, compared to the angles preset in table 1.
Fig. 18 is a block diagram of a beamforming system of a super-surface antenna according to an embodiment of the present disclosure. As shown in fig. 18, the beamforming system is configured to implement the beamforming method provided in the foregoing embodiment, and includes a construction module, a solution module, and a deployment module.
The construction module is configured to construct a beam forming model of the super-surface antenna, wherein the target in the beam forming model is that a user side cluster and the communication quality index between the super-surface antennas are the largest, the decision variable in the beam forming model is a state code word respectively corresponding to N antenna units included in the super-surface antenna, the state code word is represented by b bits, b is a positive integer, and the user side cluster comprises at least one user side.
In the binary bat algorithm solving process, individuals in the population are integral code words formed by arranging N state code words corresponding to the antenna units in sequence, the fitness of the individuals in the population is the communication quality index between a user side cluster and the super-surface antenna when the integral code words corresponding to the individuals are deployed on the super-surface antenna, and the optimal solution comprises the optimal value of the state code words of the antenna units.
The deployment module is configured to deploy the super-surface antenna according to the current optimal solution.
For a specific description of each functional module, reference may be made to the content in the foregoing embodiment, which is not repeated here.
Based on the same inventive concept, the embodiment of the disclosure also provides electronic equipment. Fig. 19 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure. As shown in fig. 19, an embodiment of the present disclosure provides an electronic device comprising one or more processors 101, memory 102, one or more I/O interfaces 103. The memory 102 stores one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the beamforming method according to any of the embodiments described above, and one or more I/O interfaces 103 coupled between the processors and the memory configured to implement information interaction between the processors and the memory.
The processor 101 is a device with data processing capability, including but not limited to a Central Processing Unit (CPU), the memory 102 is a device with data storage capability, including but not limited to a random access memory (RAM, more specifically SDRAM, DDR, etc.), a Read Only Memory (ROM), a charged erasable programmable read only memory (EEPROM), a FLASH memory (FLASH), and an I/O interface (read/write interface) 103 is connected between the processor 101 and the memory 102, so that information interaction between the processor 101 and the memory 102 can be realized, including but not limited to a data Bus (Bus), etc.
In some embodiments, processor 101, memory 102, and I/O interface 103 are connected to each other via bus 104, and thus to other components of the computing device.
In some embodiments, the one or more processors 101 comprise a field programmable gate array.
According to an embodiment of the present disclosure, there is also provided a computer-readable medium. The computer readable medium has stored thereon a computer program, wherein the program when executed by a processor realizes the steps of the beamforming method as in any of the above embodiments.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a machine-readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via a communication portion, and/or installed from a removable medium. The above-described functions defined in the system of the present disclosure are performed when the computer program is executed by a Central Processing Unit (CPU).
It should be noted that the computer readable medium shown in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of a computer-readable storage medium may include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It is to be understood that the above embodiments are merely exemplary embodiments employed to illustrate the principles of the present disclosure, however, the present disclosure is not limited thereto. Various modifications and improvements may be made by those skilled in the art without departing from the spirit and substance of the disclosure, and are also considered to be within the scope of the disclosure.
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