Channel estimation method and related equipment for six-dimensional movable antenna base station

Abstract

The channel estimation method and related equipment of a six-dimensional movable antenna base station proposed in an embodiment of the present application, the six-dimensional movable antenna base station is provided with at least one antenna surface, the antenna surface includes at least one six-dimensional antenna, the method includes: obtaining a pilot signal sent by a user terminal, performing statistical channel estimation calculation based on the pilot signal and the intermediate matrix, and obtaining a channel power matrix and a directional sparsity matrix between the user terminal and the six-dimensional antenna; solving a rate optimization model based on the channel power matrix to obtain a target position rotation parameter, and performing position rotation adjustment on at least one six-dimensional antenna according to the target position rotation parameter; performing instantaneous channel estimation based on the pilot signal and the directional sparsity matrix to obtain an instantaneous channel estimation result, thereby effectively improving the information transmission rate between the six-dimensional movable antenna base station and the user terminal, while also improving the accuracy of the channel estimation between the six-dimensional movable antenna base station and the Internet of Things terminal.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a channel estimation method and related equipment for a six-dimensional movable antenna base station. Background Art

[0002] To meet the growing number of IoT devices in the upcoming sixth-generation wireless networks, the demand for data transmission rates between base stations and IoT terminals is also increasing. In related technologies, multiple six-dimensional movable antennas are typically installed on base stations to improve the data transmission rate between base stations and IoT terminals. In addition, real-time channel estimation is required between base stations and IoT terminals to plan appropriate data transmission parameters for data transmission.

[0003] However, for a base station equipped with a six-dimensional movable antenna, due to the flexibility of the six-dimensional movable antenna, the channel estimation method in the related art cannot accurately obtain the instantaneous channel estimation value between the base station and the IoT terminal. Summary of the Invention

[0004] The embodiments of the present application provide a channel estimation method and related equipment for a six-dimensional movable antenna base station, which can improve the accuracy of channel estimation between the six-dimensional movable antenna base station and an Internet of Things terminal.

[0005] To achieve the above objectives, a first aspect of an embodiment of the present application provides a channel estimation method for a six-dimensional movable antenna base station, wherein the six-dimensional movable antenna base station is provided with at least one antenna surface, and the antenna surface includes at least one six-dimensional antenna. The method includes:

[0006] Obtaining a pilot signal sent by a user terminal, performing a statistical channel estimation calculation based on the pilot signal and an intermediate matrix to obtain a channel power matrix and a directional sparsity matrix between the user terminal and the six-dimensional antenna, wherein the intermediate matrix is ​​obtained based on the noise variance of the six-dimensional movable antenna base station;

[0007] Solving a rate optimization model based on the channel power matrix to obtain a target position rotation parameter, and performing position rotation adjustment on at least one of the six-dimensional antennas according to the target position rotation parameter to optimize the transmission rate between the user terminal and the six-dimensional antenna;

[0008] After at least one of the six-dimensional antennas is rotated and adjusted, instantaneous channel estimation is performed based on the pilot signal and the directional sparsity matrix to obtain an instantaneous channel estimation result between the six-dimensional movable antenna base station and the user terminal.

[0009] In some embodiments, performing statistical channel estimation calculation based on the pilot signal and the intermediate matrix to obtain a channel power matrix and a directional sparsity matrix between the user terminal and the six-dimensional antenna includes:

[0010] Acquiring a power state vector parameter and a directional sparsity matrix parameter, obtaining an intermediate variable based on a maximum likelihood estimate of the pilot signal and the intermediate matrix, and updating the power state vector parameter based on the intermediate variable to obtain an updated power state vector parameter;

[0011] updating the intermediate matrix based on the intermediate variable and the pilot signal to obtain an updated intermediate matrix;

[0012] When the updated power state vector parameter is greater than a preset state vector threshold, updating the directional sparsity matrix parameter to obtain an updated directional sparsity matrix parameter;

[0013] Iteratively updating the updated intermediate matrix as a new intermediate matrix, the updated power state vector parameter as a new power state vector parameter, and the updated directional sparsity matrix parameter as a new directional sparsity matrix parameter, and using the updated directional sparsity matrix parameter after iterative updating as the directional sparsity matrix;

[0014] The channel power matrix is ​​obtained based on the directional sparsity matrix and the power state vector parameter.

[0015] In some embodiments, there are multiple user terminals, and obtaining the channel power matrix based on the directional sparsity matrix and the power state vector parameter includes:

[0016] Determining the directional sparsity between each user terminal and the six-dimensional antenna one by one according to the directional sparsity matrix;

[0017] Based on the directional sparsity, the channel power matrix between each user terminal and the six-dimensional antenna is obtained according to the power state vector parameter.

[0018] In some embodiments, there are multiple six-dimensional antennas, and the step of constructing the rate optimization model includes:

[0019] Obtaining a target rate optimization function based on a product of a channel power matrix parameter, a directional sparsity matrix parameter, and a transmit power of the user terminal and taking a logarithm thereof;

[0020] Obtain an index matrix corresponding to the position rotation parameter and a distance matrix between each two of the six-dimensional antennas, obtain an index distance based on the product of the transposed matrix of the index matrix and the distance matrix, and then multiply the index matrix by the product, and generate a safety distance constraint based on the size relationship between the index distance and the minimum distance;

[0021] Generate binary position and rotation selection constraints with position and rotation parameters;

[0022] The rate optimization model is generated based on the target rate optimization function, the safety distance constraint, the binary position rotation selection constraint, and the position rotation parameter.

[0023] In some embodiments, solving the rate optimization model based on the channel power matrix to obtain the target position rotation parameter includes:

[0024] Continuously relaxing the binary position rotation selection constraint, and updating the rate optimization model according to the relaxed binary position rotation selection constraint to obtain an updated rate optimization model;

[0025] Substituting the channel power matrix into the channel power matrix parameters in the update rate optimization model;

[0026] The update rate optimization model is solved to obtain the target position rotation parameters.

[0027] In some embodiments, performing instantaneous channel estimation based on the pilot signal and the directional sparsity matrix to obtain an instantaneous channel estimation result between the six-dimensional movable antenna base station and the user terminal includes:

[0028] Acquire a received signal matrix obtained from a pilot signal matrix, wherein the pilot signal matrix includes a plurality of the pilot signals;

[0029] generating an observation signal matrix based on the pilot signal matrix and the unit diagonal matrix;

[0030] Acquire channel estimation parameters, perform instantaneous channel estimation based on the channel estimation parameters, the observation signal matrix, the directional sparsity matrix, and the received signal matrix, and obtain the instantaneous channel estimation result.

[0031] In some embodiments, performing instantaneous channel estimation based on the channel estimation parameters, the observed signal matrix, the directional sparsity matrix, and the received signal matrix to obtain the instantaneous channel estimation result includes:

[0032] generating support vectors based on the directional sparsity matrix and the unit diagonal matrix, and generating a support observation matrix based on the support vectors and the observation matrix;

[0033] Obtaining a target channel estimation function based on the product of the support observation matrix and the channel estimation parameter and subtracting the received signal matrix;

[0034] The instantaneous channel estimation result is obtained based on the least squares of the channel estimation function and the channel estimation parameters.

[0035] To achieve the above objectives, a second aspect of an embodiment of the present application provides a six-dimensional movable antenna base station, comprising:

[0036] The six-dimensional movable antenna base station is provided with at least one antenna surface and a central processing unit, wherein the antenna surface is provided with a six-dimensional antenna and local computing hardware;

[0037] The local computing hardware is configured to obtain a pilot signal transmitted by a user terminal, perform statistical channel estimation calculations based on the pilot signal and an intermediate matrix, and obtain a channel power matrix and a directional sparsity matrix between the user terminal and the six-dimensional antenna, wherein the intermediate matrix is ​​obtained based on the noise variance of the six-dimensional movable antenna base station;

[0038] The central processing unit is configured to solve a rate optimization model based on the channel power matrix to obtain a target position rotation parameter, and perform position rotation adjustment on the six-dimensional antenna according to the target position rotation parameter, wherein the rate optimization model includes a target rate optimization function between the user terminal and the six-dimensional antenna;

[0039] The local computing hardware is further used to perform instantaneous channel estimation based on the pilot signal and the directional sparsity matrix to obtain an instantaneous channel estimation result between the six-dimensional movable antenna base station and the user terminal.

[0040] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, the memory storing a computer program, and the processor implementing the channel estimation method of the six-dimensional movable antenna base station as described in the first aspect when executing the computer program.

[0041] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a storage medium, which is a computer-readable storage medium, and the storage medium stores a computer program. When the computer program is executed by a processor, it implements the channel estimation method of the six-dimensional movable antenna base station described in the first aspect above.

