Distributed MIMO near-field AP circular deployment parameter optimization method and device

Abstract

The invention discloses a distributed MIMO near-field AP circular deployment parameter optimization method. The method comprises the following steps: acquiring the position and azimuth angle of a user in a service area; according to the position and the azimuth angle of the user and the deployment parameters of the AP, establishing a traversal and rate expression of the distributed MIMO downlink near field communication system; based on a traversal and rate expression, solving a deployment parameter enabling the traversal and rate to be maximum; according to the method, the traversal and rate expression of the distributed MIMO downlink near field communication system can be established by using the position and the direction angle of the user in the service area, and then the expression is solved to obtain the deployment parameters of a plurality of APs, so that the system performance of the distributed MIMO downlink is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to a method and device for optimizing circular deployment parameters of distributed MIMO near-field APs. Background Art

[0002] With the official launch of 5G commercial use, people have higher expectations for 6G in terms of peak rate, spectrum efficiency, and coverage, so the development of new wireless technologies is particularly important. In recent years, distributed MIMO has emerged in response to technological development. Unlike centralized MIMO, where all antennas are located in the same location, distributed MIMO consists of multiple antenna ports (antenna points, APs) distributed in a certain area at different geographical locations. All APs can provide services to multiple users in the same time-frequency resources. Compared with centralized MIMO, the links between distributed MIMO transmission ports are more independent, which improves the coverage of the cell, shortens the distance of signal access, and can achieve higher system capacity.

[0003] In addition, benefiting from the huge spatial multiplexing and beamforming gains of massive MIMO technology, researchers proposed using extremely large-scale antenna arrays (XLAA) to improve system performance. Based on the characteristics of distributed MIMO, combining it with XLAA, that is, deploying XLAA at each AP, will bring new possibilities and directions to 6G technology. During AP deployment, the deployment location of each AP is usually optimized to increase the utilization of the system. However, after the existing AP deployment scheme has achieved a certain level of system performance, the system performance has reached a bottleneck and it is difficult to improve it further. Summary of the invention

[0004] The purpose of the present invention is to provide a distributed MIMO near-field AP circular deployment parameter optimization method and device, which optimizes the AP deployment parameters in combination with the distance between the user and the AP, and improves the system traversal and rate.

[0005] The present invention adopts the following technical solution: a distributed MIMO near-field AP circular deployment parameter optimization method, comprising:

[0006] Obtain the location and azimuth of users in the service area;

[0007] Based on the user's position and azimuth, and the AP's deployment parameters, the traversal and rate expressions of the distributed MIMO downlink near-field communication system are established;

[0008] Solve the deployment parameters that maximize the traversal and rate based on the traversal and rate expressions.

[0009] Preferably, solving the deployment parameters that maximize the traversal and rate based on the traversal and rate expressions includes:

[0010] Solve for deployment parameters that minimize the interference term, which is obtained by approximating the ergodic and rate expressions.

[0011] Preferably, the interference term is:

[0012]

[0013] Among them, Ω represents the interference term, m represents the ordinal number of AP, M represents the total number of AP, and b k,m (r k ,θ k ) represents the steering vector between the kth user and the mth AP, r k represents the distance between the projection of the kth user on the xoy plane in the service area coordinate system and the origin of the coordinate system in the service area coordinate system, θ k represents the azimuth of the kth user, b i,m (r i ,θ i ) represents the steering vector between the i-th user and the m-th AP, i≠m, r i represents the distance between the projection of the i-th user on the xoy plane in the service area coordinate system and the origin of the coordinate system in the service area coordinate system, θ i represents the azimuth angle of the i-th user.

[0014] Preferably, the steering vector is calculated based on the distance between the user and an antenna in the AP.

[0015] Preferably, the distance between the user and the antenna in the AP is calculated based on the position and azimuth of the user, and the position of the antenna in the AP.

