A Large-Scale Array Synthesis Method for System Capacity Maximization

Abstract

The present invention relates to a large-scale array synthesis method for maximizing the system channel capacity. The large-scale array synthesis method for system capacity can be used in large-scale MIMO scenarios. This method combines electromagnetic field theory and information theory, aims to maximize the system capacity, extracts the transmission coefficient between the transceiver antennas and the coupling characteristics between the elements in the large-scale array by changing the electromagnetic characteristics of the large-scale array, and optimizes the arrangement mode of each element in the large-scale array, thereby improving the system performance. The present invention meets the requirements of the current DBF large-scale array, and at the same time bridges the near field and far field of the antenna array, solving the situation where the near-field pattern of the antenna is not applicable.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communication, and in particular relates to a comprehensive method for large-scale arrays for maximizing system capacity. Background Art

[0002] The booming development of wireless mobile devices and services has put forward more challenging requirements for the next-generation communication systems, such as high transmission rate, high capacity, low connection delay, large-scale Internet of Things (IoT) communication, etc. As a major technical indicator of 5G, massive multiple-input multiple-output (massive MIMO) technology is widely used in the 5G millimeter-wave (mmWave) band. Although the multi-antenna configuration of massive MIMO can increase the spatial degrees of freedom, improve the diversity gain and multiplexing gain, and its high-gain characteristic can reduce the loss of the base station transmission power, with the increase in the number of antennas, the number of back-end links connected to the antennas also increases accordingly. The increased number of links not only brings a sharp increase in cost, but also puts forward higher requirements for aspects such as system heat dissipation, complexity, and integration.

[0003] The sparsification of the array can reduce the number of array elements on the basis of meeting certain performance indicators. Compared with the periodic array, with the same aperture, the number of array elements becomes smaller, the number of channels is reduced, thereby reducing the cost and complexity. However, due to the wide near-field range of the large-scale array, the indicators commonly used to evaluate the antenna performance become meaningless in the near-field region at this time, and the sparsification method of the large-scale array is realized by suppressing the grating lobes of the radiation pattern, and this method is not applicable to the near-field region of the large-scale array. Therefore, it is necessary to establish the relationship between the antenna sparsification method and the system evaluation model to complete the sparsification of the large-scale MIMO array based on system indicators. Summary of the Invention

[0004] Technical Problems to be Solved

[0005] In order to avoid the deficiencies of the prior art, the present invention provides a comprehensive method for large-scale arrays for maximizing the system channel capacity.

[0006] Technical Solution

[0007] A comprehensive method for large-scale arrays for maximizing system capacity, characterized by the following steps:

[0008] Step 1: Through electromagnetic simulation software, calculate the load impedance Z of the receiving end at all possible positions under the conditions of a known antenna element size and number within a limited aperture range L , the impedance matrix Z of the receiving array RR , the impedance transfer matrix Z between the transmitting end and the receiving end RT , the impedance matrix Z of the transmitting array TT and the source impedance Z of the transmitting endS ;

[0009] Step 2: Calculate the channel matrix:

[0010] H = H EM = Z L (Z L + Z RR ) -1 Z RT (Z TT + Z s ) - 1

[0011] where H EM is the channel matrix with electromagnetic characteristics;

[0012] Step 3: Set the optimization objective function:

[0013]

[0014]

[0015] In the formula, C is the maximized system capacity, which is the optimization objective; n R , n T represent the number of receiving and transmitting antennas respectively, is the n R -dimensional identity matrix, P is the total transmission power, N0 is the noise power, is the channel matrix, h ij represents the channel gain from the j-th transmitting antenna to the i-th receiving antenna, represents the conjugate transpose of the matrix; Δ i is the array element selection function, whose value is 0 when there is no element at that position and 1 when there is an antenna element at that position. There are N positions in the limited aperture for arranging M antenna elements;

[0016] Step 4: Use the genetic algorithm to optimize the objective function in Step 3 to obtain the arrangement of each element in the large-scale array.

[0017] The electromagnetic simulation software in Step 1 is CST or HFSS.

[0018] A computer system, comprising: one or more processors, a computer-readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the above method.

[0019] A computer-readable storage medium, storing computer-executable instructions, which are used to implement the above method when executed.

[0020] Beneficial effects

[0021] A large-scale array synthesis method for maximizing the system channel capacity provided by the present invention establishes the relationship between the antenna sparsification method and the system evaluation model, and completes the sparsification of the large-scale MIMO array based on the system index. This method combines the electromagnetic field theory and the information theory, aims at maximizing the system capacity, extracts the transmission coefficient between the transceiver antennas and the coupling characteristics between the units in the large-scale array by changing the electromagnetic characteristics of the large-scale array, and optimizes the arrangement of each unit in the large-scale array, thereby improving the system performance. In addition, the method of combining the electromagnetic field theory and the information theory described in the present invention is also applicable to channel modeling in different environments. Compared with the traditional statistical channel modeling, this method can obtain the channel characteristics more accurately. Description of the drawings

[0022] The drawings are only for the purpose of showing specific embodiments and are not considered to be a limitation of the present invention. Throughout the drawings, the same reference numerals represent the same components.