[0042] The channel estimation method and related equipment of a six-dimensional movable antenna base station proposed in an embodiment of the present application are that the six-dimensional movable antenna base station is provided with at least one antenna surface, and the antenna surface includes at least one six-dimensional antenna. The method includes: first, obtaining a pilot signal sent by a user terminal, performing statistical channel estimation calculation based on the pilot signal and an intermediate matrix, and obtaining a channel power matrix and a directional sparsity matrix between the user terminal and the six-dimensional antenna, wherein the intermediate matrix is ​​obtained based on the noise variance of the six-dimensional movable antenna base station; then, solving a rate optimization model based on the channel power matrix to obtain a target position rotation parameter, and performing position rotation adjustment on at least one six-dimensional antenna according to the target position rotation parameter to optimize the transmission rate between the user terminal and the six-dimensional antenna; finally, after the position rotation adjustment of at least one six-dimensional antenna, performing instantaneous channel estimation based on the pilot signal and the directional sparsity matrix, and obtaining an instantaneous channel estimation result between the six-dimensional movable antenna base station and the user terminal. The embodiment of the present application uses the pilot signal emitted by the user terminal and the intermediate matrix obtained by the noise variance of the six-dimensional movable antenna base station for estimation to obtain the channel power matrix between the six-dimensional movable antenna base station and the user terminal and the directional sparsity matrix corresponding to the directional sparsity generated between different six-dimensional antennas and the user terminal; then the six-dimensional antenna is adjusted using the channel power matrix and the target position rotation parameter obtained by the rate optimization model to further effectively improve the transmission rate between the user terminal and the six-dimensional antenna, and after fixing the six-dimensional antenna at this moment, the pilot signal and the directional sparsity matrix are used to perform instantaneous channel estimation to obtain an accurate instantaneous channel estimation result between the six-dimensional movable antenna base station and the user terminal, thereby effectively improving the information transmission rate between the six-dimensional movable antenna base station and the user terminal while also improving the accuracy of the channel estimation between the six-dimensional movable antenna base station and the Internet of Things terminal.

[0043] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. The purposes and other advantages of the present application can be achieved and obtained through the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a schematic diagram of the communication structure of a six-dimensional movable antenna base station provided in one embodiment of the present application.

[0045] Figure 2 This is a structural schematic diagram of a telescopic rotating rod provided in another embodiment of the present application.

[0046] Figure 3 This is a schematic diagram of the central processing architecture of a six-dimensional movable antenna base station provided in another embodiment of the present application.

[0047] Figure 4 This is a schematic diagram of the central processing architecture of another six-dimensional movable antenna base station provided in yet another embodiment of the present application.

[0048] Figure 5 This is a flowchart of a channel estimation method for a six-dimensional movable antenna base station provided in another embodiment of the present application.

[0049] Figure 6 This is a schematic diagram of candidate position-rotation pairs of a six-dimensional antenna provided in another embodiment of the present application.

[0050] Figure 7 This is a multi-stage schematic diagram of a channel estimation value of a six-dimensional movable antenna base station provided by another embodiment of the present application.

[0051] Figure 8 This is a simulation performance diagram of the first channel estimation method for a six-dimensional movable antenna base station provided in another embodiment of the present application.

[0052] Figure 9 This is a simulation performance diagram of a second channel estimation method for a six-dimensional movable antenna base station provided in another embodiment of the present application.

[0053] Figure 10 This is a simulation performance diagram of a third channel estimation method for a six-dimensional movable antenna base station provided in another embodiment of the present application.

[0054] Figure 11 This is a simulation performance diagram of a fourth channel estimation method for a six-dimensional movable antenna base station provided in another embodiment of the present application.

[0055] Figure 12 This is a schematic diagram of the hardware structure of an electronic device provided in another embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0057] It should be noted that although the functional modules are divided in the device schematic and the logical order is shown in the flowchart, in some cases, the steps shown or described can be performed in a different order than the module division in the device or the order in the flowchart.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0059] First, let’s analyze some of the terms used in this application:

[0060] Channel estimation is a key technology in modern communication systems. Its primary function is to determine channel characteristics, such as gain and phase, in a dynamically changing wireless environment. By utilizing known reference signals (such as pilot symbols) or predefined training sequences, a receiver can estimate the channel response and compensate for distortions encountered during signal transmission, such as multipath propagation and fading, thereby enhancing data reception accuracy and communication quality. This process is crucial to ensuring the efficiency and reliability of wireless communication systems.

[0061] Directional sparsity refers to the characteristic of a signal having a non-uniform or concentrated distribution in certain directions. This means that the signal has significant energy only in a few specific directions, while the energy in other directions is very small or even zero. This property has applications in various fields, particularly in array signal processing, beamforming in radar and communication systems, feature detection in image processing, and compressed sensing theory. Directional sparsity can be exploited to design more efficient algorithms for data processing. For example, in wireless communications, identifying and exploiting the directional sparsity of signals can optimize resource allocation, reduce interference, and improve overall system performance.

[0062] To meet the growing number of IoT devices in the upcoming sixth-generation wireless networks, the demand for data transmission rates between base stations and IoT terminals is also increasing. In related technologies, multiple six-dimensional movable antennas are typically installed on base stations to improve the data transmission rate between base stations and IoT terminals. In addition, real-time channel estimation is required between base stations and IoT terminals to plan appropriate data transmission parameters for data transmission.

[0063] However, for a base station equipped with a six-dimensional movable antenna, due to the flexibility of the six-dimensional movable antenna, the channel estimation method in the related art cannot accurately obtain the instantaneous channel estimation value between the base station and the IoT terminal.

[0064] In order to improve the accuracy of channel estimation between the six-dimensional movable antenna base station and the Internet of Things terminal, the embodiment of the present application uses the pilot signal emitted by the user terminal and the intermediate matrix obtained by the noise variance of the six-dimensional movable antenna base station for estimation to obtain the channel power matrix between the six-dimensional movable antenna base station and the user terminal and the directional sparsity matrix corresponding to the directional sparsity generated between different six-dimensional antennas and the user terminal; then the six-dimensional antenna is adjusted using the channel power matrix and the target position rotation parameter obtained by the rate optimization model to further effectively improve the transmission rate between the user terminal and the six-dimensional antenna, and after fixing the six-dimensional antenna at this moment, the pilot signal and the directional sparsity matrix are used to perform instantaneous channel estimation to obtain an accurate instantaneous channel estimation result between the six-dimensional movable antenna base station and the user terminal, thereby effectively improving the information transmission rate between the six-dimensional movable antenna base station and the user terminal while also improving the accuracy of the channel estimation between the six-dimensional movable antenna base station and the Internet of Things terminal.

[0065] In order to better describe the channel estimation method of the six-dimensional movable antenna base station provided by the present application, the following first describes the six-dimensional movable antenna base station applied to the channel estimation method of the six-dimensional movable antenna base station. Figure 1 , is a schematic diagram of the communication structure of a six-dimensional movable antenna base station provided in an embodiment of the present application. Figure 1 As shown, the six-dimensional movable antenna base station is provided with multiple antenna surfaces (i.e. Figure 1 The 6DMA surface shown in the figure, also known as a six-dimensional movable antenna (6DMA), has multiple six-dimensional antennas disposed on each antenna surface. These six-dimensional antennas are used to receive signals from transmit signals emitted by IoT terminals. The IoT terminals can be terminal devices used by airborne users or ground users. The antenna surface index is represented by the set B = {1, 2, ..., B}. Each antenna surface is modeled as a uniform planar array with N ≥ 1 six-dimensional antennas, with the six-dimensional antenna index represented by the set N = {1, 2, ..., N}.

[0066] like Figure 1 As shown, the antenna surface is connected to the radar base station through a telescopic rotating rod, so that the receiving signal received by the antenna surface is transmitted to the central processing unit (CPU) of the six-dimensional movable antenna base station for processing. Figure 2 , is a schematic diagram of the structure of a telescopic rotating rod provided by the present application. Figure 2As shown, the telescopic rotating rod has a flexible body that can be retracted and expanded, allowing the position of the antenna surface to be adjusted. Flexible wires (such as coaxial cables) are installed in the rod body to provide power for the operation of the antenna surface. Furthermore, rotary motors are installed at each end of the telescopic rotating rod to adjust the rotation angle of the six-dimensional antenna.

[0067] Reference Figure 3 , is a schematic diagram of the central processing architecture of a six-dimensional movable antenna base station provided in an embodiment of the present application. Figure 3 As shown in FIG, a conventional arrangement has an antenna surface (i.e. Figure 3 Base stations with a 6DMA surface (likely a 6DMA surface) typically have only one CPU installed in the base station itself. After receiving signals from multiple six-dimensional antennas on the antenna surface, the CPU acquires all received signals and performs corresponding data processing. However, as the number of antenna surfaces and six-dimensional antennas increases, the signal processing efficiency of this setup will be limited by the hardware performance of the CPU itself.