[0016] Preferably, the distance between the user and the antenna in the AP is calculated as follows:

[0017]

[0018] Among them, r k,mn represents the distance between the kth user and the nth antenna in the mth AP, Δz n Indicates the offset distance of the nth antenna on the AP relative to the center of the antenna array on the z-axis. The z-axis is the z-axis in the system coordinate system. The system coordinate system takes the center of the service area as the origin, any direction in the plane where the ground is located as the x-axis, the direction perpendicular to the x-axis in the plane where the ground is located as the y-axis, and the vertical direction as the z-axis. m represents the angle of the antenna array on the mth AP rotated counterclockwise around an axis parallel to the x-axis and passing through the center of the array, α mrepresents the angle of the antenna array on the mth AP rotated counterclockwise around the axis parallel to the z-axis and passing through the center of the array, r represents the location radius of the AP circular deployment, φ m represents the position angle of the circular deployment of the mth AP, Δx n Indicates the offset distance of the nth antenna on the AP relative to the center of the antenna array on the x-axis, D AP Indicates the deployment height of the AP, D UE Indicates the user's height.

[0019] Preferably, b k,m (r k ,θ k ) is calculated as:

[0020]

[0021] Where λ represents the wavelength, r k,m1 represents the distance between the kth user and the first antenna in the mth AP, r k,mN Represents the distance between the k-th user and the N-th antenna in the m-th AP.

[0022] Preferably, solving the deployment parameters that minimize the interference term includes:

[0023] The other item is solved in turn using any two of the AP circular deployment position radius, AP circular deployment position angle vector, and array direction angle vector in the deployment parameters as known quantities until the change in the interference item is less than the tolerance coefficient or the maximum number of iterations is reached, and the deployment parameters that minimize the interference item are obtained.

[0024] Preferably, solving another item by taking any two of the AP circular deployment position radius, the AP circular deployment position angle vector and the array direction angle vector in the deployment parameters as known quantities in sequence includes:

[0025] The position angle vector of the AP circular deployment and the array direction angle vector are known quantities, and the position radius of the AP circular deployment is solved;

[0026] The position angle vector of the AP circular deployment is solved by taking the position radius of the AP circular deployment and the array direction angle vector obtained as known quantities;

[0027] The array direction angle vector is solved by using the solved AP circular deployment position angle vector and the solved AP circular deployment position radius as known quantities.

[0028] Another technical solution of the present invention: a distributed MIMO near-field AP circular deployment parameter optimization device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to perform the above method.

[0029] The beneficial effect of the present invention is that the present invention utilizes the position and direction angle of users in the service area to establish the traversal and rate expressions of the distributed MIMO downlink near-field communication system, and then solves the expressions to obtain the deployment parameters of multiple APs, thereby improving the system performance of the distributed MIMO downlink. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a schematic diagram of an AP deployment method in an embodiment of the present invention;

[0031] Figure 2 Schematic diagram of AP deployment states corresponding to different user locations in an embodiment of the present invention;

[0032] Figure 3 4 is a diagram comparing the system performance of different methods in the embodiments of the present invention. DETAILED DESCRIPTION

[0033] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0034] Currently, existing studies have only considered the optimization of the deployment location of distributed MIMO APs, and all use the far-field plane wave channel model, which results in the actual near-field communication failing to fully utilize the additional gain brought by AP deployment parameters (location and array direction).

[0035] Within the distance range considered in wireless communication, the propagation field of electromagnetic waves is usually divided into far field and near field, and the boundary between far and near fields is determined by the Rayleigh distance. When the communication distance between the base station (BS) and the user (UE) is greater than the Rayleigh distance, the UE is located in the far field of the BS, and the electromagnetic wave propagation adopts the plane wave approximation, and the electromagnetic waves radiated by the array antenna propagate in a specific direction; when the communication distance between the BS and the UE is less than the Rayleigh distance, the UE is located in the near field of the BS, and the electromagnetic wave propagation needs to adopt a more accurate spherical wave model, and the electromagnetic wave signal radiated by the array is focused on a specific position in the physical space, that is, the energy is simultaneously focused on a position point at a specific angle and a specific distance.

