MIMO system pilot frequency power distribution and signal design method, storage medium and product

Abstract

The invention discloses an MIMO system pilot frequency power distribution and signal design method, a storage medium and a product, and belongs to the technical field of wireless communication. According to the method, the characteristic of MIMO channel space sparsity is fully utilized, maximization of channel state information is taken as a target, optimal pilot frequency power distribution output is realized, pilot frequency resources can be remarkably saved, and the power utilization efficiency and the channel estimation performance are improved. A pilot signal is designed on the basis, so that the calculation complexity of transmitting pre-coding and receiving beam forming can be reduced.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, and particularly relates to a method for pilot power allocation and signal design, storage medium and product for MIMO system. Background Technology

[0002] Multiple-input multiple-output (MIMO) technology can significantly increase the capacity of communication systems by utilizing spatial resources, and has become a core technology of mobile communication systems. Channel estimation is a prerequisite for coherent signal detection in MIMO systems. Currently, the commonly used design scheme is equal-power orthogonal pilots, which has low power utilization efficiency and requires the transmission of more pilot sequences, occupying limited pilot resources and affecting the improvement of channel estimation performance. Summary of the Invention

[0003] Purpose of the invention: The purpose of this invention is to provide a method, storage medium and product for pilot power allocation and signal design in MIMO systems, which solves the contradiction between the low utilization efficiency of existing equal-power orthogonal pilot power, the large amount of pilot resources required, and the limited pilot resources.

[0004] Technical solution: According to a first aspect of the present invention, a pilot power allocation method for a MIMO system is provided, comprising:

[0005] Obtain the estimated channel state matrix from the MIMO system;

[0006] Based on the covariance matrix and pilot matrix of the transmitting / receiving end generated by the channel state matrix, the MIMO channel state information (CSI) is obtained. The MIMO channel state information (CSI) is the mutual information between the channel state matrix and the received signal matrix.

[0007] With the goal of maximizing MIMO channel state information (CSI) and the normalization of pilot power allocation coefficients as a constraint, the optimal pilot power allocation coefficients are obtained.

[0008] Optionally, to fully utilize the sparsity of the MIMO channel for pilot power allocation coefficient optimization, the MIMO channel state information (CSI) can be constructed based on the pilot power allocation coefficients, the eigenvalues ​​of the covariance matrix, and the rank.

[0009] Preferably, the expression for the MIMO Channel State Information (CSI) is:

[0010]

[0011] in, Indicates the first Pilot power allocation coefficient; The first element of the sender's covariance matrix represents the... The eigenvalues, wherein the transmitting end covariance matrix is ​​the channel state matrix. Corresponding column covariance matrix ; The first element of the receiver covariance matrix represents the... The eigenvalues, wherein the receiver covariance matrix is ​​the channel state matrix. Corresponding row covariance matrix ; The length of each pilot sequence in the pilot matrix is ​​given. This represents the symbol signal-to-noise ratio of each receiving antenna. For row covariance matrix rank, For column covariance matrix rank, This is the received signal matrix.

[0012] Optionally, the objective of maximizing MIMO channel state information (CSI) includes:

[0013] Construct the objective function: , These are undetermined constants;

[0014] The constraints are as follows: .

[0015] Optionally, based on the channel state matrix, with the objective of maximizing MIMO channel state information (CSI) and using pilot power allocation coefficient normalization as a constraint, the optimal pilot power allocation coefficients are obtained, including:

[0016] According to the channel state matrix The corresponding row covariance matrix is ​​obtained. , column covariance matrix eigenvalues eigenvalues and rank rank The initial values ​​for the pilot power allocation coefficients are set as follows: The initial step size is l=0;

[0017]

[0018] Calculate pilot power allocation coefficient :

[0019] Repeatedly calculate the undetermined constants and pilot power allocation coefficient The steps continue until the desired result is achieved. The pilot power allocation coefficient at this time The iteration terminates as the result of the optimal pilot power allocation, where l is the iteration step size. This is the preset iteration precision.

[0020] A second aspect of the present invention provides a method for designing pilot signals for a MIMO system.