[0068] Based on this, refer to Figure 4 , is a schematic diagram of another central processing architecture of a six-dimensional movable antenna base station provided by an embodiment of the present application. Figure 4 As shown in the , in addition to the CPU, each antenna surface (i.e., 6DMA surface) is equipped with a local processing unit (LPU). These LPUs perform necessary baseband signal processing tasks, such as channel estimation and signal precoding / combining, on the received signals (or upcoming transmit signals) received by the six-dimensional antenna in a distributed and parallel manner. Furthermore, a telescopic rotating rod delivers power to the antenna surface and LPU, and facilitates control and signal exchange between the LPU and the CPU.

[0069] In some embodiments, for ease of practical implementation, it is assumed that each antenna surface can only adopt a finite set of discrete positions and rotations. For simplicity, it is assumed that for each available candidate position, there is only one possible rotation. It is further assumed that there are M ≥ B discrete position-rotation pairs (q m ,u m ), represented by the set M = {1, 2, ..., M}, where m∈M and m∈M represents the mth possible discrete position and rotation of the antenna surface, Represents the real number domain. The specific definition of the position and rotation matrix is ​​shown in the following formula (1).

[0070]

[0071] Among them, x m ,y m ,z m is the coordinate of the center of the antenna surface at the mth discrete position in the global Cartesian coordinate system. m ,β m ,γ m Corresponding to the rotation around the x-axis, y-axis and z-axis respectively. The space C defines the three-dimensional area where the antenna surface can move and rotate dynamically in the six-dimensional movable antenna base station.

[0072] Next, define Q = {(q1,u1),(q2,u2),...,(q M ,u M )} is a set of M discrete position-rotation pairs on the antenna surface. Let i b ∈Μ is the index of the position-rotation pair selected for the b-th antenna surface, where b∈Β. Therefore, the position-rotation pair of the b-th antenna surface is (q ib ,u ib )∈Q.

[0073] In addition, in this embodiment of the application, the number of user terminals is set to K and distributed in three-dimensional space. Each user terminal is equipped with a fixed antenna. Assume that there is a multipath channel between each user terminal and the six-dimensional movable antenna base station. The channel from user terminal k to the B antenna surfaces is As shown in the following formula (2).

[0074]

[0075] in, represents the channel from the kth user terminal to all six-dimensional antennas on the bth antenna surface and can be expressed as the following formula (3).

[0076]

[0077] Among them, Γ k represents the total number of channel paths from the kth user terminal to the six-dimensional movable antenna base station, μ ι,k represents the channel coefficient along the path ι from the user terminal k to the central processing unit CPU, a ι,k (q ib ,u ib ) represents the six-dimensional steering vector, g ι,k (u ib ) represents the effective antenna gain of the bth antenna surface. The channels from all K user terminals to the antenna surface at all candidate discrete positions and rotations are given by the following equation (4).

[0078]

[0079] Among them, H m Denote the channels of all six-dimensional antennas from all K user terminals to the candidate position-rotation pairs of the m-th antenna surface, as shown in the following formula (5).

[0080]

[0081] In addition, Denote the channels from all K user terminals to all B antenna surfaces on the six-dimensional movable antenna base station (given fixed B position-rotation pairs) as shown in the following formula (6).

[0082]

[0083] in, is the position / rotation selection matrix, represents Crone Technology, which is defined as shown in the following formula (7).

[0084]

[0085] Based on this, the signal received by the six-dimensional movable antenna base station can be expressed by the following formula (8).

[0086]

[0087] Among them, w:CN(0 NB ,σ 2 I NB ) represents the complex additive white noise vector at the six-dimensional movable antenna base station, with a mean of zero and an average power of σ 2 , I is a vector of all 1s. In the above formula, in is the unit average power normalized transmission signal of user terminal k, and p represents the transmission power of each user terminal. Assuming that the channels between user terminals are statistically independent, the upper bound of the rate at which the six-dimensional mobile antenna base station traverses all user terminals can be obtained as shown in the following formula (9).

[0088]

[0089] in represents the average channel power matrix, whose (m,k) element represents the average power of the channel between user terminal k and all six-dimensional antennas on the antenna surface located at candidate position-rotation pair m, as shown in the following formula (10).

[0090]

[0091] Based on the aforementioned six-dimensional movable antenna base station and its communication architecture with user terminals, the following further describes the channel estimation method and related equipment for the six-dimensional movable antenna base station provided in the embodiments of the present application. The channel estimation method for the six-dimensional movable antenna base station provided in the embodiments of the present application can be applied to the six-dimensional movable antenna base station.

[0092] The channel estimation method of the six-dimensional movable antenna base station in the embodiment of the present application will be described in detail below. Figure 5 , which is an optional flow chart of a channel estimation method for a six-dimensional movable antenna base station provided in an embodiment of the present application, Figure 5 The method may include but is not limited to steps 100 to 300. It is also understood that this embodiment is Figure 5 The order of steps 100 to 300 is not specifically limited, and the order of steps can be adjusted or some steps can be reduced or added according to actual needs.

[0093] Step 100: Obtain a pilot signal sent by a user terminal, perform statistical channel estimation calculation based on the pilot signal and the intermediate matrix, and obtain a channel power matrix and a directional sparsity matrix between the user terminal and the six-dimensional antenna.

[0094] Step 100 is described in detail below.

[0095] In some embodiments, after the six-dimensional movable antenna base station responds to the channel estimation operation between the user terminal, such as Figure 1 The multiple users (including air users and ground users) shown in FIG will send pilot signals to the six-dimensional movable antenna base station through their respective user terminals. Each user terminal generates It is generated according to an independent and identically distributed complex Gaussian distribution with a mean of zero and a variance of 1. Then the six-dimensional movable antenna base station receives these pilot signals through multiple six-dimensional antennas on multiple antenna surfaces (i.e., 6DMA surfaces) The data is then processed by the LPUs located on the respective antenna surfaces.

[0096] In addition, in order to facilitate distributed channel estimation by multiple LPUs, refer to Figure 6 , is a schematic diagram of a candidate position-rotation pair of a six-dimensional antenna provided in an embodiment of the present application. Figure 6 As shown in FIG, the embodiment of the present application divides M position-rotation pairs into B groups of equal size, each group containing M g= M / B position-rotation pairs. The LPUs on each antenna surface are then responsible for estimating the channels between all K user terminals and the subset of position-rotation pairs in group b, where b, b = 1, 2, ..., B. This allows all LPUs to estimate their respective channels in parallel, reducing channel estimation complexity and pilot overhead within each group.

[0097] Based on this, for each LPU, the received signal at the bth candidate position-rotation pair (i.e., position rotation parameter) is As shown in the following formula (11).

[0098]

[0099] in, represents the horizontal stacking of pilot signals sent by all user terminals. The additive white noise matrix with independent and identically distributed items has the following elements: CN(0,σ 2 ).

[0100] Next, based on the pilot signal sent by the user terminal and the noise variance σ of the six-dimensional movable antenna base station 2 The generated intermediate matrix Perform statistical channel estimation calculations to obtain the channel power matrix and directional sparsity matrix between the user terminal and the six-dimensional antenna.

[0101] Among them, statistical channel estimation calculation is performed based on the pilot signal and the intermediate matrix to obtain the channel power matrix and directional sparsity matrix between the user terminal and the six-dimensional antenna, including the following steps 110 to 150.

[0102] Step 110: Obtain power state vector parameters and directional sparsity matrix parameters, obtain intermediate variables based on the maximum likelihood estimation of the pilot signal and the intermediate matrix, and update the power state vector parameters based on the intermediate variables to obtain updated power state vector parameters.

[0103] Step 120: Update the intermediate matrix based on the intermediate variables and the pilot signal to obtain an updated intermediate matrix.

[0104] Step 130: When the updated power state vector parameter is greater than the state vector preset threshold, the directional sparsity matrix parameter is updated to obtain the updated directional sparsity matrix parameter.

[0105] Step 140: iteratively update the updated intermediate matrix as a new intermediate matrix, the updated power state vector parameter as a new power state vector parameter, and the updated directional sparsity matrix parameter as a new directional sparsity matrix parameter, and use the updated directional sparsity matrix parameter after iterative update as the directional sparsity matrix.

[0106] Step 150: Obtain a channel power matrix based on the directional sparsity matrix and the power state vector parameter.

[0107] Steps 110 to 150 are described in detail below.