[0036] As 5G massive MIMO evolves towards ultra-large-scale antenna arrays, the size of antenna arrays will further increase, which not only means a significant increase in the number of antennas, but also implies a qualitative change in the electromagnetic field structure from plane waves to spherical waves.

[0037] In practical applications, the sharp increase in the number of antennas will expand the near-field area of ​​the system by several orders of magnitude, the electromagnetic field structure will undergo a qualitative change from plane wave to spherical wave, and the channel model will change from the far-field plane wave model to the near-field spherical wave model. The near-field area cannot be ignored. Therefore, it is particularly important to develop new communication technologies based on the near-field channel model.

[0038] The present invention discloses a method for optimizing circular deployment parameters of a distributed MIMO near-field AP, comprising: obtaining the position and azimuth of a user in a service area; establishing traversal and rate expressions of a distributed MIMO downlink near-field communication system according to the position and azimuth of the user and the deployment parameters of the AP; and solving the deployment parameters that maximize the traversal and rate based on the traversal and rate expressions.

[0039] The present invention utilizes the position and direction angle of users in the service area to establish the traversal and rate expressions of the distributed MIMO downlink near-field communication system, and then solves the expressions to obtain the deployment parameters of multiple APs, thereby improving the system performance of the distributed MIMO downlink.

[0040] In the present invention, a single cell is taken as an example as the service area, and a single-cell distributed MIMO near-field downlink system is considered. The system distributes M APs, and each AP deploys a uniform planar array (UPA) composed of N antennas. All APs serve K single-antenna users at the same time. The signal received by the kth user is:

[0041]

[0042] Among them, p k,m represents the transmission power between the kth user and the mth AP, p i,m represents the transmission power between the i-th user and the m-th AP; is the channel matrix between the kth user and the mth AP, is the precoding vector between the kth user and the mth AP, is the precoding vector between the i-th user and the m-th AP, where represents the expectation, ||·|| represents the 2-norm, s k represents the signal to be sent to the kth user, s i Represents the signal to be sent to the i-th user, which is sent by all APs and satisfies represents the complex Gaussian additive noise of the kth user, σ 2 represents the noise variance.

[0043] Using equal power allocation, assuming that the maximum transmit power of each AP is P max , then the power allocated by each AP to each user is ρ = p k,m =P max / K.

[0044] The signal-to-noise ratio (SINR) of the kth user is:

[0045]

[0046] Then the traversal and rate of the system are:

[0047]

[0048] like Figure 1 As shown, consider M APs deployed at a height of D AP In the present invention, a service area coordinate system is first established, specifically, the center position of the service area is taken as the coordinate origin, the horizontal plane where the ground is located is taken as the xoy plane, any direction in the xoy plane is taken as the positive direction of the x-axis, the direction perpendicular to the positive direction of the x-axis in the xoy plane is taken as the positive direction of the y-axis, and the vertically upward direction is taken as the positive direction of the z-axis.

[0049] M APs are deployed on a circular path with a radius of r. In the initial state, the M APs are arranged at the same angle from the positive half axis of the x-axis. Counterclockwise, they are evenly distributed on a circle with a radius of r. At the same time, the AP on the positive half of the x-axis is designated as the first AP. In counterclockwise order, they are the second AP, ..., the Mth AP. The position of the mth AP is P mc =(rcosφ m ,rsinφ m ,D AP ) T ,in represents the deployment angle of the mth AP. The position of the kth user is represented by u k =(r k cosθ k ,r k sinθ k ,D UE ) T , r k is the distance from the projection of the kth user on the xoy plane to the origin of the coordinate system, θ k is the azimuth angle from the kth user to the origin of the coordinate system, that is, the angle between the projection line of the line connecting the user and the origin onto the xoy plane and the positive direction of the x-axis, D UE is the user deployment height. Based on the near-field uniform spherical wave LOS channel model, the channel matrix h between the kth user and the mth AP k,m for:

[0050] h k,m =g k,m b k,m (r k ,θ k ) (4)

[0051] Among them, g k,m is the complex channel gain between the kth user and the mth AP, b k,m (r k ,θ k ) represents the steering vector between the kth user and the mth AP. Specifically, the steering vector is calculated based on the distance between the user and the antenna in the AP.

