Underground 5G Massive MIMO transmitting antenna selection method

Abstract

The invention relates to an underground 5G Massive MIMO (Multiple Input Multiple Output) transmitting antenna selection method. The selection method comprises the following steps: determining an underground 5G Massive MIMO transmitting antenna selection model according to a target problem, wherein the target problem comprises maximization of a sum capacity, maximization of a minimum characteristic value and maximization of an F norm of a transmission channel; introducing a diagonal matrix, and converting the selection model into a convex optimization problem model; and calculating and solving the convex optimization problem model to obtain an MIMO transmitting antenna selection scheme. According to the underground 5G Massive MIMO transmitting antenna selection method, the method can be used for maximizing the sum capacity, maximizing the minimum characteristic value, maximizing the F norm of the transmission channel and the like, and the underground 5G Massive MIMO transmitting antenna selection method has certain universality, low operation complexity and better solvability.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of antennas, in particular to a downhole 5G Massive MIMO transmitting antenna selection method. BACKGROUND

[0002] The downhole 5G Massive MIMO antenna in the 5G system, also known as a large-scale antenna array, has more orders of magnitude of antennas than the traditional MIMO, and the downhole 5G Massive MIMO can significantly improve the performances of link reliability, data rate and spectrum efficiency, but in actual application, the number of antennas used by the base station cannot be unlimited due to the physical size limitation and the complexity of implementation, especially in the narrow space available for deploying antennas in the coal mine, so the antenna selection technology of the base station becomes a research hotspot.

[0003] There are many criteria for antenna selection, such as maximizing the system capacity, maximizing the minimum channel eigenvalue, and maximizing the channel norm. The best algorithm for its implementation is the exhaustive search method, that is, all combinations of selecting N antennas from M antennas are traversed, and the optimal solution that meets certain criteria is calculated. This exhaustive search method is almost infeasible when the number of antennas is very large. In the traditional MIMO system with a small number of antennas, an iterative algorithm or an exhaustive search method is used to select the antenna combination with the maximum channel transmission matrix norm, but for a large number of transmitting and receiving antennas, these methods have too much computation and are infeasible.

[0004] Therefore, it is necessary to improve one or more problems existing in the above related technical solutions.

[0005] It should be noted that this section aims to provide background or context to the technical solutions of the application stated in the claims. The description herein is not admitted to be prior art merely because it is included in this section. SUMMARY

[0006] The purpose of the present application is to provide a downhole 5G Massive MIMO transmitting antenna selection method to at least improve at least one of the above technical problems.

[0007] The purpose of the present application is achieved by adopting the following technical solutions:

[0008] The application provides a downhole 5G Massive MIMO transmitting antenna selection method, comprising the following steps:

[0009] According to the target problem, a downhole 5G Massive MIMO transmitting antenna selection model is determined, and the target problem includes maximizing the capacity, maximizing the minimum eigenvalue, and maximizing the F-norm of the transmission channel;

[0010] By introducing a diagonal matrix, the selection model is transformed into a convex optimization problem model;

[0011] The convex optimization problem model is calculated and solved to obtain the MIMO transmit antenna selection scheme.

[0012] Preferably, the step of determining the downhole 5G Massive MIMO transmit antenna selection model based on the target problem includes:

[0013] The input-output relationship of the downhole 5G Massive MIMO transmit antenna system is described in vector form as follows:

[0014] y = Hs + n

[0015] In the formula, y=[y1 y2 y3 ... y K ] represents a K×1 received signal vector; H represents the channel matrix from the transmitter to the user, with a size of K×M, where K is the number of users per antenna and M is the number of base station transmit antennas; s=[s1 s2 s3 ...s K ];n=[n1 n2 n3 ... n K ] is a K×1 zero-mean complex Gaussian random vector, and the variance matrix of n is... σ is the noise variance, and I is the identity matrix.

[0016] Preferably, the channel matrix H is obtained using the following method:

[0017] The base station transmitter numbers all transmitting antennas sequentially.

[0018] Based on the known channel state information, construct the channel transmission matrix H from all transmit antennas of the base station to each user's single receive antenna.

[0019] Preferably, the diagonal matrix is ​​an M×M diagonal matrix γ, and its diagonal elements are γ. i (i = 1...M), indicating whether the i-th antenna is selected; and N is the number of transmitting antennas.

[0020] Preferably, the interior point algorithm is used to calculate and solve the convex optimization problem model.

[0021] Preferably, when the objective problem is to maximize sum and capacity, the convex optimization problem model is:

[0022]

[0023]

[0024] In the formula, I K It is an identity matrix.

[0025] Preferably, when the objective problem is to maximize the minimum eigenvalue, the convex optimization problem model is:

[0026]

[0027]

[0028] In the formula, λ min It is the smallest eigenvalue.

