An antenna system precoding method, apparatus, device, storage medium and product

Abstract

The application discloses an antenna system precoding method, device, equipment, storage medium and product, and the method comprises the following steps: obtaining an initial uplink channel matrix, an access point side calibration coefficient matrix and a user equipment side calibration coefficient matrix; determining a corrected and compensated uplink channel according to the access point side calibration coefficient matrix and the user equipment side calibration coefficient matrix; determining a calibration coefficient error statistical characteristic parameter; obtaining a target precoding matrix according to the calibration coefficient error statistical characteristic parameter, a corrected and compensated downlink channel and a set precoding algorithm. The antenna system precoding method disclosed by the application introduces the calibration coefficient error statistical characteristic parameter, eliminates the influence of the uncertainty of the calibration coefficient error, and makes the precoding method not appear obvious performance decline even in the case of existing large calibration delay.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to an antenna system precoding method, apparatus, device, storage medium, and product. Background Technology

[0002] In a noncellular massive MIMO (CF-mMIMO) system, the access point (AP) is connected to the central processing unit (CPU) via a wired connection. The CPU needs to use downlink channel information to calculate the precoding matrix. To avoid downlink channel feedback, it is usually assumed that the system operates in TDD (Time-division Duplex) mode, utilizing the reciprocity of uplink and downlink channels over the air interface to obtain the downlink channel based on the uplink channel.

[0003] However, ideal channel reciprocity is limited to the air interface channel between the access point (AP) antenna and the user equipment (UE) antenna. Generally, the AP's transmit and receive chains use different RF circuits, leading to inconsistencies in uplink and downlink channel gain coefficients, thus rendering the channel reciprocal. To restore reciprocity between uplink and downlink channels, a calibration scheme can compensate for the gain difference. However, due to distributed deployment, different APs often use independent oscillators, and due to environmental changes (e.g., temperature, humidity), the phase drift between these independent oscillators exhibits time-varying characteristics, ultimately causing the gain difference between uplink and downlink channels to change over time.

[0004] In air interface calibration schemes, APs (Access Points) exchange calibration reference signals, or APs and UEs (User Equipments). The AP or UE estimates the uplink and downlink channels from the calibration reference signals and performs channel feedback. Finally, the AP derives the gain difference between the uplink and downlink channels through channel feedback and channel estimation; this gain difference is called the calibration coefficient. Clearly, in air interface calibration schemes, the calibration coefficient reflects the gain difference between the uplink and downlink channels at the time the calibration reference signal is transmitted. In practical systems, there is typically a delay of several milliseconds between the transmission time of the calibration reference signal and the time of downlink data transmission; this delay is called the calibration delay. For low-cost deployed APs, a calibration delay of several milliseconds is sufficient to cause significant errors in the calibration coefficient. Traditional precoding methods require precise downlink channel information to eliminate interference between multiple UEs. In this case, the reciprocity of the uplink and downlink channels is difficult to guarantee, and the performance of precoding is significantly affected. Summary of the Invention

[0005] This invention provides an antenna system precoding method, apparatus, device, storage medium, and product to overcome the impact of calibration delay on precoding performance.

[0006] According to one aspect of the present invention, an antenna system precoding method is provided, comprising:

[0007] Obtain the initial uplink channel matrix, the access point-side calibration coefficient matrix, and the user equipment-side calibration coefficient matrix; determine the corrected and compensated uplink channel based on the access point-side calibration coefficient matrix and the user equipment-side calibration coefficient matrix.

[0008] The calibration coefficient error statistical characteristic parameters are determined. Based on the calibration coefficient error statistical characteristic parameters and the corrected and compensated uplink channel, and combined with the set precoding algorithm, the target precoding matrix is ​​obtained.

[0009] Furthermore, the precoding algorithm includes a zero-forcing precoding algorithm, which, based on the calibration coefficient error statistical characteristic parameters and the corrected uplink channel, and in conjunction with the precoding algorithm, yields the target precoding matrix, including:

[0010] The first target norm is the norm of the difference between the product of the precoding matrix and the downlink channel matrix and the identity matrix; wherein the downlink channel matrix is ​​related to the corrected and compensated uplink channel.

[0011] According to the zero-forcing precoding algorithm, the expectation of the first target norm with respect to the calibration coefficient error statistical characteristic parameter is taken as the first target expectation function;

[0012] The precoding matrix that minimizes the first target expectation function is determined as the target precoding matrix.