[0108] In some embodiments, the power state vector parameters are first obtained and initialized and directional sparsity matrix parameters

[0109] Next, for each LPUb=1,2,...,B, and for each index k∈{1,2,...,K}, which corresponds to η m The first element of m ] k The intermediate variable is obtained based on the maximum likelihood estimation of the pilot signal and the intermediate matrix as shown in the following formula (12).

[0110]

[0111] Then based on the newly obtained intermediate variable v * Update the power state vector parameter [η m ] k , get the updated power state vector parameter [η m ] k =[η m ] k +v * .

[0112] And, based on the intermediate variable v * and pilot signal x k Update the intermediate matrix Σ m , get the updated intermediate matrix

[0113] Afterwards, when the updated power state vector parameter is greater than the state vector preset threshold ε>0 (ie [η m ] k >ε), update the direction sparsity matrix parameters, that is, set the sparsity of the direction to 1, that is, [Z] m,k =1.

[0114] Then, the updated intermediate matrix is ​​used as the new intermediate matrix, the updated power state vector parameter is used as the new power state vector parameter, and the updated direction sparsity matrix parameter is used as the new direction sparsity matrix parameter. The above steps are repeated for iterative update until the direction sparsity matrix parameter and the power state vector parameter converge. The current direction sparsity matrix parameter is output as the direction sparsity matrix. and the power state vector parameter as the power state vector

[0115] In the direction of the sparse matrix and power state vector Then, based on the directional sparsity matrix and power state vector Calculate the estimated channel power matrix

[0116] The channel power matrix is ​​obtained based on the directional sparsity matrix and the power state vector parameter, including the following steps 151 to 152.

[0117] Step 151: Determine the directional sparsity between each user terminal and the six-dimensional antenna one by one according to the directional sparsity matrix.

[0118] Step 152: Based on directional sparsity, the channel power matrix between each user terminal and the six-dimensional antenna is obtained according to the power state vector parameter.

[0119] Steps 151 to 152 are described in detail below.

[0120] In some embodiments, when obtaining the directional sparsity matrix and power state vector Then, according to the directional sparsity matrix The directional sparsity between each user terminal and the mth position rotation parameter corresponding to the six-dimensional antenna (ie, the rotation between the discrete position and the antenna surface) is determined one by one.

[0121] Next, based on directional sparsity, the channel power matrix between each user terminal and the six-dimensional antenna is obtained according to the power state vector parameter and the following formula (13).

[0122]

[0123] About to m Substitute and Z into formula (13) to obtain the channel power matrix P.

[0124] Next, based on the estimated channel power of the limited M position-rotation pairs Reconstruct the channel of the six-dimensional antenna in any position and rotation on the antenna surface. Specifically, according to the definition of the six-dimensional movable antenna channel, it has the following formula (14) We have

[0125]

[0126] in Antenna gain g ι,k (u m ,f ι,k) and the pointing vector f of the pilot signal to the six-dimensional movable antenna base station ι,k From the above expression, it can be determined that as long as c is estimated ι,k and f ι,k , the channel can be reconstructed when the antenna is in any position and rotation. In this embodiment, these two parameters are solved by solving the following formula (15).

[0127]

[0128] where p k =[P] :,k , Here [G k ] m,ι =g ι,k (u m ,f ι,k ).

[0129] Next, a sphere is generated at the six-dimensional movable antenna base station and meshed, i.e., G points are uniformly generated on the sphere. Then, the solution to problem (15) is reduced to the following formula (16).

[0130]

[0131] in, is an overcomplete matrix, It only contains Γ k The above problem can be solved by applying the orthogonal matching pursuit algorithm, and we can get Valuation

[0132] and The valuation corresponds to Some columns of has non-zero coefficients.

[0133] Through the above steps 110 to 150, and steps 151 to 152, the power state vector parameters and the directional sparsity matrix parameters are iteratively estimated and updated using the intermediate variables obtained by the maximum likelihood estimation of the pilot signal and the intermediate matrix to obtain accurately estimated directional sparsity matrix and power state vector parameters; and then the directional sparsity matrix and the power state vector parameters are accurately estimated to facilitate the subsequent use of the accurately estimated channel power matrix to rotate and adjust the position of the six-dimensional antenna, so as to improve the accuracy of the channel estimation of the six-dimensional movable antenna base station and the communication rate between the six-dimensional movable antenna base station and the user terminal.

[0134] Step 200: Solve the rate optimization model based on the channel power matrix to obtain the target position rotation parameters, and adjust the position rotation of at least one six-dimensional antenna according to the target position rotation parameters to optimize the transmission rate between the user terminal and the six-dimensional antenna.

[0135] Step 200 is described in detail below.

[0136] In some embodiments, to further improve the communication rate between a six-dimensional movable antenna base station and a user terminal, a rate optimization model is pre-built to optimize the transmission rate between the user terminal and the six-dimensional antenna. The rate optimization model is then solved using a channel power matrix to obtain target position rotation parameters. At least one six-dimensional antenna is then rotated and adjusted based on the target position rotation parameters to optimize the transmission rate between the user terminal and the six-dimensional antenna. The following describes how to build the rate optimization model.

[0137] The steps of constructing the rate optimization model include steps 210 to 240.

[0138] Step 210: A target rate optimization function is obtained based on the product of the channel power matrix parameter, the directional sparsity matrix parameter, and the transmit power of the user terminal and the logarithm thereof.

[0139] Step 220: Obtain the index matrix corresponding to the position rotation parameter and the distance matrix between each two six-dimensional antennas, multiply the index matrix by the product of the transposed matrix of the index matrix and the distance matrix, and then generate a safety distance constraint based on the size relationship between the index distance and the minimum distance.

[0140] Step 230: Generate a binary position rotation selection constraint of position rotation parameters.

[0141] Step 240: Generate a rate optimization model based on the target rate optimization function, the safety distance constraint, the binary position rotation selection constraint, and the position rotation parameters.

[0142] Steps 210 to 240 are described in detail below.

[0143] In some embodiments, first, referring to the above formula (9), based on the product of the channel power matrix parameter P, the position rotation parameter s and the transmit power p of the user terminal, and taking the logarithm, the target rate optimization function is obtained as shown in the following formula (17).

[0144]

[0145] Next, get the index matrix corresponding to the position rotation parameter s And the distance matrix D between each two six-dimensional antennas, based on the product of the transpose matrix of the index matrix and the distance matrix, and then multiplied by the index matrix, the index distance is obtained, and based on the index distance and the minimum distance d min The size relationship between them generates a safety distance constraint as shown in the following formula (18).

[0146]

[0147] Among them, the index matrix It refers to a vector whose index position in the i-th non-zero entry of s is 1 and the rest of the positions are 0. is the dimension of the channel power matrix after channel reconstruction.

[0148] And, generate the binary position rotation selection constraint [s] with position rotation parameter s i ∈{0,1}, Next, based on the target rate optimization function, the safety distance constraint, the binary position rotation selection constraint, and the position rotation parameter, a rate optimization model is generated as shown in the following formula (19).

[0149]

[0150] Next, the rate optimization model (19) is solved based on the obtained channel power matrix p.

[0151] Wherein, solving the rate optimization model based on the channel power matrix to obtain the target position rotation parameter includes the following steps 250 to 270.

[0152] Step 250: Continuously relax the binary position rotation selection constraint, and update the rate optimization model according to the relaxed binary position rotation selection constraint to obtain an update rate optimization model.

[0153] Step 260: Substitute the channel power matrix into the channel power matrix parameters in the update rate optimization model.

[0154] Step 270: Solve the update rate optimization model to obtain the target position rotation parameters.

[0155] In some embodiments, due to the binary position rotation selection constraint [s] in the rate optimization model (19) i ∈{0,1}, It is binary discrete, that is, it is not a convex constraint. Therefore, in this embodiment, the binary position rotation selection constraint is continuously relaxed, and the binary position rotation selection constraint [s] after relaxation is obtained. i ∈[0,1] update rate optimization model (19), and the update rate optimization model is obtained as shown in the following formula (20).

[0156]

[0157] Next, the channel power matrix P is substituted into the channel power matrix parameters in the update rate optimization model (20). Based on this, the update rate optimization model (20) is a convex optimization problem. Next, the update rate optimization model (20) is solved by the relevant convex optimization problem solving method (such as the interior point method, etc.) to obtain the target position rotation parameter s * .

[0158] Through the above steps 210 to 270, a target rate optimization function for optimizing the communication rate between the six-dimensional movable antenna base station and multiple user terminals is obtained based on the Shannon formula and related parameters (including channel power matrix parameters, directional sparsity matrix parameters, and the transmit power of the user terminal). In combination with the constraints related to the position rotation parameters of the multiple six-dimensional antennas on the antenna surface, a rate optimization model for optimizing the communication rate between the six-dimensional movable antenna base station and the multiple user terminals is further constructed. Next, the rate optimization model is solved using the channel power matrix and related relaxation methods to obtain target position rotation parameters that effectively improve the communication rate between the six-dimensional movable antenna base station and the multiple user terminals.