[0021] include:

[0022] At the sending end, the unitary matrix As a transmission precoding matrix;

[0023] According to the transmitted precoding matrix and pilot matrix Design pilot signals;

[0024] Among them, pilot power allocation coefficient ,in, Based on the above-mentioned pilot power allocation method for MIMO systems, the following is obtained: hour, , This refers to the number of antennas at the transmitting end. The initial pilot matrix; unitary matrix According to the channel state matrix column covariance matrix Obtained by eigenvalue decomposition; For column covariance matrix Rank.

[0025] Furthermore, the MIMO system pilot signal design method also includes:

[0026] At the receiving end, the unitary matrix As a receiving beamforming matrix, it is used for receiving beamforming;

[0027] Among them, unitary matrix According to the channel state matrix row covariance matrix It is obtained by eigenvalue decomposition.

[0028] A third aspect of the present invention provides a pilot power distribution device for a MIMO system, comprising:

[0029] The acquisition module is used to acquire the estimated channel state matrix from the MIMO system;

[0030] The allocation module is used to obtain the mutual information between the channel state matrix and the received signal matrix based on the covariance matrix of the transmitting / receiving end generated by the channel state matrix as statistical data, and use it as the MIMO channel state information (CSI); with the goal of maximizing the MIMO channel state information (CSI) and with the normalization of the pilot power allocation coefficient as a constraint, the optimal pilot power allocation coefficient is obtained.

[0031] A fourth aspect of the present invention provides a pilot signal device for a MIMO system, comprising:

[0032] The precoding module is used at the transmitting end to convert the unitary matrix... As a transmission precoding matrix;

[0033] The design module is used to design the precoding matrix and pilot matrix based on the transmission precoding matrix. Design pilot signals;

[0034] Among them, the pilot power allocation coefficient matrix ,in, Based on the above-mentioned pilot power allocation method for MIMO systems, the following is obtained: hour, , This refers to the number of antennas at the transmitting end. The initial pilot matrix; unitary matrix According to the channel state matrix column covariance matrix Obtained by eigenvalue decomposition; For column covariance matrix Rank.

[0035] Optionally, the MIMO system pilot signal device further includes:

[0036] Beamforming module, used at the receiver end, to convert a unitary matrix... As a receiving beamforming matrix, it is used for receiving beamforming.

[0037] A fifth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the MIMO system pilot power allocation method as described in any one of the embodiments of the present invention, and / or the MIMO system pilot signal design method as described in any one of them.

[0038] A sixth aspect of the present invention provides a computer program product comprising a computer program that, when executed by a processor, implements the MIMO system pilot power allocation method as described in any one of the embodiments of the present invention, and / or the MIMO system pilot signal design method as described in any one of them.

[0039] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0040] 1. The pilot power allocation method and apparatus for MIMO systems of the present invention uses the mutual information between the channel state matrix and the received signal matrix as the MIMO channel state information (CSI). By optimizing the pilot power allocation coefficients, the CSI is maximized, thereby improving the performance of MIMO channel estimation.

[0041] 2. The MIMO Channel State Information (CSI) model of this invention can be constructed based on the pilot power allocation coefficients, the eigenvalues ​​and rank of the row and column covariance matrices corresponding to the channel state matrix, thereby fully utilizing the sparsity of the MIMO channel. The optimal pilot power allocation coefficients obtained therefrom, compared with the currently commonly used equal power allocation, can significantly improve the utilization rate of channel state information, power, and pilot resources in the MIMO system.

[0042] 3. The MIMO system pilot signal design method and apparatus of the present invention, for spatially sparse channels, since the number of non-zero eigenvalues ​​is much smaller than the number of antennas, only the first few eigenvalues ​​in the unitary matrix constituting the pilot signal are present. The row participates in the calculation and completes the precoding function; similarly, only the first row of the unitary matrix that constitutes the receiving beamforming matrix contains the first row. The column participates in the calculation and completes the receiving beamforming function, among which and They are column covariance matrices Covariance matrix Therefore, this invention employs a novel pilot matrix design for the pilot signal, significantly reducing the computational complexity of transmit precoding and receive beamforming.