[0052] In one embodiment, b k,m (r k ,θ k ) is calculated as:

[0053]

[0054] Where λ represents the wavelength, r k,m1 represents the distance between the kth user and the first antenna in the mth AP, r k,mN represents the distance between the kth user and the Nth antenna in the mth AP, r k,mn Indicates the distance between the k-th user and the n-th antenna in the m-th AP.

[0055] Furthermore, consider that each AP is initially deployed parallel to the xoz plane and consists of N antennas in a uniform planar antenna array (UPA). The UPA on each AP is arranged in N order along the positive direction of the x-axis starting from the first antenna. x Antennas, arranged along the positive direction of the z-axis z antennas, so the total number of antennas on each AP is N = N x N z . Note that the first antenna on the UPA is located in the first row and first column, starting from the positive direction of the x-axis, and the second antenna is located in the first row and second column, and so on. The nth antenna is located in the first row and second column, and so on. z -1)N x +n x The root antenna is located at the nth z Line n x The distance between every two adjacent antennas is The horizontal spacing and the vertical spacing are the same, and λ represents the wavelength.

[0056] For the convenience of mathematical representation, the midpoint between the first antenna and the Nth antenna of each AP is defined as the center position of the antenna array of the AP (i.e., the location P of the AP). mc ), so the position of the first antenna on the mth AP is Among them, x mc represents the x-axis coordinate of the mth AP, y mc represents the y-axis coordinate of the mth AP, z mc Indicates the z-axis coordinate of the mth AP.

[0057] Furthermore, let the first antenna on the m-th AP be the reference antenna on the AP, then the position of the n-th antenna on the m-th AP is:

[0058]

[0059] Among them, P mn represents the position of the nth antenna on the mth AP, i x (n) = mod(n-1, N x ),i x (n) represents the row index of the nth antenna on the AP relative to the reference antenna, mod represents the modulo operation, i z (n) represents the column index of the nth antenna on the AP relative to the reference antenna, Δx n Indicates the offset distance of the nth antenna on the AP relative to the center of the antenna array on the x-axis, Δz n Indicates the offset distance of the nth antenna on the AP relative to the center of the antenna array on the z-axis.

[0060] In addition, to adapt to more general application scenarios, the UPA on each AP can be located in the three-dimensional space with the topological center P mc The rotation point is rotated to send the beam in any direction. Let α m ∈[0,2π) represents the angle of the antenna array on the mth AP rotated counterclockwise around the axis parallel to the z-axis and passing through the center of the array. Let β m ∈[-π,π) represents the angle of the antenna array on the mth AP to rotate counterclockwise around an axis parallel to the x-axis and passing through the center of the array. The UPA can be obtained by the rotation matrix T z (α m ) and T x (β m ) to get the position of the rotated array element, where T z (α m ) and T x (β m ) are:

[0061]

[0062] When the AP antenna array is not rotated, the position offset matrix from the nth antenna of any AP to the topological center of the AP is: Then the plane array rotates by any angle α m and β m Afterwards, the position of the nth antenna on the mth AP can be updated as:

[0063]

[0064] In the present invention, the distance between the user and the antenna in the AP is calculated based on the position and azimuth of the user and the position of the antenna in the AP. More specifically, the distance between the user and the antenna in the AP is calculated as follows:

[0065]