[0029] Preferably, a new variable t is introduced, let

[0030] t=λ m (H H γH)

[0031] Therefore, the convex optimization problem model is equivalent to the following model:

[0032]

[0033]

[0034] Preferably, when the objective problem is to maximize the F-norm of the transmission channel, the convex optimization problem model is:

[0035]

[0036]

[0037] The technical solutions provided by the embodiments of this application may include the following beneficial effects:

[0038] The above method for selecting 5G Massive MIMO transmit antennas in wells can be used to solve objective problems such as maximizing capacity, maximizing minimum eigenvalues, and maximizing the F-norm of the transmission channel. It has a certain degree of universality, low computational complexity, and better solvability.

[0039] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0040] The present application will be further described below with reference to the accompanying drawings and embodiments.

[0041] Figure 1 This is a schematic diagram of the structure of a downhole 5G Massive MIMO transmit antenna selection model in one embodiment of the present invention;

[0042] Figure 2This is a flowchart of a downhole 5G Massive MIMO transmit antenna selection method in one embodiment of the present invention;

[0043] Figure 3 This refers to the system bit error rate of various antenna selection methods in one embodiment of the present invention;

[0044] Figure 4 This refers to the system capacity of various antenna selection methods in one embodiment of the present invention. Detailed Implementation

[0045] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art. The described features may be combined in any suitable manner in one or more embodiments.

[0046] Furthermore, the accompanying drawings are merely illustrative of this application and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities.

[0047] This example implementation provides a method for selecting a downhole 5G Massive MIMO transmit antenna. Please refer to [reference needed]. Figure 1 The selection method includes the following steps:

[0048] Step S100: Determine the downhole 5G Massive MIMO transmit antenna selection model according to the target problem, wherein the target problem includes: maximizing the sum capacity, maximizing the minimum eigenvalue, and maximizing the F-norm of the transmission channel;

[0049] Step S200: Introduce a diagonal matrix to convert the selection model into a convex optimization problem model;

[0050] Step S300: The convex optimization problem model is calculated and solved to obtain the MIMO transmit antenna selection scheme.

[0051] In this embodiment, the above-mentioned downhole 5G Massive MIMO transmit antenna selection method can be used to solve objective problems such as maximizing capacity, maximizing minimum eigenvalue, and maximizing the F-norm of the transmission channel. It has a certain degree of universality, low computational complexity, and better solvability.

[0052] The following is a detailed description of each process in the above steps.

[0053] likeFigure 2 As shown, in the downlink of a 5G system, the base station has M transmit antennas and communicates with K single-antenna users. The transmitter at the base station of the Massive MIMO system's downlink knows the CSI completely. According to certain criteria, the best N (K < N << M) antennas are selected from the M antennas to serve the K users, forming a massive MIMO system.

[0054] The input-output relationship of the Massive MIMO system can be described in the following vector form:

[0055] y = Hs + n

[0056] where y = [y1 y2 y3... y K , representing the K×1 received signal vector; H represents the channel matrix from the transmitter to the users, with size K×M, where K is the number of single-antenna users and M is the number of base station transmit antennas; s = [s1 s2 s3... s K ; n = [n1 n2 n3... n K , a K×1 zero-mean complex Gaussian random vector, and the variance matrix of n is σ is the noise variance and I is the identity matrix.

[0057] Then perform initialization:

[0058] The transmitter at the base station numbers all the transmit antennas in sequence; according to the known channel state information, construct the channel transmission matrix H from all the transmit antennas at the base station to each single receive antenna of all users.

[0059] Perform antenna selection:

[0060] Solve the channel selection matrix according to the maximum channel capacity criterion; use the channel selection matrix to solve the numbers of the transmit antennas adopted; obtain the corresponding channel transmission matrix according to the selected channel numbers.

[0061] Introduce a diagonal matrix of size M×M, whose diagonal elements are:

[0062] γ i (i = 1... M)

[0063] γ i is a binary variable indicating whether the i-th antenna is selected. At the same time, it must satisfy N is the number of transmit antennas to meet the total transmit power constraint. Through the selection matrix γ, the optimization objective of maximizing the system capacity is:

[0064]

[0065] where, I KGiven the identity matrix, calculate and solve:

[0066] For large-scale MIMO systems where M may exceed 100, the sheer number of possible antenna combinations makes traversal searching difficult. Therefore, this application transforms the objective problem into a solvable convex optimization problem. This problem can be solved using an interior-point algorithm. This approach makes γ... i Convert it into a fraction, select N maximum values ​​from it, and their indices represent the antennas selected on the base station side.

[0067] Furthermore, when the objective problem is to maximize the minimum eigenvalue, the convex optimization problem model is:

[0068]

[0069]

[0070] In the formula, λ min It is the smallest eigenvalue.