[0043] 4. The storage medium provided by the present invention can execute any step of the pilot power allocation method and / or pilot signal design method of the MIMO system provided by the present invention, and has the corresponding beneficial effects of executing the method. Attached Figure Description

[0044] Figure 1 This is a schematic flowchart of the pilot power allocation method of the present invention;

[0045] Figure 2 This is a flowchart illustrating the pilot signal design method of the present invention;

[0046] Figure 3 This is a schematic diagram of the structure of an electronic device in one embodiment;

[0047] Figure 4 A graph showing the relationship between CSI and SNR of a MIMO system using equal power allocation and the method of the present invention in one embodiment;

[0048] Figures 5(a) and 5(b) show the relationship between CSI and the number of antennas under different SNR conditions in one embodiment using equal power allocation and the method of the present invention, respectively. Detailed Implementation

[0049] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0050] The derivation process of the expression for the MIMO Channel State Information (CSI) constructed in this invention is as follows.

[0051] (1) For quasi-static block fading channels, construct a MIMO channel estimation model.

[0052] Assume the MIMO transmitter has There are one antenna, and the receiver has... There are one antenna. The data stream is transmitted in blocks, and the channel is a quasi-static block fading channel, meaning the channel gain remains constant within a transmission block and is independent between blocks. Meanwhile, to facilitate defining the symbol signal-to-noise ratio for each receiving antenna... The MIMO channel estimation system model is constructed as follows:

[0053] (1)

[0054] in, yes × The channel state matrix, within a transport block, It is a constant matrix. It consists of pilot sequences × Pilot matrix, The The line is the first The pilot sequence transmitted by the antenna has a length of [length missing]. The pilot matrix is ​​known at both the transmitting and receiving ends. yes × The noise matrix consists of elements that are independent and identically distributed complex Gaussian random variables with zero mean. It was received. × Received signal matrix, due to It is a constant matrix. It is also a complex Gaussian random matrix.

[0055] set up The average power of the symbol in the middle is 1 unit, that is... , and The average power of the symbols in the middle is 1 unit. Therefore, in equation (1)... It refers to the symbol signal-to-noise ratio of each receiving antenna.

[0056] (2) Based on the above MIMO channel estimation system model, construct the MIMO channel state statistical model and the MIMO channel model respectively.

[0057] Due to the noise matrix The elements are independent and identically distributed complex Gaussian random variables with zero mean and unit power. The probability distribution is as follows:

[0058] (2)

[0059] in, Let the trace of the matrix be represented. Substituting equation (1) into the above equation, we obtain the trace under the channel state. Downward receiving signal Conditional distribution model:

[0060] (3)

[0061] The conditional distribution in the above equation is the MIMO channel model, which is a special matrix Gaussian distribution.

[0062] The construction of the MIMO channel state statistical model includes the following steps:

[0063] First, the source of information for channel estimation is the channel state matrix. The calculation formula is as follows:

[0064] (4)

[0065] In the formula, It is a vector composed of the complex gain of the scattering path. These represent the receive array response matrix and the transmit array response matrix, respectively. and These represent the receiver array response vector and the transmitter array response vector, respectively. This is called spatial frequency. These represent the antenna spacing, wavelength, angle of arrival (DOA), and angle of departure (AOA), respectively. L represents the total number of scattering paths.

[0066] Assuming the scattered signal follows a complex Gaussian distribution and that different paths are uncorrelated, then... ; in, Indicates the first Complex gain of each scattering path; Indicates the first The power gain of each scattering path, then Let represent the scattered power vector. For ease of description, let From equation (4), row covariance matrix Covariance matrix They are respectively:

[0067] (5)

[0068] (6)

[0069] in, yes × The row covariance matrix describes the spatial correlation at the receiver. yes × The column covariance matrix describes the spatial correlation of the transmitter. Their traces are:

[0070] (7)

[0071] (8)

[0072] Let the first Complex gain of each scattering path If the channel state follows a complex Gaussian distribution with a mean of zero, then the statistical model of the MIMO channel state, i.e., the channel state... The probability density function (PDF) has a mean of The matrix Gaussian distribution:

[0073] (9)

[0074] (3) Based on the above MIMO channel model, we can obtain Based on the above MIMO channel state statistical model, we can obtain Then, the closed-form expression for the MIMO channel state information (CSI) is derived.