[0013] Furthermore, after obtaining the target precoding matrix, the process also includes:

[0014] Determine the power constraint factor, and then use the product of the power constraint factor and the target precoding matrix to determine the actual precoding matrix.

[0015] Furthermore, the power constraint factor is determined based on the maximum transmitted signal power of the signal transmitter and the initial transmitted signal power of the signal transmitter before precoding.

[0016] Further, the precoding algorithm includes the Wiener precoding algorithm, which, based on the calibration coefficient error statistical characteristic parameters and the corrected uplink channel, and in conjunction with the precoding algorithm, yields the target precoding matrix, including:

[0017] The product of the received signal and the gain factor is taken as the first product, and the norm of the difference between the transmitted signal and the first product is taken as the second target norm; wherein, the received signal is related to the corrected and compensated uplink channel;

[0018] According to the Wiener precoding algorithm, the expectation of the second target norm with respect to the calibration coefficient error statistical characteristic parameter, the transmitted signal, and the receiver noise is used as the second target expectation function;

[0019] The precoding matrix that minimizes the second objective expectation function and satisfies the power constraint is determined as the target precoding matrix.

[0020] Further, the precoding algorithm includes a minimum mean square error precoding algorithm, which, based on the calibration coefficient error statistical characteristic parameters and the corrected uplink channel, and in conjunction with the precoding algorithm, yields the target precoding matrix, including:

[0021] The norm of the difference between the transmitted signal and the received signal is used as the third target norm; wherein, the received signal is related to the corrected and compensated uplink channel;

[0022] According to the minimum mean square error precoding algorithm, the expectation of the third target norm with respect to the calibration coefficient error statistical characteristic parameter, the transmitted signal, and the receiver noise is used as the third target expectation function;

[0023] The precoding matrix that minimizes the third objective expectation function and satisfies the power constraint is determined as the objective precoding matrix.

[0024] According to another aspect of the present invention, an antenna system precoding apparatus is provided, comprising:

[0025] The corrected and compensated uplink channel determination module is used to obtain the initial uplink channel matrix, the access point-side calibration coefficient matrix and the user equipment-side calibration coefficient matrix, and determine the corrected and compensated uplink channel based on the access point-side calibration coefficient matrix and the user equipment-side calibration coefficient matrix;

[0026] The target precoding matrix determination module is used to determine the calibration coefficient error statistical characteristic parameters, and obtain the target precoding matrix based on the calibration coefficient error statistical characteristic parameters and the corrected and compensated uplink channel, combined with the set precoding algorithm.

[0027] Optionally, the precoding algorithm includes a zero-forcing precoding algorithm, and the target precoding matrix determination module is further used for:

[0028] The first target norm is the norm of the difference between the product of the precoding matrix and the downlink channel matrix and the identity matrix; wherein the downlink channel matrix is ​​related to the corrected and compensated uplink channel.

[0029] According to the zero-forcing precoding algorithm, the expectation of the first target norm with respect to the calibration coefficient error statistical characteristic parameter is taken as the first target expectation function;

[0030] The precoding matrix that minimizes the first target expectation function is determined as the target precoding matrix.

[0031] Optionally, the apparatus further includes an actual precoding matrix determination module, used to determine a power constraint factor and determine the product of the power constraint factor and the target precoding matrix as the actual precoding matrix.

[0032] Optionally, the power constraint factor is determined based on the maximum transmitted signal power of the signal transmitter and the initial transmitted signal power of the signal transmitter before precoding.

[0033] Optionally, the precoding algorithm is defined as the Wiener precoding algorithm, and the target precoding matrix determination module is further configured to:

[0034] The product of the received signal and the gain factor is taken as the first product, and the norm of the difference between the transmitted signal and the first product is taken as the second target norm; wherein, the received signal is related to the corrected and compensated uplink channel;

[0035] According to the Wiener precoding algorithm, the expectation of the second target norm with respect to the calibration coefficient error statistical characteristic parameter, the transmitted signal, and the receiver noise is used as the second target expectation function;

[0036] The precoding matrix that minimizes the second objective expectation function and satisfies the power constraint is determined as the target precoding matrix.

