A data processing method and communication device

Abstract

This application provides a data processing method and a communication device, relating to the field of communication technology. The method can be used for both downlink and uplink data processing. The communication device can receive a reference signal and obtain a first matrix based on the reference signal. The first matrix is ​​an autocorrelation matrix of dimension N rows and N columns, where N represents the number of channels in the antenna array. Based on the first matrix, the channels in different rows of the antenna array are averaged to obtain a second matrix, which has dimensions of N / (x×R) rows and N / (x×R) columns. Here, R represents the number of channel rows in the antenna array; x represents the polarization of the antenna array; when the antenna array is a single-polarized antenna array, x is 1; when the antenna array is a dual-polarized antenna array, x is 2. This application improves data processing efficiency by reducing the dimensionality of the first matrix, and by averaging the channels in different rows of the antenna array, it reduces the impact of channel row correlation interference.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a data processing method and a communication device. Background Technology

[0002] Traditional cell splitting schemes split the original cell into multiple horizontal or vertical physical cells by superimposing different beam weights onto the same physical antenna. The terminal can obtain channel information by measuring the weighted pilot signal and use a measurement algorithm to select the codebook information with the highest matching degree with the channel information for feedback. The base station can then use the weights and the codebook information fed back by the terminal to precode and weight the downlink data.

[0003] In the above process, the channel information fed back by the terminal is quantized, which has a large error compared with the real downlink channel information, resulting in low weight performance and low data processing efficiency. Therefore, how to design a data processing method to improve weight performance and data processing efficiency has become an urgent problem to be solved. Summary of the Invention

[0004] This application provides a data processing method and a communication device that can improve weight performance and thus improve data processing efficiency.

[0005] Firstly, this application provides a data processing method that can be applied to a first communication device or a second communication device. The first communication device can be understood as a network device, such as a transmission reception point (TRP), a 5G base station (gnodeB, gNB), etc., or as a module (e.g., a chip) within a network device. The second communication device can be understood as a terminal device, such as user equipment (UE), in-vehicle equipment, etc., or as a module (e.g., a chip) within a terminal device. This application does not specifically limit the definition of the second communication device.

[0006] The following example uses the first communication device as a network device and the second communication device as a terminal device. During downlink data transmission, the network device obtains the reference signal fed back by the terminal device, and then performs channel estimation based on the reference signal to obtain a first matrix. This first matrix is ​​an autocorrelation matrix with dimensions of N rows and N columns, where N represents the number of channels in the antenna array. Based on this first matrix, the network device averages the channels between different rows of the antenna array to obtain a second matrix. This second matrix has dimensions of N / (x×R) rows and N / (x×R) columns, where N, R, and x are positive integers. R represents the number of channel rows in the antenna array, and x represents the polarization of the antenna array. When the antenna array is a single-polarized antenna array, x is 1; when the antenna array is a dual-polarized antenna array, x is 2.

[0007] During uplink data transmission, the terminal device can also perform the above data processing operations, which will not be elaborated here.

[0008] The aforementioned reference signal may be a channel sounding reference signal (SRS), a channel state information reference signal (CSI-RS), and a demodulation reference signal (DMRS), or other reference signals. The network device or terminal device estimates the channel condition by measuring the reference signal and obtains a first matrix related to the channel condition. If the operation is performed by the network device, the first matrix is ​​the uplink autocorrelation matrix; if the operation is performed by the terminal device, the first matrix is ​​the downlink autocorrelation matrix.

[0009] Typically, the dimension of the first matrix is ​​related to the number of channels in the antenna array. For example, if the antenna array of a network device has 32 channels, then the dimension of the first matrix is ​​32×32, i.e., 32 rows and 32 columns. In this method, the dimension of the first matrix is ​​relatively large, resulting in high computational complexity. This application, when obtaining the dimension of the second matrix, considers not only the number of channels in the antenna array but also the number of channel rows and the polarization of the antenna array. For example, if the antenna array of a network device has 32 channels, 2 channel rows, and is a dual-polarized antenna array, then the dimension of the second matrix is ​​32 / (2×2) rows and 32 / (2×2) columns, i.e., 8 rows and 8 columns. The dimension of the second matrix obtained in this application is relatively low, resulting in less data volume and higher data processing efficiency during data calculation. Furthermore, since the channels in different rows of the antenna array are averaged based on the first matrix, the influence of channel row correlation interference is reduced, improving the accuracy of the second matrix and thus improving the performance of the weights.

[0010] In one alternative approach, based on the first matrix, the channels between different rows of the antenna array are averaged to obtain a second matrix, including: obtaining x×R matrices with dimensions of N / (x×R) rows and N / (x×R) columns along the main diagonal of the first matrix, and averaging the x×R matrices with dimensions of N / (x×R) rows and N / (x×R) columns to obtain the second matrix.