[0075] First, the MIMO channel state information (CSI) is a channel state matrix. and received signal matrix Mutual information between It reflects the theoretical performance of channel estimation and can be expressed as the conditional distribution of the received signal under a given channel condition (i.e., the MIMO channel model). Edge distribution of received signal The mathematical expectation of the relative entropy between them is shown in the following formula:

[0076] (10)

[0077] In the formula, , (·) is the prior probability density function.

[0078] Based on the properties of mutual information, . The differential entropy of the noise matrix is ​​given by the following formula:

[0079] (11)

[0080] The differential entropy of the randomly received signal matrix is ​​derived below. Given a pilot matrix, the received signal follows a Gaussian distribution. yes Linear transformation, based on the properties of the Gaussian distribution of the matrix, The row covariance matrix is The covariance matrix is Therefore, the differential entropy of the received signal is:

[0081] (12)

[0082] In the formula, Let Kronecker product be represented. From equations (11) and (12), we obtain the closed-form expression for CSI:

[0083] (13)

[0084] in, express OK The identity matrix of columns.

[0085] Assume the pilot sequences are mutually orthogonal, let ,here It is to satisfy The pilot power allocation vector, It is the first of the sending end The pilot power allocation coefficients for each antenna are given, and the total pilot power satisfies the following:

[0086] (14)

[0087] Substituting formula (14) into formula (13), we obtain the closed-form expression for Channel State Information (CSI):

[0088] (15)

[0089] in, The signal-to-noise ratio of the sequence. This represents the symbol signal-to-noise ratio of each receiving antenna. is the length of the pilot sequence.

[0090] When distributing power equally ,

[0091] (16)

[0092] When the covariance matrix of the channel is , hour,

[0093] (17)

[0094] The above formula is The capacity of an independent channel. This is because, when , At that time, the perceived source of information is Independent Gaussian source, the channel is For independent complex AWGN channels, the information state information corresponds to the Shannon channel capacity. The above analysis shows that information state information is a performance metric for channel estimators, providing a new approach to channel estimator design.

[0095] Perform eigenvalue decomposition on the covariance matrix:

[0096] (18)

[0097] (19)

[0098] in, They correspond to eigenvectors, and It is a unitary matrix. Since the determinant of a unitary matrix has a magnitude of 1, substituting formulas (18) and (19) into formula (15) further yields:

[0099] (20)

[0100] The CSI closure expression above takes the eigenvalue vector and pilot power distribution vector as parameters.

[0101] set up and The ranks are respectively and Due to the spatial sparsity of MIMO arrays, the rank generally satisfies and Expanding equation (20), we obtain CSI as:

[0102] (twenty one)

[0103] in, The pilot power allocation vector is represented by the first... Each component (i.e., pilot power allocation coefficient) and They are The One and The Each feature value.

[0104] The above equation shows that the MIMO channel estimation system is equivalent to Independent parallel channels, due to channel state It is a source of information awaiting estimation; the information state information (CSI) equals... The sum of the capacities of independent parallel channels.

[0105] (4) But the above The signal-to-noise ratio (SNR) of independent parallel channels is not completely independent. Therefore, it is necessary to optimize the distribution of SNR across these independent parallel channels with the goal of maximizing CSI, thereby allocating the optimal SNR to different pilot sequences. This SNR optimization problem is also equivalent to the optimal allocation of pilot power, thus yielding the optimal pilot power allocation result.

[0106] only The SNR of each channel is configurable. Since all channels are assigned the same SNR, the objective optimization is to maximize the Channel State Information (CSI) of the MIMO channel, taking into account the total power constraint. To facilitate obtaining the optimized power allocation result, the pilot power allocation coefficients are normalized as a constraint. , Construct the following objective function:

[0107] (twenty two)

[0108] In the formula It is an undetermined constant.

[0109] right Find the partial derivative and let have to

[0110] (twenty three)

[0111] in For characteristic vectors No. The component, that is, the first component of the transmitter. One eigenvalue; For characteristic vectors No. The first component, that is, the first component received by the receiver. Each feature value.