[0037] Optionally, the precoding algorithm includes a minimum mean square error precoding algorithm, and the target precoding matrix determination module is further used for:

[0038] The norm of the difference between the transmitted signal and the received signal is used as the third target norm; wherein, the received signal is related to the corrected and compensated uplink channel;

[0039] According to the minimum mean square error precoding algorithm, the expectation of the third target norm with respect to the calibration coefficient error statistical characteristic parameter, the transmitted signal, and the receiver noise is used as the third target expectation function;

[0040] The precoding matrix that minimizes the third objective expectation function and satisfies the power constraint is determined as the objective precoding matrix.

[0041] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0042] At least one processor; and

[0043] A memory communicatively connected to the at least one processor; wherein,

[0044] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the antenna system precoding method according to any embodiment of the present invention.

[0045] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the antenna system precoding method according to any embodiment of the present invention.

[0046] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program / instructions, which, when executed by a processor, implement the steps of the antenna system precoding method according to any embodiment of the present invention.

[0047] The antenna system precoding method disclosed in this invention first obtains an initial uplink channel matrix, an access point-side calibration coefficient matrix, and a user equipment-side calibration coefficient matrix. Based on the access point-side and user equipment-side calibration coefficient matrices, the corrected and compensated uplink channel is determined. Then, the calibration coefficient error statistical characteristic parameters are determined. Based on these parameters and the corrected and compensated uplink channel, and combined with a precoding algorithm, the target precoding matrix is ​​obtained. This antenna system precoding method, by introducing calibration coefficient error statistical characteristic parameters, eliminates the uncertainty of calibration coefficient errors, ensuring that even with significant calibration delays, the precoding method does not exhibit significant performance degradation.

[0048] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a flowchart of an antenna system precoding method provided according to Embodiment 1 of the present invention;

[0051] Figure 2 This is a schematic diagram of a non-cellular massive MIMO antenna system provided according to Embodiment 1 of the present invention;

[0052] Figure 3 This is a schematic diagram of the structure of an antenna system precoding device according to Embodiment 2 of the present invention;

[0053] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the antenna system precoding method of Embodiment 3 of the present invention. Detailed Implementation

[0054] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0055] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0056] Example 1

[0057] Figure 1 This is a flowchart of an antenna system precoding method provided in Embodiment 1 of the present invention. This embodiment is applicable to precoding in antenna systems. The method can be executed by an antenna system precoding device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0058] S110. Obtain the initial uplink channel matrix, the access point-side calibration coefficient matrix, and the user equipment-side calibration coefficient matrix. Determine the corrected and compensated uplink channel based on the access point-side calibration coefficient matrix and the user equipment-side calibration coefficient matrix.

[0059] In this embodiment, Cellular-free massive MIMO (CF-mMIMO) is a wireless communication technology designed to eliminate inter-cell interference by deploying a large number of distributed small base stations and introducing a cooperative mechanism, thereby significantly improving network capacity and coverage. Figure 2 This is a schematic diagram of a cellular-free massive MIMO system provided in an embodiment of the present invention. As shown in the figure, the cellular-free massive MIMO system consists of a central processing unit (CPU), multiple access points (APs), and multiple user equipments (UEs). The access points (APs) are connected to the central processing unit (CPU) via wires, and the access points (APs) and user equipments (UEs) transmit and receive signals through a wireless channel.

[0060] Let M and K represent the number of antennas on the AP side and the UE side, respectively, and define c m，tx，ap and c m，rx，ap Let c represent the gain coefficients of the transmitting and receiving RF circuits of the m-th antenna on the AP side, respectively. k，tx，ue and c k，rx，ue Let represent the gain coefficients of the transmitting and receiving radio frequency circuits of the k-th antenna on the UE side, respectively. Then, the downlink channel h between the m-th antenna on the AP side and the k-th antenna on the UE side... mk，d With uplink channel h mk，u They are respectively:

[0061] h mk，d =c k，rx，ue ·h mk ·c m，tx，ap

[0062] h mk，u =c m，rx，ap ·h mk ·c k，tx，ue

[0063] Among them, h mk Let be the air interface channel between the m-th antenna on the AP side and the k-th antenna on the UE side. Representing the above relationship in matrix form, we have:

[0064]

[0065]

[0066] Define the calibration matrix as follows:

[0067]

[0068]

[0069] Define the calibration vector as:

[0070] c ap =diag(Cap )

[0071] c ue =diag(C ue )

[0072]

[0073]

[0074] Based on the above relationships, we can obtain the following equation:

[0075]

[0076] Among them, H u This is the initial uplink channel matrix, C. ap and C ue These are the calibration coefficient matrices for the access point (AP) side and the user equipment (UE) side, respectively. Based on these matrices, the corrected and compensated uplink channel can be determined.