[0159] In some embodiments, after obtaining the target position rotation parameter s * Afterwards, the positions of the selected six-dimensional antennas corresponding to the target position rotation parameters are rotated and adjusted to the target position q corresponding to the target position rotation parameters according to the target position rotation parameters. * and target rotation angle u * , to optimize the transmission rate between the user terminal and the six-dimensional antenna.

[0160] Step 300: After at least one six-dimensional antenna is rotated and adjusted, instantaneous channel estimation is performed based on the pilot signal and the directional sparsity matrix to obtain an instantaneous channel estimation result between the six-dimensional movable antenna base station and the user terminal.

[0161] Step 300 is described in detail below.

[0162] In some embodiments, the positions of the selected six-dimensional antennas corresponding to the target position rotation parameters are rotated and adjusted to the target position q corresponding to the target position rotation parameters. * and target rotation angle u * Afterwards, instantaneous channel estimation is further performed based on the pilot signal x sent by the user terminal and the directional sparsity matrix Z to obtain an instantaneous channel estimation result between the six-dimensional movable antenna base station and the user terminal.

[0163] The instantaneous channel estimation is performed based on the pilot signal and the directional sparsity matrix to obtain the instantaneous channel estimation result between the six-dimensional movable antenna base station and the user terminal, including the following steps 310 to 330.

[0164] Step 310: Obtain a received signal matrix obtained from the pilot signal matrix.

[0165] Step 320: Generate an observation signal matrix based on the pilot signal matrix and the unit diagonal matrix.

[0166] Step 330: Acquire channel estimation parameters, perform instantaneous channel estimation based on the channel estimation parameters, the observed signal matrix, the directional sparsity matrix, and the received signal matrix, and obtain an instantaneous channel estimation result.

[0167] Steps 310 to 330 are described in detail below.

[0168] In some embodiments, the received signal matrix corresponding to the pilot signal matrix X sent by multiple user terminals by the LPU set on each antenna surface is shown in the following formula (21).

[0169]

[0170] By vectorizing formula (18), we can obtain the following formula (22).

[0171] y ib =Ah ib +w ib (twenty two)

[0172] Among them, y ib =vec(Y ib ), w ib =vec(W ib ), h ib =vec(H ib ), A is the observation signal matrix, which is based on the pilot signal matrix X and the unit diagonal matrix I K The result is shown in the following formula (23).

[0173]

[0174] Next, the channel estimation parameter h is obtained, and then based on the channel estimation parameter h, the observation signal matrix A, the directional sparsity matrix Z and the received signal matrix y ib Perform statistical channel estimation calculations to obtain the instantaneous channel estimation result h between the six-dimensional movable antenna base station and multiple user terminals ib .

[0175] The instantaneous channel estimation is performed based on the channel estimation parameters, the observation signal matrix, the directional sparsity matrix and the received signal matrix to obtain the instantaneous channel estimation result, including the following steps 331 to 333.

[0176] Step 331: Generate support vectors based on the directional sparsity matrix and the unit diagonal matrix, and generate a support observation matrix based on the support vectors and the observation matrix.

[0177] Step 332: Based on the product of the support observation matrix and the channel estimation parameter and subtracting the received signal matrix, a target channel estimation function is obtained.

[0178] Step 333: Obtain an instantaneous channel estimation result based on the least squares of the channel estimation function and the channel estimation parameters.

[0179] Steps 331 to 333 are described in detail below.

[0180] In some embodiments, the accuracy of channel estimation is improved by using the previously estimated directional sparsity matrix Z. Specifically, based on the directional sparsity matrix Z and the unit diagonal matrix 1 N×1 Generate support vector j b As shown in the following formula (24).

[0181]

[0182] Then, based on the support vector j b And the observation matrix A is generated to represent the observation matrix A and the support vector j b The corresponding columns of the support observation matrix A jb .

[0183] Based on this, the received signal (22) can be changed to the following formula (25).

[0184] y ib =A jb h ib,jb +w ib (25)

[0185] Then, based on the support observation matrix A jb The product of the channel estimation parameter h and the received signal matrix y is subtracted ib , get the target channel estimation function A jb hy ib .

[0186] Finally, the least squares based on the channel estimation function and channel estimation parameters is shown in the following formula (26).

[0187]

[0188] And by solving the least square formula (26) to obtain the instantaneous channel estimation result h between the six-dimensional movable antenna base station and multiple user terminals ib,jb .

[0189] Through the above steps 310 to 330, and steps 331 to 333, the support vectors generated by the directional sparsity matrix unique to the six-dimensional mobile antenna base station and multiple user terminals, and the support observation matrix further obtained, can effectively improve the accuracy of channel estimation between the six-dimensional mobile antenna base station and the user terminals.

[0190] Reference Figure 7 , is a multi-stage schematic diagram of a channel estimation value of a six-dimensional movable antenna base station provided in an embodiment of the present application. Figure 7 As shown in , the channel estimation between a six-dimensional movable antenna base station and multiple user terminals is divided into three stages (including an estimation stage of statistical channel state information, a position / rotation optimization and adjustment stage of the six-dimensional movable antenna, and a channel estimation stage).

[0191] In the first stage, the candidate position / rotation statistical CSI (i.e., joint directional sparsity and channel power) is estimated: in the long transmission frame, it is assumed that the statistical CSI between all candidate position-rotation pairs of the six-dimensional antenna and all user terminals remains unchanged. Therefore, in the first stage, the statistical CSI (i.e., the channel power matrix) can be estimated based on the directional sparsity matrix Z and the average channel power matrix P (see the relevant description of steps 110 to 150 above for details). It should be noted that during this stage, user terminal communications will continue and will not be disturbed by the movement of the antenna surface.

[0192] In the second stage, the position / rotation optimization and movement of the six-dimensional antenna on the antenna surface are performed: after the first stage, all LPUs send their locally estimated candidate position / rotation statistical CSI to the CPU. With the global statistical CSI, the CPU of the six-dimensional movable antenna base station optimizes the position and rotation of all B antenna surfaces in this stage to maximize the traversal and rate (see steps 210 to 240 above for details). Once the optimized position and rotation of the antenna surface are determined, the antenna surface will be gradually moved to the optimized position and rotation in consecutive communication blocks. Note that similar to the first stage, although the antenna surface moves in this stage, the user terminal communication continues uninterrupted. This is because the movement of these surfaces occurs on a time scale much larger than the coherence time of the instantaneous user terminal channel, and therefore the position / rotation of the antenna surface can be assumed to be constant within the coherence time of each channel (see Figure 7 ).

[0193] In the third phase, after the user terminal communicates at the optimized 6DMA position / rotation with an improved data rate, the directional sparsity matrix is ​​used to perform instantaneous channel estimation: when all antenna surfaces are moved to the optimized positions and rotations in the second phase, the user terminal can communicate with the six-dimensional movable antenna base station with an improved overall rate. In this case, the instantaneous channel between each antenna surface at the optimized position and rotation and the user terminal can be estimated in a distributed manner on the connected LPUs by utilizing the directional sparsity estimated in the first phase (see the description of steps 310 to 330 above for details).

[0194] In order to further verify the reliability of the channel estimation method for the six-dimensional movable antenna base station provided by the present application, the embodiment of the present application also conducts simulation performance verification on the channel estimation method for the six-dimensional movable antenna base station. Assume that discrete candidate position-rotation pairs are uniformly generated on the spherical surface. Set N=4, that is, each 6DMA surface is equipped with a 2×2 array with an antenna element spacing of λ / 2. The number of 6DMA surfaces is B=16, and the number of candidate position-rotation pairs is M=256. The carrier frequency is 2.4GHz, and the wavelength is λ=0.125 meters. Set the number of multipath components of each user terminal to Γ k =100, The multipath channel for each user terminal is generated by first randomly generating the user terminal's location within the coverage area of ​​the six-dimensional mobile antenna base station, assuming this area is a circular area ranging from 30 to 200 meters from the center of the central processing unit (CPU). Random scatterers are then uniformly generated within a circle centered at the user terminal's location. The effective antenna gain is assumed to be a half-space directional antenna pattern.

[0195] It can be understood that the specific descriptions of the performance indicators of directional sparsity estimation, average channel power estimation, and instantaneous channel estimation are as follows: For directional sparsity detection, the detection error rate is used as the performance indicator. The detection error rate is defined as the sum of the missed detection probability and the false alarm probability. In the first stage, the performance of the average channel power estimation is evaluated by the normalized mean square error (NMSE), which is defined as shown in the following formula (27).