[0023] Figure 1 It is a flowchart of the large-scale array synthesis method for maximizing the system channel capacity in the present invention;

[0024] Figure 2 It is a schematic diagram of a large-scale array with 512 elements under uniform arrangement;

[0025] Figure 3 It is a schematic diagram of a sparse large-scale array with 512 elements obtained by the optimization method;

[0026] Figure 4 It is a comparison chart of the capacities of the uniformly arranged array and the optimized sparse array when the array aperture increases proportionally. Detailed implementation manners

[0027] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0028] This embodiment provides a large-scale array synthesis method for maximizing the system channel capacity. The antenna unit for the large-scale array synthesis method for maximizing the system capacity can be a single unit or a sub-array composed of multiple units; the number of antenna units is any; the array form of the large-scale array can be a regular or irregular two-dimensional planar array, or a conformal array or other array structures. In this embodiment, the array form of the large-scale array is a special case of a one-dimensional linear array.

[0029] According to Shannon's formula, the channel capacity of the MIMO system can be obtained as follows:

[0030]

[0031] where n R and n T represent the number of receiving and transmitting antennas respectively, is an n R -dimensional identity matrix, P is the total transmit power, N0 is the noise power, is the channel matrix, and h ij represents the channel gain from the j-th transmitting antenna to the i-th receiving antenna, represents the conjugate transpose of the matrix.

[0032] According to the multi-port network theory, the relationship between the received voltage v R of the receiving antenna and the source voltage v s of the transmitting antenna can be written as:

[0033] v R = Z L (Z L + Z RR ) -1 Z RT (Z TT + Z S ) -1 v s (2)

[0034] In the formula, v R is the voltage at the receiving end, Z L is the load impedance at the receiving end, Z RR is the impedance matrix of the receiving array, Z RT is the impedance transfer matrix between the transmitting and receiving ends, Z TT is the impedance matrix of the transmitting array, and Z S is the source impedance at the transmitting end.

[0035] The steps to combine electromagnetic field theory and information theory are as follows:

[0036] From the relationship between the received voltage at the receiving end and the source voltage at the transmitting end in Equation 2, the channel matrix H in Equation 1 can be written from Equation 2 as:

[0037] H = H EM = Z L (Z L + Z RR ) -1 Z RT (Z TT + Z S ) -1(3)

[0038] Among them, H EM is the channel matrix with electromagnetic characteristics. Further, Equation 1 can be written as:

[0039]

[0040] Figure 1 This is the flowchart of the large-scale array synthesis method for maximizing the system channel capacity in the present invention. In this example, the optimization of a transmitting antenna array with 512 elements is taken as an example, the reception is a multi-user model, and the optimization method takes the genetic algorithm as an example. The arrangement of each element in the large-scale array is obtained through the genetic algorithm. Specifically:

[0041] The optimization model is written as:

[0042]

[0043]

[0044] In the formula, the optimization objective is the maximized system capacity, where Δ i is the array element selection function. When its value is 0, it means there is no element at this position. When its value is 1, it means there is an antenna element at this position. There are N positions in the limited aperture range for arranging M antenna elements. In this example, the number of antenna elements is 512.

[0045] After the optimization objective and the objective function are given, it is necessary to substitute the electromagnetic characteristics of the array for solution. Specifically:

[0046] Through the electromagnetic simulation software, the load impedance Z L of the receiving end, the impedance matrix Z RR of the receiving array, the impedance transfer matrix Z RT between the transmitting end and the receiving end, the impedance matrix Z TT of the transmitting array, and the source impedance Z S of the transmitting end are calculated according to Equation 7.

[0047] After the calculation is completed, an initial population is created in combination with the foregoing given optimization objective and objective function, and the optimal individual is output according to the fitness function to obtain the optimal array arrangement. The specific steps are as follows:

[0048] 1) Encoding

[0049] Suppose the number of individuals is N P , then a population can be represented by a binary value parameter vector with a dimension of L = M h ×M v . Each individual in it can be represented as:

[0050] f i,g(i = 1, 2, …, N P )

[0051] where i is the serial number of an individual in the corresponding population, g represents the number of genetic generations, and N P represents the number of individuals in a population, M h and M v respectively represent the unit position coordinates in the horizontal and vertical directions of the array. In this example, the number of individuals n P takes the value of 50.