[0066] Among them, r k,mn represents the distance between the kth user and the nth antenna in the mth AP, Δz n Indicates the offset distance of the nth antenna on the AP relative to the center of the antenna array on the z-axis. The z-axis is the z-axis in the system coordinate system. The system coordinate system takes the center of the service area as the origin, any direction in the plane where the ground is located as the x-axis, the direction perpendicular to the x-axis in the plane where the ground is located as the y-axis, and the vertical direction as the z-axis. m represents the angle of the antenna array on the mth AP rotated counterclockwise around an axis parallel to the x-axis and passing through the center of the array, α m represents the angle of the antenna array on the mth AP rotated counterclockwise around the axis parallel to the z-axis and passing through the center of the array, r represents the location radius of the AP circular deployment, φ m represents the position angle of the circular deployment of the mth AP, Δx n Indicates the offset distance of the nth antenna on the AP relative to the center of the antenna array on the x-axis, D AP Indicates the deployment height of the AP, D UE represents the user’s height, and (a) represents the second-order Taylor expansion.

[0067] In the embodiment of the present invention, the optimization problem is constructed with the location radius of the AP circular deployment, the location angle vector of the AP circular deployment, and the array direction angle vector of the AP as variables, with the goal of maximizing the traversal and rate of the system. The position angle interval is uniformly distributed, that is is a constant, so optimizing the position angle vector of the AP circular deployment is equivalent to optimizing the position angle φ1 of the first AP.

[0068] In addition, in the embodiment of the present invention, only the optimization of the azimuth rotation angle of the antenna array is considered, so the first optimization problem is:

[0069]

[0070] Where r represents the location radius of the AP circular deployment, r min and r max They represent the radius of the AP circular deployment (the value is determined according to the service range), φ1 represents the position angle of the first AP circular deployment, α=[α1 α2 … α M] represents the array direction angle vector of AP.

[0071] In the embodiment of the present invention, the objective function in the first optimization problem is analyzed and approximated, and the approximate expression of the system traversal and rate is obtained using Jensen inequality:

[0072]

[0073] Using MRT precoding method At the same time, since the numerator and denominator in the SINR term are independent of each other, according to Mullen's inequality for:

[0074]

[0075] Based on the law of large numbers and the statistical properties of Gaussian random variables, we can obtain:

[0076]

[0077]

[0078] It can be further approximated as:

[0079]

[0080] Therefore, the approximate values ​​of the traversal and rate are:

[0081]

[0082] Therefore, solving the deployment parameters that maximize the traversal and rate based on the traversal and rate expressions includes: solving the deployment parameters that minimize the interference term; wherein the interference term is obtained by approximating the traversal and rate expressions.

[0083] Specifically, the interference items are:

[0084]

[0085] Among them, Ω represents the interference term, m represents the ordinal number of AP, M represents the total number of AP, and b k,m (r k ,θ k ) represents the steering vector between the kth user and the mth AP, r k represents the distance between the projection of the kth user on the xoy plane in the service area coordinate system and the origin of the coordinate system in the service area coordinate system, θ k represents the azimuth of the kth user, b i,m (r i ,θ i) represents the steering vector between the i-th user and the m-th AP, i≠m, r i represents the distance between the projection of the i-th user on the xoy plane in the service area coordinate system and the origin of the coordinate system in the service area coordinate system, θ i represents the azimuth angle of the i-th user.

[0086] Therefore, based on the above analysis, solving the interference term is to solve the second optimization problem:

[0087]

[0088] In the embodiment of the present invention, in order to solve the second optimization problem, an alternating optimization method is used to decompose the optimization problem into three optimization sub-problems, that is, the position radius of the circular deployment of APs, the position angle vector of the circular deployment of APs, and the AP array direction vector are respectively optimized and solved alternately, that is, the deployment parameters that minimize the interference term include:

[0089] The other item is solved in turn using any two of the AP circular deployment position radius, deployment position angle vector, and array direction angle vector in the deployment parameters as known quantities until the change in the interference item is less than the tolerance coefficient or the maximum number of iterations is reached, and the deployment parameters that minimize the interference item are obtained.