[0077]

[0078] S120. Determine the statistical characteristic parameters of the calibration coefficient error. Based on the statistical characteristic parameters of the calibration coefficient error and the uplink channel after correction and compensation, and in conjunction with the set precoding algorithm, obtain the target precoding matrix.

[0079] Under ideal channel reciprocity conditions, after obtaining the initial uplink channel matrix H u And the calibration coefficient matrix C on the AP side and UE side ap and C ue Then, the CPU can calculate the downlink channel matrix under ideal conditions. Precoding is then performed. However, due to environmental variations (e.g., temperature, humidity), the phase drift between independent oscillators exhibits time-varying characteristics. Generally, oscillator phase drift causes the phase of the actual calibration coefficients to jitter around the estimated calibration coefficient phase, while the amplitude of the actual calibration coefficients changes very little. Therefore, considering the calibration delay and the time-varying characteristics of the calibration coefficients, the relationship between the uplink and downlink channels can be modeled as follows:

[0080]

[0081] in, To correct the compensated uplink channel, E ue and E ap The calibration coefficient error matrices for the UE side and the AP side are respectively, and their corresponding calibration coefficient error vectors are defined as follows:

[0082] e ap =diag(E ap )

[0083] e ue =diag(E ue )

[0084]

[0085]

[0086] Where, θ m，ap m = 1, ..., M and θ k，ue Let k = 1, ..., K each follow some independent and identically distributed system that is symmetric about 0°, such as a uniform distribution or a Gaussian truncated distribution. Therefore, we can denote... as well as e ap and e ue That is, the statistical characteristic parameter of the calibration coefficient error.

[0087] In this embodiment, after determining the statistical characteristic parameters of the calibration coefficient error, the target precoding matrix can be calculated based on the statistical characteristic parameters of the calibration coefficient error, the corrected and compensated uplink channel, and the set precoding algorithm.

[0088] Optionally, the precoding algorithm includes a zero-forcing precoding algorithm. The method for obtaining the target precoding matrix based on the calibration coefficient error statistical characteristic parameters and the corrected uplink channel, combined with the precoding algorithm, can be as follows: the norm of the difference between the product of the precoding matrix and the downlink channel matrix and the identity matrix is ​​used as the first target norm; wherein the downlink channel matrix is ​​related to the corrected uplink channel; according to the zero-forcing precoding algorithm, the expectation of the first target norm with respect to the calibration coefficient error statistical characteristic parameters is used as the first target expectation function; the precoding matrix that minimizes the first target expectation function is determined as the target precoding matrix.

[0089] Specifically, Zero Forcing (ZF) precoding is a linear precoding technique. Its core principle is to separate the transmitted data stream through beamforming, resulting in a unit response in the desired direction and zero response in the undesired direction. When using RZF for precoding, the target precoding matrix can be obtained by solving the following optimization problem:

[0090]

[0091] Where W is the precoding matrix, W RZF Let I be the optimal solution for the precoding matrix W (i.e., the target precoding matrix), and let I be the identity matrix. This represents the expectation of a random variable x. For the first objective norm, Let e ​​be the expected function of the first objective, representing the statistical characteristic parameter e of the first objective norm with respect to the calibration coefficient error. ap and e ue The expectation. Due to Therefore, the downlink channel matrix H d With the corrected and compensated uplink channel Related.

[0092] Target precoding matrix W RZF For order The value of the precoding matrix W that takes the minimum value is used to solve for W. RZF ,definition Then we have:

[0093]

[0094] Here, Δ is a quantity independent of W.

[0095] The conjugate gradient of J(W) with respect to W is:

[0096]

[0097] According to the first-order optimality condition, we can obtain:

[0098]

[0099] in This indicates the search for the pseudo-inverse operation, and it has...

[0100]

[0101]

[0102] Where ⊙ represents the Hadamard product, and has

[0103] E = (1 - |e ap | 2 )·I+|e ap | 2 ·1 M×M

[0104] Substituting, we can obtain

[0105]

[0106] in, for The conjugate matrix, for The transpose of .

[0107] Based on the above formula The error statistical characteristic parameter e of the calibration coefficient can be used as a basis. ap e ue and the corrected and compensated uplink channel Calculate the target precoding matrix W RZF .