[0112] Find the second-order partial derivatives, we have ,so It is a convex function, and its maximum value must be its maximum value.

[0113] Combine the above equation (23) with the constraint equation Solving together, we get:

[0114] (twenty four)

[0115] Divide equation (24) by Based on the constraints of the pilot power allocation coefficient, we can obtain...

[0116] (25)

[0117] The above formula is an iterative formula, and based on this, the following pilot power allocation algorithm is proposed in this application:

[0118] Step (1): Initialization , , , Symbol signal-to-noise ratio Let the configurable pilot power allocation coefficient be... The initial value is Iteration accuracy The step size l=0; where, according to the channel state matrix Statistical values ​​(for example, can be) (Statistical average) Initialize the row covariance matrix of the channel state Covariance matrix This leads to the initialization of the corresponding feature vector. eigenvectors and rank and rank Since the system's average SNR is a known quantity, therefore It is also a known quantity;

[0119] Step (2): Calculate using equation (24) ;

[0120] Step (3): Calculate using equation (25) ;

[0121] Step (4): Increase the iteration step size, l l+1, repeat steps (2) and (3) until The obtained pilot power allocation coefficients are then taken as the optimal pilot power allocation result.

[0122] Finally, a set was obtained value This is the optimal pilot power allocation result.

[0123] Example 1

[0124] Based on the above reasoning process of this invention, this invention proposes a pilot power allocation method for MIMO systems, such as... Figure 1 As shown, it includes:

[0125] S11. Obtain the estimated channel state matrix from the MIMO system;

[0126] S12. Based on the covariance matrix and pilot matrix of the transmitting / receiving end generated by the channel state matrix, the MIMO channel state information (CSI) is obtained; the MIMO channel state information (CSI) is the mutual information between the channel state matrix and the received signal matrix.

[0127] S13. With the goal of maximizing MIMO channel state information (CSI) and the normalization of pilot power allocation coefficients as a constraint, the optimal pilot power allocation coefficients are obtained.

[0128] In some specific implementations, in step S11, the channel state matrix can be obtained from the channel estimation module of the MIMO system. The channel estimation algorithm that the MIMO system's channel estimation module can use includes, but is not limited to, least squares (LS) and minimum mean square error (MMSE). Since the channel estimation algorithm itself and its selection are not the focus of this invention, no limitations are imposed. The focus of this invention is on how to analyze the obtained channel state matrix to obtain the optimal pilot power allocation and pilot signal design, thereby achieving the technical objectives of high power utilization, low pilot resource consumption, and low computational complexity.

[0129] Furthermore, considering the need to obtain the optimized results of the pilot power allocation coefficients, in order to make full use of the sparsity of the MIMO channel, the MIMO channel state information (CSI) can be constructed based on the pilot power allocation coefficients, the eigenvalues ​​and rank of the row and column covariance matrices corresponding to the channel state matrix.

[0130] In one specific implementation, the expression for the MIMO Channel State Information (CSI) is:

[0131]

[0132] in, Indicates the first Pilot power allocation coefficient; The first element of the sender's covariance matrix represents the... The eigenvalues, wherein the transmitting end covariance matrix is ​​the channel state matrix. Corresponding column covariance matrix ; The first element of the receiver covariance matrix represents the... Eigenvalues, wherein the receiver covariance matrix is ​​the row covariance matrix corresponding to the channel state matrix. ; The length of each pilot sequence in the pilot matrix is ​​given. This represents the symbol signal-to-noise ratio of each receiving antenna. For row covariance matrix rank, For column covariance matrix rank, This is the received signal matrix.

[0133] In one specific implementation, the objective of maximizing MIMO channel state information (CSI) includes:

[0134] Construct the objective function: ,

[0135] The constraints are as follows: .

[0136] In one specific implementation, based on the channel state matrix, with the objective of maximizing MIMO channel state information (CSI) and using pilot power allocation coefficient normalization as a constraint, the optimal pilot power allocation coefficients are obtained, including:

[0137] Based on the above channel state statistical model, and according to the channel state matrix The corresponding row covariance matrix is ​​obtained. , column covariance matrix eigenvalues eigenvalues and rank ,rank The initial values ​​for the pilot power allocation coefficients are set as follows: The initial step size is l=0;

[0138] Calculate the undetermined constants :

[0139] Calculate pilot power allocation coefficient :

[0140] Repeatedly calculate the undetermined constants and pilot power allocation coefficient The steps continue until the desired result is achieved. The pilot power allocation coefficient at this time The iteration terminates as the result of the optimal pilot power allocation, where l is the iteration step size. This is the preset iteration precision.