[0090] Specifically, solving another item in sequence using any two of the AP circular deployment position radius, the AP circular deployment position angle vector, and the array direction angle vector in the deployment parameters as known quantities includes: solving the AP circular deployment position radius using the AP circular deployment position angle vector and the array direction angle vector as known quantities; solving the AP circular deployment position angle vector using the solved AP circular deployment position radius and the array direction angle vector as known quantities; solving the array direction angle vector using the solved AP circular deployment position angle vector and the solved AP circular deployment position radius as known quantities.

[0091] In the first calculation process of the alternating optimization solution, the initial parameters can be arbitrarily selected within the value range of each parameter for calculation. However, in the subsequent calculation process, the parameters obtained in the previous calculation need to be calculated as the initial values.

[0092] In a specific embodiment, the solution method is shown in Table 1.

[0093] Table 1

[0094]

[0095] First, optimize the location radius of the circular deployment of APs.

[0096] Given the position angle φ1 of the first AP circular deployment and the AP array direction angle vector α, the optimization problem becomes (optimization sub-problem 1):

[0097]

[0098] The expression of Ω is:

[0099]

[0100] Therefore, the objective function of optimizing subproblem 1 is:

[0101]

[0102] The gradient of the objective function at r is:

[0103]

[0104] Where Ψ'(r) represents the derivative of Ψ(r) with respect to r.

[0105] Based on the above analysis, the gradient descent method is used to optimize the location radius of the AP circular deployment. Due to the constraint on r in equation (20), when the gradient descent method is used to solve the problem, it is necessary to determine whether the constraint condition is met during each iteration. If the constraint condition is not met in a certain iteration, it is necessary to move along the positive gradient direction to find a feasible solution and continue to iterate until the convergence condition is met.

[0106] The process of solving the optimization sub-problem 1 can be expressed as shown in Table 2 below.

[0107] Table 2

[0108]

[0109]

[0110] After the above optimization sub-problem 1 is completed, the optimization sub-problem 2 is continued, that is, optimizing the position angle vector of the circular deployment of APs.

[0111] Given the AP circular deployment location radius r and AP array direction angle vector α, the optimization problem becomes (optimization sub-problem 2)

[0112]

[0113] The expression of Ω is:

[0114]

[0115]

[0116] The objective function of optimizing subproblem 2 is:

[0117]

[0118] The solution method is the same as that of optimization subproblem 1. The gradient descent method is also used to solve the deployment angle φ1 of the first AP, and then the deployment position angle vector φ of all APs is obtained. The specific solution process will not be repeated here.

[0119] After solving optimization sub-problem 2, continue to solve optimization sub-problem 3 to optimize the AP array direction angle vector.

[0120] Given the location radius r of the AP circular deployment and the location angle φ1 of the first AP circular deployment, the optimization problem becomes (obtaining optimization sub-problem 3):

[0121]

[0122] The objective function of the optimization problem is:

[0123]

[0124] The solution method is the same as that of optimization subproblem 1. The AP array method angle vector α is also solved using the gradient descent method. The specific solution process will not be repeated here.

[0125] After solving the above three sub-problems, we then judge whether the solution is completed according to step 4 in Table 1, and finally obtain the required parameters.

[0126] The effectiveness of the present invention is illustrated by simulation experiments below.

[0127] Set 20 users, 4 user clusters, user aggregation degree of 0.8, 4 APs, 36 antennas on each AP, cell range of 400m×400m, antenna array pitch angle rotation angle of -20°, AP deployment height of 10m, and user height of 2m.