[0108] Furthermore, after obtaining the target precoding matrix, we can also: determine the power constraint factor, and use the product of the power constraint factor and the target precoding matrix to determine the actual precoding matrix.

[0109] Preferably, the power constraint factor is determined based on the maximum transmitted signal power of the signal transmitter and the initial transmitted signal power of the signal transmitter before precoding.

[0110] Specifically, considering the power constraints at the AP side transmitter, let P max Indicates the maximum transmitted signal power of the signal transmitter, let Let represent the initial transmitted signal power at the signal transmitter before precoding. Then we have: in This refers to the downlink data symbol vector transmitted by the AP side, and it has... You can be in W RZF The expression introduces a power constraint factor β. RZF :

[0111]

[0112] The actual precoding matrix can then be represented as:

[0113]

[0114] Optionally, the precoding algorithm includes the Wiener precoding algorithm. The method for obtaining the target precoding matrix based on the calibration coefficient error statistical characteristic parameters and the compensated uplink channel, combined with the precoding algorithm, can be as follows: The product of the received signal and the gain factor is used as the first product; the norm of the difference between the transmitted signal and the first product is used as the second target norm; wherein the received signal is related to the compensated uplink channel; according to the Wiener precoding algorithm, the expectation of the second target norm with respect to the calibration coefficient error statistical characteristic parameters, the transmitted signal, and the receiver noise is used as the second target expectation function; the precoding matrix that minimizes the second target expectation function and satisfies the power constraint condition is determined as the target precoding matrix.

[0115] Specifically, Wiener precoding (WF) is a linear precoding technique whose basic principle is to design a filter by minimizing the mean square error between the received signal and the original signal. Assume the downlink data symbol vector transmitted by the AP side is... The data symbol vector received by the UE side is Where n is additive white Gaussian noise, and has as well as in This indicates the initial transmitted signal power at the signal transmitter before precoding. Let P be the noise power. Considering automatic gain control on the UE side and power constraints at the AP side transmitter, let P... max This indicates the maximum transmitted signal power at the signal transmitter.

[0116] When using WF for precoding, the target precoding matrix can be obtained by solving the following optimization problem:

[0117]

[0118] Among them, W RW For the optimal solution of the precoding matrix W (i.e., the target precoding matrix), β -1 This is the gain factor on the UE side. The first product, For the second objective norm, Let e ​​be the expectation function of the second objective, representing the statistical characteristic parameter e of the second objective norm with respect to the calibration coefficient error. ap and e ue The expected values ​​of the transmitted signal s and the receiver noise n. This is a power constraint condition. W RW and β RW For order The values ​​of the precoding matrix W and the gain factor β are determined by taking the minimum value.

[0119] According to the Lagrange multiplier method, the following result can be obtained:

[0120]

[0121]

[0122]

[0123] Where ⊙ represents the Hadamard product, and has

[0124] E = (1 - |e ap | 2 )·I+|e ap | 2 ·1 M×M

[0125] Based on expression The error statistical characteristic parameter e of the calibration coefficient can be used as a basis. ap e ueand the corrected and compensated uplink channel Calculate the target precoding matrix W RW .

[0126] Optionally, the precoding algorithm includes a minimum mean square error precoding algorithm. The method for obtaining the target precoding matrix based on the calibration coefficient error statistical characteristic parameters and the compensated uplink channel, combined with the precoding algorithm, can be as follows: The norm of the difference between the transmitted and received signals is used as the third target norm; where the received signal is related to the compensated uplink channel; according to the minimum mean square error precoding algorithm, the expectation of the third target norm with respect to the calibration coefficient error statistical characteristic parameters, the transmitted signal, and the received noise is used as the third target expectation function; the precoding matrix that minimizes the third target expectation function and satisfies the power constraint condition is determined as the target precoding matrix.

[0127] Specifically, Minimum Mean-Square Error (MMSE) precoding is a signal processing technique designed to minimize the mean square error between the received and desired signals, thereby achieving optimal signal-to-noise ratio and bit error rate performance. Assume the downlink data symbol vector transmitted by the AP is... The data symbol vector received by the UE side is Where n is additive white Gaussian noise, and has as well as in This indicates the initial transmitted signal power at the signal transmitter before precoding. Let P be the noise power. Considering the power constraint at the AP side transmitter, let P... max This indicates the maximum transmitted signal power at the signal transmitter.