[0196]

[0197] in, Represents the estimated value of the channel power matrix P; similarly, the performance of instantaneous CSI estimation in the third stage is evaluated by another NMSE, defined as shown in the following formula (28).

[0198]

[0199] in, represents the estimated value of the instantaneous channel vector h. For performance evaluation, the proposed channel estimation method for a six-dimensional movable antenna base station is compared with the following benchmark schemes. These benchmark schemes all utilize a three-sector base station (a specific example of a six-dimensional movable antenna system, where the total antenna is evenly divided into three parts and placed on each sector), with the antenna plane of each sector covering approximately 120°.

[0200] 1. Continuous 6DMA alternating optimization with known channels: Assume that the instantaneous CSI is perfectly known, and use an alternating optimization method to continuously optimize the antenna position and rotation.

[0201] 2. Discrete 6DMA PSO with known channels: Assume that the instantaneous CSI is perfectly known and use PSO to optimize discrete antenna positions and rotations

[0202] 3. Fixed antenna communication system: The three-dimensional position and three-dimensional rotation of all antennas are fixed.

[0203] 4. Fluid antenna communication system: Assuming that the instantaneous channel information is known and the rotation angles of all antennas remain unchanged, the particle swarm algorithm is used to optimize the positions of all antennas within each sector antenna plane.

[0204] It can be understood that when multiple antenna surfaces on a six-dimensional movable antenna base station receive pilot signals from K user terminals, the corresponding channel gain H will produce a "block-sparse" pattern. This is because the channel of each user terminal has directional sparsity for all candidate position-rotation pairs, and all N antennas for a given position-rotation pair have the same channel power distribution.

[0205] Reference Figure 8 , is a simulation performance diagram of the first channel estimation method for a six-dimensional movable antenna base station provided in an embodiment of the present application. Figure 8 As shown in , the relationship between the detection error rate and the pilot sequence length L is presented. Figure 6 It can be observed that the detection error rate of all the considered algorithms decreases with the increase of pilot length. Compared with the traditional approximate message passing algorithm and block orthogonal matching pursuit algorithm, the channel estimation method for the six-dimensional movable antenna base station proposed in this application greatly saves the pilot length.

[0206] Reference Figure 9 , is a simulation performance diagram of the second channel estimation method for a six-dimensional movable antenna base station provided in an embodiment of the present application. Figure 9As shown in Figure 3, the proposed channel estimation method for a six-dimensional mobile antenna base station is compared with the traditional approximate message passing algorithm and the block orthogonal matching pursuit algorithm in terms of channel average channel power estimation. As the pilot length L increases, the NMSE of the proposed channel estimation method for a six-dimensional mobile antenna base station decreases faster than that of the two baseline schemes in channel power estimation.

[0207] Reference Figure 10 , is a simulation performance diagram of the third channel estimation method for a six-dimensional movable antenna base station provided in an embodiment of the present application. Figure 10 As shown in , the normalized mean square error (NMSE) of the instantaneous CSI estimation varies with the pilot sequence length L. As L increases, it can be seen that the NMSE of both the proposed directional sparsity-assisted least squares algorithm and the traditional least squares algorithm (which does not utilize directional sparsity) decreases. However, to achieve the same estimation accuracy, the pilot overhead (i.e., L) required by the channel estimation method for a six-dimensional mobile antenna base station proposed in this application is much lower than that of the baseline least squares solution.

[0208] Reference Figure 11 , is a simulation performance diagram of the fourth channel estimation method for a six-dimensional movable antenna base station provided in an embodiment of the present application. Figure 11 As shown in , the total rates of the considered schemes are compared under changes in the number of user terminals. First, it is observed that the proposed six-dimensional movable antenna position and rotation optimization algorithm based on channel power performs better than the comparison scheme. This is because the channel estimation method of the six-dimensional movable antenna base station proposed in this application has more spatial degrees of freedom and can more effectively deploy antenna resources to match the spatial distribution of user terminals. The fluid antenna scheme can only adjust the antenna position within a two-dimensional surface, and its antenna spatial degrees of freedom are limited. Secondly, it is observed that as the number of user terminals increases, the performance gap becomes larger. This shows that the six-dimensional movable antenna base station and algorithm design proposed in this application have particular advantages in scenarios with high network load and more severe interference.

[0209] Therefore, the channel estimation method of the six-dimensional movable antenna base station proposed in the present application can provide a high-precision channel estimation and communication solution for the wireless communication system.

[0210] The channel estimation method and related equipment of a six-dimensional movable antenna base station proposed in an embodiment of the present application, the six-dimensional movable antenna base station is provided with at least one antenna surface, the antenna surface includes at least one six-dimensional antenna, the method includes: first, obtaining a pilot signal sent by a user terminal, obtaining a power state vector parameter and a directional sparsity matrix parameter, obtaining an intermediate variable based on the maximum likelihood estimation value of the pilot signal and the intermediate matrix, and updating the power state vector parameter based on the intermediate variable to obtain an updated power state vector parameter, updating the intermediate matrix based on the intermediate variable and the pilot signal to obtain an updated intermediate matrix, when the updated power state vector parameter is greater than a preset threshold of the state vector, updating the directional sparsity matrix parameter to obtain an updated directional sparsity matrix parameter, using the updated intermediate matrix as a new intermediate matrix, using the updated power state vector parameter as a new power state vector parameter, using the updated directional sparsity matrix parameter as a new directional sparsity matrix parameter for iterative update, and using the updated directional sparsity matrix parameter after iterative update as a directional sparsity matrix, determining the directional sparsity between each user terminal and the six-dimensional antenna one by one according to the directional sparsity matrix, and obtaining the directional sparsity between each user terminal and the six-dimensional antenna based on the power state vector parameter based on the directional sparsity. The channel power matrix between the two, the intermediate matrix is ​​obtained based on the noise variance of the six-dimensional movable antenna base station; then, based on the product of the channel power matrix parameters, the directional sparsity matrix parameters and the transmit power of the user terminal, and taking the logarithm, a target rate optimization function is obtained, the index matrix corresponding to the position rotation parameter and the distance matrix between each two six-dimensional antennas are obtained, based on the product of the transpose matrix of the index matrix and the distance matrix, and then multiplied by the index matrix to obtain the index distance, and based on the size relationship between the index distance and the minimum distance, a safety distance constraint is generated, and a binary position rotation selection constraint of the position rotation parameter is generated, and a rate optimization model is generated based on the target rate optimization function, the safety distance constraint, the binary position rotation selection constraint and the position rotation parameter; then, the binary position rotation selection constraint is continuously relaxed, and the rate optimization model is updated according to the relaxed binary position rotation selection constraint to obtain an update rate optimization model, the channel power matrix is ​​substituted into the channel power matrix parameters in the update rate optimization model, the update rate optimization model is solved to obtain the target position rotation parameter, and the position rotation adjustment of at least one six-dimensional antenna is performed according to the target position rotation parameter to optimize the transmission rate between the user terminal and the six-dimensional antenna;Finally, after rotating and adjusting the position of at least one six-dimensional antenna, a received signal matrix is ​​obtained from a pilot signal matrix, the pilot signal matrix including multiple pilot signals. An observation signal matrix is ​​generated based on the pilot signal matrix and a unit diagonal matrix, and channel estimation parameters are obtained. Support vectors are generated based on the directional sparsity matrix and the unit diagonal matrix, and a support-observation matrix is ​​generated based on the support vectors and the observation matrix. A target channel estimation function is obtained by multiplying the support-observation matrix by the channel estimation parameters and subtracting the received signal matrix from the product. An instantaneous channel estimation result is obtained based on a least squares method of the channel estimation function and the channel estimation parameters.