[0128] like Figure 2 As shown, Figure 2 (a) and Figure 2 (b) is a schematic diagram of the optimized AP circular deployment position and array direction under different user cluster distributions. Figure 2 In (a), the radius of the AP circular deployment position r = 121.0617m, the AP circular deployment position angle φ = [1.0472 2.6180 4.1888 5.75]9, and the AP array direction angle α = [5.3332 2.6407 3.05920.6586]; Figure 2In (b), the AP circular deployment radius r = 136.3591 m, the AP circular deployment position angle φ = [0.7854 2.3562 3.9270 5.4978], and the AP array direction angle α = [4.1291 1.9319 2.9849 4.8354]. It can be seen that under different user cluster distributions, APs tend to be deployed close to user clusters, and the AP antenna array tends to rotate to the direction of the user cluster.

[0129] In addition, this embodiment also compares different optimization schemes. Figure 3 The performance comparison chart of the scheme proposed in the present invention, the non-optimized AP array direction (only the location radius and location angle of AP circular deployment are optimized), the non-optimized AP circular deployment location radius (only the AP circular deployment location angle and array direction are optimized), the non-optimized AP circular deployment location angle (only the AP circular deployment location radius and array direction are optimized) and AP deployment based on Poisson distribution (PPP) under different signal-to-noise ratios.

[0130] The parameters of the AP circular deployment in the method of the present invention are: r=136.m3, φ=[0.7854 2.3562 3.92705.4978], α=[4.1291 1.9319 2.9849 4.8354]; the parameters of the AP circular deployment in the unoptimized AP array direction scheme are: r=129.m9, φ=[0.8131 2.3839 3.9547 5.5255], α=[0 0 0 0]; the parameters of the AP circular deployment in the unoptimized AP circular deployment position radius scheme are: r=114.7129m, φ=[0.7691 2.3399 3.91075.4815], α=[2.1217 3.7848 2.4204 3.8661]; the parameters of the AP circular deployment in the non-optimized AP circular deployment are: r = 90.2519m, φ = [0 1.5708 3.1416 4.7124], α = [2.7624 4.90402.9392 1.9011]; the relevant parameters in the AP deployment solution based on Poisson distribution (PPP) are: the horizontal coordinates of all AP positions X AP =[73.3951 -15.4173 -115.2983 -161.5329], Y coordinate of all AP positions AP =[129.4968 -130.8437 -135.4911 -66.1759], α=[0 0 0 0].

[0131] As can be seen from the figure, the traversal and rate performance under different schemes are improved with the increase of SNR, but the performance of the scheme proposed in the present invention always remains the best. Specifically, when the signal-to-noise ratio is 10dB, compared with the non-optimized AP array direction scheme, the performance of the scheme proposed in the present invention is improved by about 20%; compared with the non-optimized AP circular deployment location radius scheme, the performance of the scheme proposed in the present invention is improved by about 69%; compared with the non-optimized AP circular deployment location angle scheme, the performance of the scheme proposed in the present invention is improved by about 180%; compared with the AP deployment scheme based on Poisson distribution (PPP), the performance of the scheme proposed in the present invention is improved by about 248%.

[0132] The present invention also discloses a distributed MIMO near-field AP circular deployment parameter optimization device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to perform the above method.

[0133] The present invention also discloses an embodiment that provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0134] The present invention also provides a computer program product. When the computer program product runs on a data storage device, the data storage device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0135] If the integrated unit module is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device capable of carrying the computer program code to a storage device, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electrical carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0136] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0137] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0138] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules or units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0139] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Claims

1. A distributed MIMO near-field AP circular deployment parameter optimization method, characterized in that: include: Obtain the location and azimuth of users in the service area; Establishing traversal and rate expressions of a distributed MIMO downlink near field communication system according to the position and azimuth of the user and the deployment parameters of the AP; The deployment parameters that maximize the traversal and rate are solved based on the traversal and rate expressions.

2. The distributed MIMO near-field AP circular deployment parameter optimization method according to claim 1, characterized in that: Solving the deployment parameters that maximize the traversal and rate based on the traversal and rate expressions includes: Solve for deployment parameters that minimize interference terms; wherein the interference terms are obtained by approximating the traversal and rate expressions.