[0128] When using MMSE for precoding, the target precoding matrix can be obtained by solving the following optimization problem:

[0129]

[0130] Among them, W RMMSE The optimal solution for the precoding matrix W (i.e., the target precoding matrix) is... For the third objective norm, Let e ​​be the expectation function of the third objective, representing the statistical characteristic parameter e of the third objective norm with respect to the calibration coefficient error. ap and e ue The expected values ​​of the transmitted signal s and the receiver noise n. This is a power constraint condition. W RMMSE For order The value of the precoding matrix W that takes the minimum value.

[0131] By introducing the Lagrange multiplier λ≥0, we can obtain

[0132]

[0133] In the formula, W RMMSE It is a function of λ. According to the relaxation complementarity condition, when

[0134]

[0135] At that time, W RMMSE With no power constraint factor β RZF W RZF Having the same form, that is

[0136]

[0137] Otherwise, the optimal solution for λ needs to be obtained by solving the following equation:

[0138]

[0139] The above formula is equivalent to

[0140]

[0141] in, DΛD H yes The eigenvalue decomposition of , where Λ is the eigenvalue matrix. Let [X] be the eigenvalue matrix. mm Let X represent the m-th diagonal element. The above formula can be simplified to:

[0142]

[0143] In the above equation, the left side of the equation is monotonically decreasing with respect to λ, and the value of λ can be determined by searching for λ using the bisection method.

[0144] After determining the value of λ, based on the expression The error statistical characteristic parameter e of the calibration coefficient can be used as a basis. ap e ue and the corrected and compensated uplink channel Calculate the target precoding matrix W RMMSE .

[0145] The antenna system precoding method disclosed in this invention first obtains an initial uplink channel matrix, an access point-side calibration coefficient matrix, and a user equipment-side calibration coefficient matrix. Based on the access point-side and user equipment-side calibration coefficient matrices, the corrected and compensated uplink channel is determined. Then, the calibration coefficient error statistical characteristic parameters are determined. Based on these parameters and the corrected and compensated uplink channel, and combined with a precoding algorithm, the target precoding matrix is ​​obtained. This antenna system precoding method, by introducing calibration coefficient error statistical characteristic parameters, eliminates the uncertainty of calibration coefficient errors, ensuring that even with significant calibration delays, the precoding method does not exhibit significant performance degradation.

[0146] Example 2

[0147] Figure 3 This is a schematic diagram of the structure of an antenna system precoding device provided in Embodiment 2 of the present invention, as shown below. Figure 3 As shown, the device includes: a corrected and compensated uplink channel determination module 310 and a target precoding matrix determination module 320.

[0148] The corrected and compensated uplink channel determination module 310 is used to obtain the initial uplink channel matrix, the access point-side calibration coefficient matrix and the user equipment-side calibration coefficient matrix, and to determine the corrected and compensated uplink channel based on the access point-side calibration coefficient matrix and the user equipment-side calibration coefficient matrix.

[0149] The target precoding matrix determination module 320 is used to determine the statistical characteristic parameters of the calibration coefficient error. Based on the statistical characteristic parameters of the calibration coefficient error and the uplink channel after correction and compensation, and in conjunction with the set precoding algorithm, the target precoding matrix is ​​obtained.

[0150] Optionally, the precoding algorithm is set to include a zero-forcing precoding algorithm, and the target precoding matrix determination module 320 is also used for:

[0151] The first target norm is the difference between the product of the precoding matrix and the downlink channel matrix and the identity matrix; the downlink channel matrix is ​​related to the corrected and compensated uplink channel; according to the zero-forcing precoding algorithm, the expectation of the first target norm with respect to the statistical characteristic parameter of the calibration coefficient error is taken as the first target expectation function; the precoding matrix that minimizes the first target expectation function is determined as the target precoding matrix.

[0152] Optionally, the apparatus further includes an actual precoding matrix determination module 330, used to determine the power constraint factor and determine the product of the power constraint factor and the target precoding matrix as the actual precoding matrix.

[0153] Optionally, the power constraint factor is determined based on the maximum transmitted signal power of the signal transmitter and the initial transmitted signal power of the signal transmitter before precoding.

[0154] Optionally, the precoding algorithm is set to include the Wiener precoding algorithm, and the target precoding matrix determination module 320 is also used for:

[0155] The product of the received signal and the gain factor is taken as the first product, and the norm of the difference between the transmitted signal and the first product is taken as the second target norm; wherein, the received signal is related to the corrected and compensated uplink channel; according to the Wiener precoding algorithm, the expectation of the second target norm with respect to the statistical characteristic parameter of the calibration coefficient error, the transmitted signal and the receiver noise is taken as the second target expectation function; the precoding matrix that minimizes the second target expectation function and satisfies the power constraint condition is determined as the target precoding matrix.