[0011] It should be noted that the matrix with x×R dimensions and N / (x×R) rows and N / (x×R) columns obtained by the above method is a row-polarized matrix. The second matrix obtained by averaging the row-polarized matrix reduces the influence of row correlation interference, improves the accuracy of the second matrix, and thus improves the performance of the weights.

[0012] In one alternative approach, a third matrix is ​​obtained based on the second matrix and the transformation matrix, the transformation matrix being related to the uplink and downlink frequencies, the third matrix being the product of the transformation matrix and the second matrix; weights are obtained based on the third matrix, and the data is weighted based on the weights.

[0013] When the first matrix is ​​the uplink autocorrelation matrix, the third matrix is ​​the downlink autocorrelation matrix; when the first matrix is ​​the downlink matrix, the third matrix is ​​the uplink autocorrelation matrix. This transformation matrix can utilize the reciprocity of the angular power spectrum of the uplink and downlink channels to obtain the downlink autocorrelation matrix from the uplink autocorrelation matrix, or vice versa.

[0014] In one alternative approach, the transformation matrix is ​​obtained based on the actual radiation pattern of the antenna channel and a pre-defined mathematical theorem; the pre-defined mathematical theorem is either the projection theorem or the series theorem.

[0015] The transformation matrix is ​​obtained by using the actual radiation pattern of the antenna channel and pre-defined mathematical theorems, ensuring its accuracy. When the accuracy of the transformation matrix is ​​high, the third matrix obtained based on it is even more accurate. Calculations based on this accurate third matrix yield more precise weights, thereby improving weighting performance.

[0016] In one alternative approach, the first matrix is ​​subjected to singular value decomposition (SVD), and eigenvalues ​​smaller than a first preset threshold are set to zero to obtain a corrected first matrix; based on the corrected first matrix, a second matrix is ​​obtained. By using SVD and the operation of setting small eigenvalues ​​to zero (i.e., removing eigenvalues ​​smaller than the first preset threshold), the error caused by insufficient statistics in the first matrix can be reduced, thereby improving the accuracy of the first matrix and thus improving the performance of the weights.

[0017] In one alternative approach, SVD is performed on the transformation matrix to zero out eigenvalues ​​smaller than a second preset threshold, resulting in a corrected transformation matrix. A third matrix is ​​then obtained based on the second matrix and the corrected transformation matrix. By performing SVD and zeroing out small eigenvalues ​​on the transformation matrix (i.e., removing eigenvalues ​​smaller than the second preset threshold), the cross-correlation effect between sub-paths in the transformation matrix can be reduced, thereby improving the accuracy of the transformation matrix and ultimately enhancing the performance of the weights.

[0018] In one alternative approach, when the sub-path power contained in the second matrix is ​​negative, the sub-path power is set to zero. By setting the negative sub-path power to zero, the performance impact of negative power paths can be effectively reduced, the accuracy of the third matrix can be improved, and thus the performance of the weights can be enhanced.

[0019] In one alternative approach, the signal increment and interference increment can be obtained using a third matrix and weights. Based on the obtained channel increment and interference increment, an adaptive selection can be made as to whether to perform a correction operation, thereby obtaining a better weight.

[0020] In one alternative approach, the reference signal includes one of the following: SRS, CSI-RS, or DMRS.

[0021] Secondly, this application provides a communication device, which can be understood as a network device, such as a TRP or gNB, or as a module (e.g., a chip) within a network device, or as a terminal device, such as a user UE or in-vehicle device, or as a module (e.g., a chip) within a terminal device. This application does not specifically limit the definition herein. The communication device may include a processing unit and a transceiver unit.

[0022] It should be understood that the transceiver unit can be referred to as an input / output unit, communication unit, etc. When the communication device is a terminal device, the input / output unit can be a transceiver; the processing unit can be a processor. When the communication device is a module (e.g., a chip) in a terminal device, the input / output unit can be an input / output interface, input / output circuit, or input / output pin, etc., and can also be referred to as an interface, communication interface, or interface circuit, etc.; the processing unit can be a processor, processing circuit, or logic circuit, etc.

[0023] The transceiver unit is used to receive the feedback reference signal; the processing unit is used to obtain a first matrix based on the reference signal, the first matrix being an autocorrelation matrix with dimensions of N rows and N columns, where N represents the number of channels in the antenna array; based on the first matrix, the channels in different rows of the antenna array are averaged to obtain a second matrix, the second matrix having dimensions of N / (x×R) rows, where N / (x×R) are positive integers, R represents the number of channel rows in the antenna array, and x represents the polarization of the antenna array. When the antenna array is a single-polarized antenna array, x is 1, and when the antenna array is a dual-polarized antenna array, x is 2.

[0024] The aforementioned reference signal may be SRS, CSI-RS, or DMRS, or other reference signals. Network devices or terminal devices measure the reference signal to estimate the channel condition and obtain a first matrix related to the channel condition. If this operation is performed by a network device, the first matrix is ​​the uplink autocorrelation matrix; if the operation is performed by a terminal device, the first matrix is ​​the downlink autocorrelation matrix. Furthermore, when the first matrix is ​​the uplink autocorrelation matrix, the third matrix is ​​the downlink autocorrelation matrix; and when the first matrix is ​​the downlink autocorrelation matrix, the third matrix is ​​the uplink autocorrelation matrix.