[0052] By initially encoding the individuals of the population, an initial search point is established. Let the number of units be N L units. Assuming the initialized population conforms to a Gaussian distribution, the initial parameters of the individuals can be obtained by the following formula:

[0053] f ji,0 = randn[0, 1](i = 1, 2, …, N P ; j = 1, 2, …, N L )

[0054] where randn[0, 1] represents a random number that conforms to a Gaussian distribution generated between [0, 1]. Let the values of the largest N L genes in each individual be 1, and the values of the remaining genes be 0. In this example, the number of units N L is 512, that is, the array is composed of 512 units in total.

[0055] In this example, within the limited aperture range, there are 1024 positions for arranging 512 antenna units. Therefore, Δ is a 1024-dimensional identity matrix. According to the initial parameter f ji,0 , there are 512 elements on its diagonal that take the value of 1, and the remaining elements are 0, representing that 512 units are selected.

[0056] 2) Selection

[0057] The "roulette wheel" selection method is adopted, and the proportion of the fitness of each individual f i,g is used to determine the possibility of retaining its offspring. If the fitness of an individual f i,g is fit i , and the population size is N P , then the probability of its being selected is expressed as:

[0058]

[0059] When the value of the individual fitness is larger, it means the probability of its being selected is also larger. In this example, fit irepresents the capacity value in the case of the i-th array arrangement, that is, in the i-th array distribution case, the capacity value obtained by synthesizing the electromagnetic characteristics of the array. Finally, N P individuals are selected from the population with the largest fitness value to obtain the optimal array element distribution Δ in this generation of the population. In this example, in order to select crossover individuals, multiple rounds of selection are required. A uniform random number in the range of [0, 1] is generated in each round, and this random number is used as a selection pointer to determine the selected individual.

[0060] 3) Crossover

[0061] Pair up the selected odd individuals f 2i-1,g and even individuals f 2i,g . For each pair of individuals, exchange some of their genes with a crossover probability P c . This individual is the optimal individual Δ obtained in the selection step i .

[0062] 4) Mutation

[0063] For each individual Δ in the population after crossover i , change the gene values at some loci to other allele values with a mutation probability P m .

[0064] Figure 2 is a schematic diagram of a large-scale array with 512 elements in a uniform arrangement; it can be seen from the figure that the spacing between adjacent antennas is consistent and fills the entire aperture.

[0065] Figure 3 is a schematic diagram of a thinned large-scale array with 512 elements obtained by an optimization method; compared with the uniform array shown in Figure 2 , it can be seen that the arrangement of the elements has randomness and has the characteristics of thinning.

[0066] Figure 4 is the capacity comparison between the uniform arrangement array and the thinned array obtained by optimization when the array aperture increases proportionally. It can be seen from the results that the array thinning method proposed by the present invention can improve the system capacity under the same array aperture and maximize the aperture utilization rate.

[0067] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of the present invention.

Claims

1. A large-scale array synthesis method for maximizing system capacity, characterized in that The steps are as follows: Step 1: Using electromagnetic simulation software, calculate the load impedance Z of the receiving end at all possible positions under the conditions of a known antenna element size and number within a limited aperture range L , the impedance matrix Z of the receiving array RR , the impedance transfer matrix Z between the transmitting end and the receiving end RT , the impedance matrix Z of the transmitting array TT and the source impedance Z of the transmitting end S ; Step 2: Calculate the channel matrix: H = H EM = Z L (Z L + Z RR ) -1 Z RT (Z TT + Z S ) -1 Among them, H EM is a channel matrix with electromagnetic characteristics; Step 3: Set the optimization objective function: where C is the maximized system capacity, which is the optimization objective; n R , n T represent the numbers of receiving and transmitting antennas respectively, is the n R -dimensional identity matrix, P is the total transmit power, N0 is the noise power, is the channel matrix, h ij represents the channel gain from the j-th transmitting antenna to the i-th receiving antenna, represents the conjugate transpose of the matrix; Δ i is the array element selection function, whose value of 0 means there is no element at that position, and whose value of 1 means there is an antenna element at that position. There are N positions in the limited aperture range for arranging M antenna elements; Step 4: Use the genetic algorithm to optimize the objective function in Step 3 to obtain the arrangement of each unit in the large-scale array.

2. The large-scale array synthesis method for system capacity maximization according to claim 1, characterized in that: The electromagnetic simulation software in Step 1 is CST or HFSS.

3. A computer system, characterized in that Including: One or more processors, a computer-readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in claim 1.

4. A computer-readable storage medium, characterized in that Stored with computer-executable instructions that are used to implement the method described in claim 1 when executed.

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