[0141] The symbols or formulas in the above embodiments are represented in the same way as the characters or formulas in the above reasoning process.

[0142] Example 2

[0143] This invention proposes a method for designing pilot signals for MIMO systems, comprising:

[0144] At the sending end, the unitary matrix As a transmission precoding matrix;

[0145] According to the transmitted precoding matrix and pilot matrix Design pilot signals;

[0146] Among them, the pilot power allocation coefficient matrix ,in, Based on the MIMO system pilot power allocation method in the above embodiments or examples, the following method was obtained. hour, , This refers to the number of antennas at the transmitting end. The initial pilot matrix; unitary matrix According to the channel state matrix column covariance matrix Obtained by eigenvalue decomposition; For column covariance matrix Rank.

[0147] In one specific embodiment, the method of the present invention further includes:

[0148] At the receiving end, the unitary matrix As a receiving beamforming matrix, it is used for receiving beamforming;

[0149] Among them, unitary matrix According to the channel state matrix row covariance matrix Obtained by eigenvalue decomposition; for row covariance matrix Rank.

[0150] Specifically, let the initial pilot matrix be... Any A row orthogonal matrix, where the power of each row sequence is equal to M units. Pilot matrix. satisfy . There are multiple ways to generate a matrix, such as from... Choose any DFT matrix row composition A row orthogonal matrix, or an equal-power orthogonal matrix recommended by mobile communication standards.

[0151] Let the new pilot matrix ,here The unitary matrix is ​​obtained from the power allocation method in any of the above embodiments. Depend on column covariance matrix This is obtained by eigenvalue decomposition. Therefore:

[0152]

[0153] The above formula shows It is a row orthogonal matrix, the first row... The power of the line equals Total power equals Remain unchanged. Here Its function is to perform pilot power allocation. Unitary matrix Its function is to precode the pilot sequence at the transmitting end. Precoding is equivalent to transmitting beamforming, which directs the transmitted power toward the direction of the characteristic domain scatterer.

[0154] Further, there are:

[0155]

[0156] Substituting into equation (13), we have

[0157]

[0158] The above equation is the same as equation (20), indicating that the designed pilot signal meets the requirements. At the receiving end, receiving beamforming is performed, i.e. ,here It is the unitary matrix obtained by eigenvalue decomposition of the transmit covariance matrix U. The equivalent receive covariance matrix obtained after beamforming. . The matrix directs the receiving beam toward the direction of the scatterer in the characteristic domain. Because... It is a unitary matrix. It does not affect the statistical properties of the noise.

[0159] Due to the sparsity of MIMO arrays, According to the power allocation algorithm, when hour, Therefore, only the first few elements of the unitary matrix Q actually participate in the precoding operation. OK Then utilize Design pilot signals, with the number of pilot sequences starting from... Descending to This not only saves pilot resources, but also reduces the complexity of transmission precoding.

[0160] Similarly, since only The former A unitary matrix with non-zero elements. In reality, only the front-end beamforming part actually participates in the receiving beamforming operation. List Using it for receiving beamforming can reduce computational complexity.

[0161] In summary, this invention leverages the spatial sparsity of MIMO channels to maximize CSI through transmit precoding, pilot power allocation, and receive beamforming. This improves power utilization, conserves pilot resources, and reduces the complexity of transmit precoding and receive beamforming.

[0162] Example 3

[0163] This invention proposes a pilot power allocation device for a MIMO system, comprising:

[0164] The acquisition module is used to acquire the estimated channel state matrix from the MIMO system;

[0165] The allocation module is used to obtain the mutual information between the channel state matrix and the received signal matrix based on the covariance matrix of the transmitting / receiving end generated by the channel state matrix as statistical data, and use it as the MIMO channel state information (CSI); with the goal of maximizing the MIMO channel state information (CSI) and with the normalization of the pilot power allocation coefficient as a constraint, the optimal pilot power allocation coefficient is obtained.