[0156] Optionally, the precoding algorithm is set to include the minimum mean square error precoding algorithm, and the target precoding matrix determination module 320 is also used for:

[0157] The norm of the difference between the transmitted and received signals is taken as the third target norm; where the received signal is related to the corrected and compensated uplink channel; according to the minimum mean square error precoding algorithm, the expectation of the third target norm with respect to the statistical characteristic parameter of the calibration coefficient error, the transmitted signal and the receiver noise is taken as the third target expectation function; the precoding matrix that minimizes the third target expectation function and satisfies the power constraint condition is determined as the target precoding matrix.

[0158] The antenna system precoding apparatus provided in this embodiment of the invention can execute the antenna system precoding method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0159] Example 3

[0160] Figure 4 A schematic diagram of an electronic device 10 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, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0161] like Figure 4As 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 may 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.

[0162] 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.

[0163] 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 antenna system precoding methods.

[0164] In some embodiments, the antenna system precoding method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted 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 antenna system precoding described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the antenna system precoding method by any other suitable means (e.g., by means of firmware).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include 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.

[0170] 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.

[0171] 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.

[0172] 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.

Claims

1. A precoding method for an antenna system, characterized in that, include: Obtain the initial uplink channel matrix, the access point-side calibration coefficient matrix, and the user equipment-side calibration coefficient matrix. Determine the corrected and compensated uplink channel based on the access point-side calibration coefficient matrix and the user equipment-side calibration coefficient matrix. ;in, To correct and compensate for the uplink channel. For the user equipment side calibration coefficient matrix, The initial uplink channel matrix, For the calibration coefficient matrix on the access point side; Determine the statistical characteristic parameters of the calibration coefficient error, and based on the statistical characteristic parameters of the calibration coefficient error and the corrected and compensated uplink channel, and in conjunction with the set precoding algorithm, obtain the target precoding matrix; The precoding algorithm includes a zero-forcing precoding algorithm, which, based on the calibration coefficient error statistical characteristic parameters and the corrected uplink channel, and in conjunction with the precoding algorithm, yields a target precoding matrix, including: The first target norm is the norm of the difference between the product of the precoding matrix and the downlink channel matrix and the identity matrix; wherein, the downlink channel matrix is ​​related to the corrected and compensated uplink channel. ;in, This is the downlink channel matrix. The calibration coefficient error matrix on the user equipment side. This is the calibration coefficient error matrix on the access point side; According to the zero-forcing precoding algorithm, the expectation of the first target norm with respect to the calibration coefficient error is used as the first target expectation function; The precoding matrix that minimizes the first target expectation function is determined as the target precoding matrix; The target precoding matrix is: ； in, For the precoding matrix, The target precoding matrix under the zero-forcing precoding algorithm, As a unit array, Represents the relationship between random variables Seeking expectations, For the first objective norm, Let be the expectation function of the first objective, representing the first objective norm with respect to the calibration coefficient error. and Expectations , Calibration coefficient error The expectation of each element in is Calibration coefficient error The expectation of each element in is , and These are the statistical characteristic parameters of the calibration coefficient error.

2. The method according to claim 1, characterized in that, After obtaining the target precoding matrix, the following is also included: Determine the power constraint factor, and then use the product of the power constraint factor and the target precoding matrix to determine the actual precoding matrix.

3. The method according to claim 2, characterized in that, The power constraint factor is determined based on the maximum transmitted signal power of the signal transmitter and the initial transmitted signal power of the signal transmitter before precoding.