[0071] Introduce a new variable t, let

[0072] t=λ min (H H γH)

[0073] Therefore, the convex optimization problem model is equivalent to the following model:

[0074]

[0075]

[0076] When the objective problem is to maximize the F-norm of the transmission channel, the convex optimization problem model is:

[0077]

[0078]

[0079] The effectiveness of the invention can be further illustrated by the following simulation experimental conditions:

[0080] To analyze the performance of the proposed algorithm, Monte Carlo simulations were performed on the algorithms for maximizing system capacity, maximizing the minimum eigenvalue of the channel matrix, maximizing total received power, and randomly selecting antennas, based on the unified signal processing model provided in this invention. The system capacity and bit error rate performance were compared. In the simulations, it was assumed that the downlink transmitter had 128 antennas, the receiver had 3 single-antenna users, the MIMO channel was a Rayleigh flat fading channel, and the user data was a unity-power BPSK signal. The base station had known CSI, and each precoding vector was ensured by its 2-norm |p k | 2 =1.

[0081] In this application, it can be seen from the antenna selection process under various criteria that common antenna selection problems can be designed using a unified signal processing model that incorporates an antenna selection matrix. Furthermore, the design problems can be transformed into typical convex optimization problems using a positive semidefinite relaxation method, and then solved using classical algorithms such as the interior-point method. This unified signal processing model and convex optimization solution method for various antenna selection methods are applicable to antenna selection methods that maximize system capacity, minimize eigenvalues, and maximize the total received signal power. Simulations can verify the feasibility of the proposed antenna selection methods (using N out of 128 antennas).

[0082] Figure 3 The system bit error rates for various antenna selection methods are given. Figure 4 The system capacity of various antenna selection methods is presented, with the maximization and capacity algorithms largely coinciding with the curves for maximizing the minimum eigenvalue. Table 1 shows the computational complexity of various algorithms. Among them, the method proposed in this application, which solves for maximizing system capacity, suffers a capacity loss of approximately 5% due to relaxation, while exhibiting lower complexity and better solvability than traditional iterative methods. This demonstrates that the signal processing model and solution method of this application possess high feasibility and universality.

[0083] Table 1. Comparison of Time Complexity of Different Algorithms (s)

[0084]

[0085] It should be noted that although the steps of the method in this application are described in a specific order in the accompanying drawings, this does not require or imply that these steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps. Furthermore, it is readily understood that these steps may be executed synchronously or asynchronously, for example, in multiple modules / processes / threads.

[0086] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for selecting a 5G Massive MIMO transmit antenna in a well, characterized in that, Includes the following steps: The downhole 5G Massive MIMO transmit antenna selection model is determined based on the target problems, which include: maximizing the sum and capacity, maximizing the minimum eigenvalue, and maximizing the F-norm of the transmission channel; By introducing a diagonal matrix, the selection model is transformed into a convex optimization problem model; The convex optimization problem model is calculated and solved to obtain the MIMO transmit antenna selection scheme.

2. The method for selecting a downhole 5G Massive MIMO transmit antenna according to claim 1, characterized in that, The steps for determining the downhole 5G Massive MIMO transmit antenna selection model based on the target problem include: The input-output relationship of the downhole 5G Massive MIMO transmit antenna system is described in vector form as follows: y = Hs + n In the formula, y=[y1 y2 y3 ... y K ] represents a K×1 received signal vector; H represents the channel matrix from the transmitter to the user, with a size of K×M, where K is the number of users per antenna and M is the number of base station transmit antennas; s=[s1 s2 s3 ... s K ];n=[n1 n2 n3 ... n K ] is a K×1 zero-mean complex Gaussian random vector, and the variance matrix of n is... σ is the noise variance, and I is the identity matrix.

3. The method for selecting a downhole 5G Massive MIMO transmit antenna according to claim 2, characterized in that, The method for obtaining the channel matrix H is as follows: The base station transmitter numbers all transmitting antennas sequentially. Based on the known channel state information, construct the channel transmission matrix H from all transmit antennas of the base station to each user's single receive antenna.

4. The method for selecting a downhole 5G Massive MIMO transmit antenna according to claim 3, characterized in that, The diagonal matrix is ​​an M×M diagonal matrix Y, with diagonal elements γ. i (i = 1...M), indicating whether the i-th antenna is selected; and N is the number of transmitting antennas.

5. The method for selecting a downhole 5G Massive MIMO transmit antenna according to claim 1, characterized in that, The interior point algorithm is used to calculate and solve the convex optimization problem model.

6. The method for selecting a downhole 5G Massive MIMO transmit antenna according to claim 4, characterized in that, When the objective problem is to maximize sum and capacity, the convex optimization problem model is: In the formula, I K It is an identity matrix.

7. The method for selecting a downhole 5G Massive MIMO transmit antenna according to claim 4, characterized in that, When the objective problem is to maximize the minimum eigenvalue, the convex optimization problem model is: In the formula, λ min It is the smallest eigenvalue.

8. The method for selecting a downhole 5G Massive MIMO transmit antenna according to claim 7, characterized in that, Introduce a new variable t, let t=λ min (H H (H) Therefore, the convex optimization problem model is equivalent to the following model:

9. The method for selecting a downhole 5G Massive MIMO transmit antenna according to claim 4, characterized in that, When the objective problem is to maximize the F-norm of the transmission channel, the convex optimization problem model is:

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