[0166] This invention proposes a pilot signal device for a MIMO system, comprising:

[0167] The precoding module is used at the transmitting end to transmit the unitary matrix Q as the precoding matrix;

[0168] The design module is used to determine the transmission precoding matrix Q and the pilot matrix. Design pilot signals;

[0169] Among them, the pilot power allocation coefficient matrix ,,in, Based on the above-mentioned pilot power allocation method for MIMO systems, the following is obtained: hour, , This refers to the number of antennas at the transmitting end. The initial pilot matrix; unitary matrix According to the channel state matrix column covariance matrix Obtained by eigenvalue decomposition; For column covariance matrix Rank.

[0170] In one specific embodiment, the MIMO system pilot signal device further includes:

[0171] Beamforming module, used at the receiver end, to convert a unitary matrix... As a receiving beamforming matrix, it is used for receiving beamforming.

[0172] The present invention proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the MIMO system pilot power allocation method as described in any of the above embodiments, and / or the MIMO system pilot signal design method as described in any of the above embodiments.

[0173] This invention proposes a computer program product, which includes a computer program that, when executed by a processor, implements the MIMO system pilot power allocation method as described in any of the above embodiments, and / or the MIMO system pilot signal design method as described in any of the above embodiments.

[0174] Figure 3 A schematic diagram of an electronic device that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers or mobile devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the invention described and / or claimed herein.

[0175] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0176] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0177] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as MIMO system pilot power allocation methods and / or pilot signal design methods.

[0178] In some embodiments, the MIMO system pilot power allocation method and pilot signal design method can both be implemented as computer programs tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the MIMO system pilot power allocation method and pilot signal design method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the MIMO system pilot power allocation method and pilot signal design method by any other suitable means (e.g., by means of firmware).

[0179] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0180] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0181] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0182] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0183] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0184] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0185] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0186] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

[0187] Experimental comparison:

[0188] The effectiveness of this invention will be verified below for a 16*16 MIMO antenna. Those skilled in the art should understand that the foregoing limitations are merely illustrative, and this patent is not limited to this specific application scenario.

[0189] Numerical simulations are performed to obtain the channel state information from the MIMO channel estimation. For simplicity, the channel gain is normalized. Assume there are two scattering paths between the transmitting and receiving arrays, with AOA and DOA as follows: , ; , .

[0190] The relationship between CSI and SNR of a 16×16 MIMO system under equal power / optimal power allocation is as follows: Figure 4 The horizontal axis The vertical axis I represents the symbol signal-to-noise ratio (SNR) of each receiving antenna, and the vertical axis I represents the MIMO channel state information (CSI). It can be seen that the CSI increases linearly with the logarithm of the SNR, with a slope of approximately 13 bits / dB. When CSI = 40 bits, the optimal power allocation results in an SNR gain of approximately 9 dB compared to the equal power allocation. When SNR = 10 dB, the optimal power allocation increases the CSI by approximately 12 bits compared to the equal power allocation. This demonstrates that the optimal pilot power allocation method can significantly improve the channel state information of MIMO systems under spatially sparse conditions.

[0191] The relationship between CSI and the number of antennas for different SNRs is shown in Figures 5(a) and 5(b). Simulation results show that CSI generally increases monotonically with the number of antennas, increasing faster when the number of antennas is small, and almost linearly with the number of antennas when the number is large. Under all antenna count conditions, power allocation can significantly increase CSI. This is because as the number of antennas increases, the beamwidth of the array narrows, improving the space utilization of power and thus enhancing CSI performance.

[0192] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0193] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0194] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

[0195] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A pilot power allocation method for a MIMO system, characterized in that, include: Obtain the estimated channel state matrix from the MIMO system; The MIMO channel state information (CSI) is obtained from the covariance matrix and pilot matrix of the transmitting / receiving end generated by the channel state matrix; the MIMO channel state information (CSI) is the mutual information between the channel state matrix and the received signal matrix. With the goal of maximizing MIMO channel state information (CSI) and the normalization of pilot power allocation coefficients as a constraint, the optimal pilot power allocation coefficients are obtained.