3. The distributed MIMO near-field AP circular deployment parameter optimization method according to claim 2, characterized in that: The interference term is: Among them, Ω represents the interference term, m represents the ordinal number of AP, M represents the total number of AP, and b k,m (r k ,θ k ) represents the steering vector between the kth user and the mth AP, r k represents the distance between the projection of the kth user on the xoy plane in the service area coordinate system and the origin of the coordinate system in the service area coordinate system, θ k represents the azimuth of the kth user, b i,m (r i ,θ i ) represents the steering vector between the i-th user and the m-th AP, i≠m, r i represents the distance between the projection of the i-th user on the xoy plane in the service area coordinate system and the origin of the coordinate system in the service area coordinate system, θ i represents the azimuth angle of the i-th user.

4. The distributed MIMO near-field AP circular deployment parameter optimization method according to claim 3, characterized in that: The steering vector is calculated based on the distance between the user and the antenna in the AP.

5. The distributed MIMO near-field AP circular deployment parameter optimization method according to claim 4, characterized in that: The distance between the user and the antenna in the AP is calculated based on the position and azimuth of the user and the position of the antenna in the AP.

6. The distributed MIMO near-field AP circular deployment parameter optimization method according to claim 5, characterized in that: The distance between the user and the AP antenna is calculated as follows: Among them, r k,mn represents the distance between the kth user and the nth antenna in the mth AP, Δz n represents the offset distance of the nth antenna on the AP relative to the center of the antenna array on the z-axis, where the z-axis is the z-axis in the system coordinate system. The system coordinate system takes the center of the service area as the coordinate origin, any direction in the plane where the ground is located as the x-axis, the direction perpendicular to the x-axis in the plane where the ground is located as the y-axis, and the vertical direction as the z-axis. m represents the angle of the antenna array on the mth AP rotated counterclockwise around an axis parallel to the x-axis and passing through the center of the array, α m represents the angle of the antenna array on the mth AP rotated counterclockwise around the axis parallel to the z-axis and passing through the center of the array, r represents the location radius of the AP circular deployment, φ m represents the position angle of the circular deployment of the mth AP, Δx n Indicates the offset distance of the nth antenna on the AP relative to the center of the antenna array on the x-axis, D AP Indicates the deployment height of the AP, D UE Indicates the user's height.

7. The distributed MIMO near-field AP circular deployment parameter optimization method according to claim 6, characterized in that: b k,m (r k ,θ k ) is calculated as: Where λ represents the wavelength, r k,m1 represents the distance between the kth user and the first antenna in the mth AP, r k,mN Represents the distance between the k-th user and the N-th antenna in the m-th AP.

8. The distributed MIMO near-field AP circular deployment parameter optimization method according to claim 2, characterized in that: Solving for the deployment parameters that minimize the interference term includes: The other item is solved in sequence using any two of the AP circular deployment position radius, the AP circular deployment position angle vector and the array direction angle vector in the deployment parameters as known quantities until the change in the interference item is less than the tolerance coefficient or the maximum number of iterations is reached, thereby obtaining the deployment parameters that minimize the interference item.

9. The distributed MIMO near-field AP circular deployment parameter optimization method according to claim 8, characterized in that: Solving another item by taking any two of the AP circular deployment position radius, the AP circular deployment position angle vector, and the array direction angle vector in the deployment parameters as known quantities in sequence includes: The position angle vector of the AP circular deployment and the array direction angle vector are known quantities, and the position radius of the AP circular deployment is solved; The position angle vector of the AP circular deployment is solved by taking the position radius of the AP circular deployment and the array direction angle vector obtained as known quantities; The array direction angle vector is solved by using the solved AP circular deployment position angle vector and the solved AP circular deployment position radius as known quantities.

10. A distributed MIMO near-field AP circular deployment parameter optimization device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 9 is implemented.

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