[0211] The embodiment of the present application uses intermediate variables obtained by maximum likelihood estimation of pilot signals and intermediate matrices to iteratively estimate and update power state vector parameters and directional sparsity matrix parameters to obtain accurately estimated directional sparsity matrix and power state vector parameters; then uses the directional sparsity matrix and power state vector parameters to accurately estimate the channel power matrix, so that the position rotation adjustment of the six-dimensional antenna can be performed subsequently using the accurately estimated channel power matrix to improve the accuracy of channel estimation of the six-dimensional movable antenna base station and the communication rate between the six-dimensional movable antenna base station and the user terminal; and, uses the Shannon formula and related parameters (including channel power matrix parameters, directional sparsity matrix parameters and the transmit power of the user terminal) to obtain a target rate optimization function for optimizing the communication rate between the six-dimensional movable antenna base station and multiple user terminals, and combines the constraints related to the position rotation parameters of multiple six-dimensional antennas on the antenna surface to further construct a rate optimization model for optimizing the communication rate between the six-dimensional movable antenna base station and multiple user terminals. Next, the rate optimization model is solved using the channel power matrix and the related relaxation method to obtain the target position rotation parameters that effectively improve the communication rate between the six-dimensional movable antenna base station and multiple user terminals; and, the intermediate matrix obtained by the pilot signal emitted by the user terminal and the noise variance of the six-dimensional movable antenna base station is estimated to obtain the channel power matrix between the six-dimensional movable antenna base station and the user terminal and the directional sparsity matrix corresponding to the directional sparsity generated between different six-dimensional antennas and the user terminal; then, the six-dimensional antenna is adjusted using the channel power matrix and the target position rotation parameters obtained by the rate optimization model to further effectively improve the transmission rate between the user terminal and the six-dimensional antenna, and after fixing the six-dimensional antenna at this moment, the pilot signal and the directional sparsity matrix are used to perform instantaneous channel estimation to obtain an accurate instantaneous channel estimation result between the six-dimensional movable antenna base station and the user terminal, thereby effectively improving the information transmission rate between the six-dimensional movable antenna base station and the user terminal, and also improving the accuracy of the channel estimation between the six-dimensional movable antenna base station and the Internet of Things terminal.

[0212] The embodiment of the present application also provides a six-dimensional movable antenna base station, which can implement the channel estimation method of the six-dimensional movable antenna base station. Figure 2 The six-dimensional movable antenna base station is provided with at least one antenna surface and a central processor, and each antenna surface is provided with a six-dimensional antenna and local computing hardware.

[0213] Local computing hardware is used to obtain the pilot signal sent by the user terminal and perform statistical channel estimation based on the pilot signal and the intermediate matrix to obtain the channel power matrix and directional sparsity matrix between the user terminal and the six-dimensional antenna. The intermediate matrix is ​​obtained based on the noise variance of the six-dimensional movable antenna base station.

[0214] A central processing unit is configured to solve a rate optimization model based on a channel power matrix to obtain target position rotation parameters, and perform position rotation adjustment on the six-dimensional antenna according to the target position rotation parameters. The rate optimization model includes a target rate optimization function between the user terminal and the six-dimensional antenna.

[0215] The local computing hardware is also used to perform instantaneous channel estimation based on the pilot signal and the directional sparsity matrix to obtain the instantaneous channel estimation result between the six-dimensional movable antenna base station and the user terminal.

[0216] In some embodiments, the local computing hardware is also used to:

[0217] Obtaining a power state vector parameter and a directional sparsity matrix parameter, obtaining an intermediate variable based on a maximum likelihood estimate of a pilot signal and an intermediate matrix, and updating the power state vector parameter based on the intermediate variable to obtain an updated power state vector parameter;

[0218] updating the intermediate matrix based on the intermediate variable and the pilot signal to obtain an updated intermediate matrix;

[0219] When the updated power state vector parameter is greater than a preset state vector threshold, updating the direction sparsity matrix parameter to obtain an updated direction sparsity matrix parameter;

[0220] Iteratively updating the updated intermediate matrix as a new intermediate matrix, the updated power state vector parameter as a new power state vector parameter, and the updated direction sparsity matrix parameter as a new direction sparsity matrix parameter, and using the updated direction sparsity matrix parameter after iterative update as the direction sparsity matrix;

[0221] The channel power matrix is ​​obtained based on the directional sparsity matrix and the power state vector parameters.

[0222] In some embodiments, the local computing hardware is also used to:

[0223] Determine the directional sparsity between each user terminal and the six-dimensional antenna one by one according to the directional sparsity matrix;

[0224] Based on directional sparsity, the channel power matrix between each user terminal and the six-dimensional antenna is obtained according to the power state vector parameters.

[0225] In some embodiments, the central processing unit is further configured to:

[0226] A target rate optimization function is obtained by multiplying the channel power matrix parameters, the directional sparsity matrix parameters, and the transmit power of the user terminal and taking the logarithm thereof.

[0227] Obtain the index matrix corresponding to the position rotation parameter and the distance matrix between each two six-dimensional antennas. Multiply the index matrix by the product of the transposed matrix of the index matrix and the distance matrix, and then multiply it by the index matrix to obtain the index distance. Then generate a safety distance constraint based on the size relationship between the index distance and the minimum distance.

[0228] Generate binary position and rotation selection constraints with position and rotation parameters;

[0229] A rate optimization model is generated based on the target rate optimization function, safety distance constraints, binary position rotation selection constraints, and position rotation parameters.

[0230] In some embodiments, the central processing unit is further configured to:

[0231] Continuously relaxing the binary position rotation selection constraint, and updating the rate optimization model according to the relaxed binary position rotation selection constraint to obtain an update rate optimization model;

[0232] Substitute the channel power matrix into the channel power matrix parameters in the update rate optimization model;

[0233] The update rate optimization model is solved to obtain the target position rotation parameters.

[0234] In some embodiments, the local computing hardware is also used to:

[0235] Acquire a received signal matrix obtained from a pilot signal matrix, where the pilot signal matrix includes a plurality of pilot signals;

[0236] Generate an observation signal matrix based on the pilot signal matrix and the unit diagonal matrix;

[0237] Acquire channel estimation parameters, perform instantaneous channel estimation based on the channel estimation parameters, the observation signal matrix, the directional sparsity matrix, and the received signal matrix, and obtain an instantaneous channel estimation result.

[0238] In some embodiments, the local computing hardware is also used to:

[0239] Generate support vectors based on the directional sparsity matrix and the unit diagonal matrix, and generate a support observation matrix based on the support vectors and the observation matrix;

[0240] Based on the product of the support observation matrix and the channel estimation parameters and subtracting the received signal matrix, the target channel estimation function is obtained;

[0241] Based on the least squares of the channel estimation function and the channel estimation parameters, the instantaneous channel estimation result is obtained.

[0242] In the above embodiments, the description of each embodiment has its own focus. For the part that is not described in detail in a certain embodiment, the specific implementation method of the channel estimation device of the six-dimensional movable antenna base station is basically the same as the specific implementation method of the channel estimation method of the above-mentioned six-dimensional movable antenna base station, and will not be repeated here.

[0243] In an embodiment of the present application, the six-dimensional movable antenna base station uses intermediate variables obtained by maximum likelihood estimation of the pilot signal and the intermediate matrix to iteratively estimate and update the power state vector parameters and the directional sparsity matrix parameters to obtain accurately estimated directional sparsity matrix and power state vector parameters; then the directional sparsity matrix and the power state vector parameters are used to accurately estimate the channel power matrix, so that the position rotation adjustment of the six-dimensional antenna can be performed using the accurately estimated channel power matrix to improve the accuracy of the channel estimation of the six-dimensional movable antenna base station and the communication rate between the six-dimensional movable antenna base station and the user terminal; and, based on the Shannon formula and related parameters (including channel power matrix parameters, directional sparsity matrix parameters and the transmit power of the user terminal), a target rate optimization function for optimizing the communication rate between the six-dimensional movable antenna base station and multiple user terminals is obtained, and combined with the constraints related to the position rotation parameters of the multiple six-dimensional antennas on the antenna surface, a rate optimization model for optimizing the communication rate between the six-dimensional movable antenna base station and multiple user terminals is further constructed. Next, the rate optimization model is solved using the channel power matrix and the related relaxation method to obtain the target position rotation parameters that effectively improve the communication rate between the six-dimensional movable antenna base station and multiple user terminals; and, the intermediate matrix obtained by the pilot signal emitted by the user terminal and the noise variance of the six-dimensional movable antenna base station is estimated to obtain the channel power matrix between the six-dimensional movable antenna base station and the user terminal and the directional sparsity matrix corresponding to the directional sparsity generated between different six-dimensional antennas and the user terminal; then, the six-dimensional antenna is adjusted using the channel power matrix and the target position rotation parameters obtained by the rate optimization model to further effectively improve the transmission rate between the user terminal and the six-dimensional antenna, and after fixing the six-dimensional antenna at this moment, the pilot signal and the directional sparsity matrix are used to perform instantaneous channel estimation to obtain an accurate instantaneous channel estimation result between the six-dimensional movable antenna base station and the user terminal, thereby effectively improving the information transmission rate between the six-dimensional movable antenna base station and the user terminal, and also improving the accuracy of the channel estimation between the six-dimensional movable antenna base station and the Internet of Things terminal.

[0244] An embodiment of the present application further provides an electronic device, including:

[0245] at least one memory;

[0246] at least one processor;

[0247] at least one program;

[0248] The program is stored in the memory, and the processor executes at least one program to implement the channel estimation method for the six-dimensional movable antenna base station implemented in the present application. The electronic device can be any smart terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), an in-vehicle computer, etc.