4. The method according to claim 1, characterized in that, The precoding algorithm includes the Wiener precoding algorithm. Based on the calibration coefficient error statistical characteristic parameters and the corrected uplink channel, and in conjunction with the precoding algorithm, a target precoding matrix is ​​obtained, including: The product of the received signal and the gain factor on the user equipment side is taken as the first product, and the norm of the difference between the transmitted signal and the first product is taken as the second target norm; wherein, the received signal is related to the corrected and compensated uplink channel: ;in, In order to receive signals, In order to send a signal, Here, n is the precoding matrix, and n is the receiver noise. This is the downlink channel matrix. , The calibration coefficient error matrix on the user equipment side. This is the calibration coefficient error matrix on the access point side; According to the Wiener precoding algorithm, the expectation of the second target norm with respect to the calibration coefficient error, the transmitted signal, and the receiver noise is used as the second target expectation function; The precoding matrix that minimizes the second objective expectation function and satisfies the power constraint is determined as the target precoding matrix. The target precoding matrix is: ； ； in, This represents the target precoding matrix under the Wiener precoding algorithm. For order The value of the gain factor that takes the minimum value. For gain factor, For the gain factor on the user equipment side, ; The first product, For the second objective norm, Let be the expectation function of the second objective, representing the norm of the second objective with respect to the calibration coefficient error. and The expected values ​​of the transmitted signal s and the received noise n. , Calibration coefficient error The expectation of each element in is Calibration coefficient error The expectation of each element in is , and These are the statistical characteristic parameters of the calibration coefficient error; For power constraints, This indicates the maximum transmitted signal power at the signal transmitting end.

5. The method according to claim 1, characterized in that, The precoding algorithm includes a minimum mean square error precoding algorithm. Based on the calibration coefficient error statistical characteristic parameters and the corrected uplink channel, and in conjunction with the precoding algorithm, a target precoding matrix is ​​obtained, including: The norm of the difference between the transmitted and received signals is used as the third target norm; wherein the received signal is related to the corrected and compensated uplink channel: ;in, In order to receive signals, In order to send a signal, Here, n is the precoding matrix, and n is the receiver noise. This is the downlink channel matrix. , The calibration coefficient error matrix on the user equipment side. This is the calibration coefficient error matrix on the access point side; According to the minimum mean square error precoding algorithm, the expectation of the third target norm with respect to the calibration coefficient error, the transmitted signal, and the receiver noise is used as the third target expectation function; The precoding matrix that minimizes the third objective expectation function and satisfies the power constraint is determined as the target precoding matrix. The target precoding matrix is: ； in, This represents the target precoding matrix under the minimum mean square error precoding algorithm. For the third objective norm, Let be the expectation function of the third objective, representing the third objective norm with respect to the calibration coefficient error. and The expected values ​​of the transmitted signal s and the received noise n. , Calibration coefficient error The expectation of each element in is Calibration coefficient error The expectation of each element in is , and These are the statistical characteristic parameters of the calibration coefficient error; For power constraints, This indicates the maximum transmitted signal power at the signal transmitting end.

6. A precoding apparatus for an antenna system, characterized in that, include: The corrected and compensated uplink channel determination module is used to obtain the initial uplink channel matrix, the access point-side calibration coefficient matrix, and the user equipment-side calibration coefficient matrix, and to determine the corrected and compensated uplink channel based on the access point-side calibration coefficient matrix and the user equipment-side calibration coefficient matrix. ;in, To correct and compensate for the uplink channel. For the user equipment side calibration coefficient matrix, The initial uplink channel matrix, For the calibration coefficient matrix on the access point side; The target precoding matrix determination module is used to determine the calibration coefficient error statistical characteristic parameters, and obtain the target precoding matrix based on the calibration coefficient error statistical characteristic parameters and the corrected and compensated uplink channel, combined with the set precoding algorithm. The precoding algorithm is set to include zero-forcing precoding, and the target precoding matrix determination module is also used for: The first target norm is the norm of the difference between the product of the precoding matrix and the downlink channel matrix and the identity matrix; where the downlink channel matrix is ​​related to the corrected and compensated uplink channel. ;in, This is the downlink channel matrix. The calibration coefficient error matrix on the user equipment side. This is the calibration coefficient error matrix on the access point side; According to the zero-forcing precoding algorithm, the expectation of the first target norm with respect to the calibration coefficient error is used as the first target expectation function; The precoding matrix that minimizes the first objective expectation function is determined as the target precoding matrix; The target precoding matrix is: ； in, For the precoding matrix, The target precoding matrix under the zero-forcing precoding algorithm, As a unit array, Represents the relationship between random variables Seeking expectations, For the first objective norm, Let be the expectation function of the first objective, representing the first objective norm with respect to the calibration coefficient error. and Expectations , Calibration coefficient error The expectation of each element in is Calibration coefficient error The expectation of each element in is , and These are the statistical characteristic parameters of the calibration coefficient error.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the antenna system precoding method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the antenna system precoding method according to any one of claims 1-5.

9. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the antenna system precoding method as described in any one of claims 1-5.

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