2. The pilot power allocation method for a MIMO system according to claim 1, characterized in that, The expression for the MIMO Channel State Information (CSI) is: in, Indicates the first Pilot power allocation coefficient; The first element of the sender's covariance matrix represents the... The eigenvalues, wherein the transmitting end covariance matrix is ​​the channel state matrix. Corresponding column covariance matrix ; The first element of the receiver covariance matrix represents the... Eigenvalues, wherein the receiver covariance matrix is ​​the channel state matrix. Corresponding row covariance matrix ; The length of each pilot sequence in the pilot matrix is ​​given. This represents the symbol signal-to-noise ratio of each receiving antenna. For row covariance matrix rank, For column covariance matrix rank, This is the received signal matrix.

3. The pilot power allocation method for a MIMO system according to claim 2, characterized in that, The objective of maximizing MIMO channel state information (CSI) includes: Construct the objective function: , The constraints are as follows: .

4. The pilot power allocation method for a MIMO system according to claim 3, characterized in that, Based on the channel state matrix, with the objective of maximizing MIMO channel state information (CSI) and using pilot power allocation coefficient normalization as a constraint, the optimal pilot power allocation coefficients are obtained, including: According to the channel state matrix The corresponding row covariance matrix is ​​obtained. , column covariance matrix eigenvalues eigenvalues and rank rank The initial values ​​for the pilot power allocation coefficients are set as follows: The initial step size is l=0; Calculate the undetermined constants : Calculate pilot power allocation coefficient : Repeatedly calculate the undetermined constants and pilot power allocation coefficient The steps continue until the desired result is achieved. The pilot power allocation coefficient at this time The iteration terminates as the result of the optimal pilot power allocation, where l is the iteration step size. This is the preset iteration precision.

5. A method for designing pilot signals for a MIMO system, characterized in that, include: At the sending end, the unitary matrix As a transmission precoding matrix; According to the transmitted precoding matrix and pilot matrix Design pilot signals; Among them, the pilot power allocation coefficient matrix ,in, Based on the pilot power allocation method for the MIMO system according to any one of claims 1-4, the following method was obtained. hour, , This refers to the number of antennas at the transmitting end. The initial pilot matrix; unitary matrix According to the channel state matrix column covariance matrix Obtained by eigenvalue decomposition; For column covariance matrix Rank.

6. The MIMO system pilot signal design method according to claim 5, characterized in that, Also includes: At the receiving end, the unitary matrix As a receiving beamforming matrix, it is used for receiving beamforming; Among them, unitary matrix According to the channel state matrix row covariance matrix It is obtained by eigenvalue decomposition.

7. A pilot power distribution device for a MIMO system, characterized in that, include: The acquisition module is used to acquire the estimated channel state matrix from the MIMO system; The allocation module is used to obtain the mutual information between the channel state matrix and the received signal matrix based on the covariance matrix of the transmitting / receiving end generated by the channel state matrix as statistical data, and use it as MIMO channel state information (CSI). With the goal of maximizing MIMO channel state information (CSI) and the normalization of pilot power allocation coefficients as a constraint, the optimal pilot power allocation coefficients are obtained.

8. A pilot signal device for a MIMO system, characterized in that, include: The precoding module is used at the transmitting end to convert the unitary matrix... As a transmission precoding matrix; The design module is used to design the precoding matrix and pilot matrix based on the transmission precoding matrix. Design pilot signals; Among them, the pilot power allocation coefficient matrix ,in, Based on the pilot power allocation method for the MIMO system according to any one of claims 1-4, the following method was obtained. hour, , This refers to the number of antennas at the transmitting end. The initial pilot matrix; unitary matrix According to the channel state matrix column covariance matrix Obtained by eigenvalue decomposition; For column covariance matrix Rank.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the MIMO system pilot power allocation method as described in any one of claims 1 to 4, and / or the MIMO system pilot signal design method as described in any one of claims 5 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the MIMO system pilot power allocation method as described in any one of claims 1 to 4, and / or the MIMO system pilot signal design method as described in any one of claims 5 to 6.

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