[0249] See also Figure 12 , Figure 12 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0250] The processor 1201 can be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0251] The memory 1202 can be implemented in the form of ROM (Read Only Memory), static storage device, dynamic storage device or RAM (Random Access Memory). The memory 1202 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1202, and the processor 1201 calls and executes the channel estimation method for the six-dimensional movable antenna base station of the embodiment of the present application;

[0252] Input / output interface 1203, used to implement information input and output;

[0253] Communication interface 1204, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0254] Bus 1205 , which transmits information between various components of the device (e.g., processor 1201 , memory 1202 , input / output interface 1203 , and communication interface 1204 );

[0255] The processor 1201 , the memory 1202 , the input / output interface 1203 and the communication interface 1204 are connected to each other in communication within the device via a bus 1205 .

[0256] An embodiment of the present application further provides a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, the channel estimation method of the six-dimensional movable antenna base station is implemented.

[0257] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0258] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0259] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0260] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0261] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0262] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0263] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0264] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0265] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0266] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0267] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0268] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A channel estimation method for a six-dimensional movable antenna base station, characterized in that: The six-dimensional movable antenna base station is provided with at least one antenna surface, wherein the antenna surface includes at least one six-dimensional antenna, and the method includes: Obtaining a pilot signal sent by a user terminal, performing a statistical channel estimation calculation based on the pilot signal and an intermediate matrix to obtain a channel power matrix and a directional sparsity matrix between the user terminal and the six-dimensional antenna, wherein the intermediate matrix is ​​obtained based on the noise variance of the six-dimensional movable antenna base station; Solving a rate optimization model based on the channel power matrix to obtain a target position rotation parameter, and performing position rotation adjustment on at least one of the six-dimensional antennas according to the target position rotation parameter to optimize the transmission rate between the user terminal and the six-dimensional antenna; After at least one of the six-dimensional antennas is rotated and adjusted, instantaneous channel estimation is performed based on the pilot signal and the directional sparsity matrix to obtain an instantaneous channel estimation result between the six-dimensional movable antenna base station and the user terminal; The performing statistical channel estimation calculation based on the pilot signal and the intermediate matrix to obtain a channel power matrix and a directional sparsity matrix between the user terminal and the six-dimensional antenna includes: Acquiring a power state vector parameter and a directional sparsity matrix parameter, obtaining an intermediate variable based on a maximum likelihood estimate of the pilot signal and the intermediate matrix, and updating the power state vector parameter based on the intermediate variable to obtain an updated power state vector parameter; updating the intermediate matrix based on the intermediate variable and the pilot signal to obtain an updated intermediate matrix; When the updated power state vector parameter is greater than a preset state vector threshold, updating the directional sparsity matrix parameter to obtain an updated directional sparsity matrix parameter; Iteratively updating the updated intermediate matrix as a new intermediate matrix, the updated power state vector parameter as a new power state vector parameter, and the updated directional sparsity matrix parameter as a new directional sparsity matrix parameter, repeating the above steps for iterative updating until the directional sparsity matrix parameter and the power state vector parameter converge, outputting the current directional sparsity matrix parameter as the directional sparsity matrix and the power state vector parameter as the power state vector; An estimated channel power matrix is ​​obtained by calculation based on the directional sparsity matrix and the power state vector.

2. The channel estimation method for a six-dimensional movable antenna base station according to claim 1, characterized in that: There are multiple user terminals, and obtaining the channel power matrix based on the directional sparsity matrix and the power state vector parameter includes: Determining the directional sparsity between each user terminal and the six-dimensional antenna one by one according to the directional sparsity matrix; Based on the directional sparsity, the channel power matrix between each user terminal and the six-dimensional antenna is obtained according to the power state vector parameter.

3. The channel estimation method for a six-dimensional movable antenna base station according to claim 1, wherein: There are multiple six-dimensional antennas, and the steps of constructing the rate optimization model include: Obtaining a target rate optimization function based on a product of a channel power matrix parameter, a directional sparsity matrix parameter, and a transmit power of the user terminal and taking a logarithm thereof; Obtain an index matrix corresponding to the position rotation parameter and a distance matrix between each two of the six-dimensional antennas, obtain an index distance based on the product of the transposed matrix of the index matrix and the distance matrix, and then multiply the index matrix by the product, and generate a safety distance constraint based on the size relationship between the index distance and the minimum distance; generating a binary position rotation selection constraint for the position rotation parameter; The rate optimization model is generated based on the target rate optimization function, the safety distance constraint, the binary position rotation selection constraint, and the position rotation parameter.

4. The channel estimation method for a six-dimensional movable antenna base station according to claim 3, characterized in that: Solving the rate optimization model based on the channel power matrix to obtain the target position rotation parameter includes: Continuously relaxing the binary position rotation selection constraint, and updating the rate optimization model according to the relaxed binary position rotation selection constraint to obtain an updated rate optimization model; Substituting the channel power matrix into the channel power matrix parameters in the update rate optimization model; The update rate optimization model is solved to obtain the target position rotation parameters.

5. The channel estimation method for a six-dimensional movable antenna base station according to claim 1, wherein performing instantaneous channel estimation based on the pilot signal and the directional sparsity matrix to obtain an instantaneous channel estimation result between the six-dimensional movable antenna base station and the user terminal comprises: Acquire a received signal matrix obtained from a pilot signal matrix, wherein the pilot signal matrix includes a plurality of the pilot signals; generating an observation signal matrix based on the pilot signal matrix and the unit diagonal matrix; Acquire channel estimation parameters, perform instantaneous channel estimation based on the channel estimation parameters, the observation signal matrix, the directional sparsity matrix, and the received signal matrix, and obtain the instantaneous channel estimation result.

6. The channel estimation method for a six-dimensional movable antenna base station according to claim 5, wherein the instantaneous channel estimation is performed based on the channel estimation parameters, the observation signal matrix, the directional sparsity matrix, and the received signal matrix to obtain the instantaneous channel estimation result, comprising: generating support vectors based on the directional sparsity matrix and the unit diagonal matrix, and generating a support observation matrix based on the support vectors and the observation matrix; Obtaining a target channel estimation function based on the product of the support observation matrix and the channel estimation parameter and subtracting the received signal matrix; The instantaneous channel estimation result is obtained based on the least squares of the channel estimation function and the channel estimation parameters.

7. A six-dimensional movable antenna base station, characterized in that: include: The six-dimensional movable antenna base station is provided with at least one antenna surface and a central processing unit, wherein the antenna surface is provided with a six-dimensional antenna and local computing hardware; The local computing hardware is configured to obtain a pilot signal transmitted by a user terminal, perform statistical channel estimation calculations based on the pilot signal and an intermediate matrix, and obtain a channel power matrix and a directional sparsity matrix between the user terminal and the six-dimensional antenna, wherein the intermediate matrix is ​​obtained based on the noise variance of the six-dimensional movable antenna base station; The central processing unit is configured to solve a rate optimization model based on the channel power matrix to obtain a target position rotation parameter, and perform position rotation adjustment on the six-dimensional antenna according to the target position rotation parameter, wherein the rate optimization model includes a target rate optimization function between the user terminal and the six-dimensional antenna; The local computing hardware is further configured to perform instantaneous channel estimation based on the pilot signal and the directional sparsity matrix to obtain an instantaneous channel estimation result between the six-dimensional movable antenna base station and the user terminal; The performing statistical channel estimation calculation based on the pilot signal and the intermediate matrix to obtain a channel power matrix and a directional sparsity matrix between the user terminal and the six-dimensional antenna includes: Acquiring a power state vector parameter and a directional sparsity matrix parameter, obtaining an intermediate variable based on a maximum likelihood estimate of the pilot signal and the intermediate matrix, and updating the power state vector parameter based on the intermediate variable to obtain an updated power state vector parameter; updating the intermediate matrix based on the intermediate variable and the pilot signal to obtain an updated intermediate matrix; When the updated power state vector parameter is greater than a preset state vector threshold, updating the directional sparsity matrix parameter to obtain an updated directional sparsity matrix parameter; Iteratively updating the updated intermediate matrix as a new intermediate matrix, the updated power state vector parameter as a new power state vector parameter, and the updated directional sparsity matrix parameter as a new directional sparsity matrix parameter, repeating the above steps for iterative updating until the directional sparsity matrix parameter and the power state vector parameter converge, outputting the current directional sparsity matrix parameter as the directional sparsity matrix and the power state vector parameter as the power state vector; An estimated channel power matrix is ​​obtained by calculation based on the directional sparsity matrix and the power state vector.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the channel estimation method for the six-dimensional movable antenna base station according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the channel estimation method for the six-dimensional movable antenna base station according to any one of claims 1 to 6 is implemented.

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