[0025] The aforementioned reference signal may be a channel sounding reference signal (SRS), a channel state information reference signal (CSI-RS), and a demodulation reference signal (DMRS), or other reference signals. The network device or terminal device estimates the channel condition by measuring the reference signal and obtains a first matrix related to the channel condition. If the operation is performed by the network device, the first matrix is ​​the uplink autocorrelation matrix; if the operation is performed by the terminal device, the first matrix is ​​the downlink autocorrelation matrix.

[0026] Typically, the dimension of the first matrix is ​​related to the number of channels in the antenna array. For example, if the antenna array of a network device has 32 channels, then the dimension of the first matrix is ​​32×32. In this approach, the dimension of the first matrix is ​​relatively large, resulting in high computational complexity. This application, when obtaining the dimension of the second matrix, considers not only the number of channels in the antenna array but also the number of channel rows and the polarization of the antenna array. For example, if the antenna array of a network device has 32 channels, 2 channel rows, and is a dual-polarized antenna array, then the dimension of the second matrix is ​​32 / (2×2) rows and 32 / (2×2) columns, i.e., 8 rows and 8 columns. The dimension of the second matrix obtained in this application is relatively low, resulting in less data volume and higher data processing efficiency during data calculation. Furthermore, since the channels in different rows of the antenna array are averaged based on the first matrix, the influence of channel row correlation interference is reduced, improving the accuracy of the second matrix and thus improving the performance of the weights.

[0027] In one alternative approach, the processing unit is configured to: obtain x×R matrices with dimensions N / (x×R) rows and N / (x×R) columns along the main diagonal of the first matrix, and average the x×R matrices with dimensions N / (x×R) rows and N / (x×R) columns to obtain a second matrix.

[0028] In an alternative approach, the processing unit is further configured to: obtain a third matrix based on the second matrix and the transformation matrix, wherein the transformation matrix is ​​related to the uplink frequency point and the downlink frequency point, and the third matrix is ​​the product of the transformation matrix and the second matrix; obtain weights based on the third matrix, and weight the data based on the weights.

[0029] In one alternative approach, the transformation matrix is ​​obtained based on the actual radiation pattern of the antenna channel and a pre-defined mathematical theorem; the pre-defined mathematical theorem is either the projection theorem or the series theorem.

[0030] In one alternative embodiment, the processing unit is further configured to: perform SVD based on the first matrix to remove eigenvalues ​​smaller than a first preset threshold to obtain a modified first matrix; and obtain a second matrix based on the modified first matrix.

[0031] In one alternative embodiment, the processing unit is further configured to: perform SVD based on the transformation matrix to remove eigenvalues ​​smaller than a second preset threshold to obtain a modified transformation matrix; and obtain a third matrix based on the second matrix and the modified transformation matrix.

[0032] In one alternative approach, when the sub-path power contained in the second matrix is ​​negative, the sub-path power is set to zero.

[0033] In an alternative embodiment, the processing unit is also configured to: obtain the signal increment and interference increment using the third matrix and weights.

[0034] In an alternative embodiment, the processing unit is further configured to: obtain a first matrix based on a reference signal; the reference signal includes one of the following: SRS, CSI-RS, or DMRS.

[0035] Thirdly, this application provides a communication device including a processor coupled to a memory for storing programs or instructions. When the programs or instructions are executed by the processor, the communication device performs the methods described in the first aspect or the embodiments of the first aspect.

[0036] Fourthly, this application provides another communication device, comprising: an interface circuit and a logic circuit; wherein the interface circuit can be understood as an input / output interface, and the logic circuit can be used to run code instructions to perform the methods of the first aspect or the embodiments thereof.

[0037] Fifthly, this application also provides a computer-readable storage medium storing computer-readable instructions that, when executed on a computer, cause the computer to perform a method as described in the first aspect or any possible design in the first aspect.

[0038] In a sixth aspect, this application provides a computer program product containing instructions that, when the computer program or instructions are run on a computer, cause the computer to perform the methods described in the first aspect or the embodiments of the first aspect.

[0039] In a seventh aspect, this application provides a chip system including a processor and potentially a memory, for implementing the methods described in the first aspect or any possible design within the first aspect. The chip system may be composed of chips or may include chips and other discrete devices.

[0040] Eighthly, this application provides a communication system comprising a first communication device and / or a second communication device, the communication system being used to perform the method described in the first aspect or any possible design of the first aspect.

[0041] For the technical effects that can be achieved in aspects two through eight above, please refer to the description of the technical effects that can be achieved by the corresponding possible design schemes in aspect one above. This application will not repeat them here. Attached Figure Description

[0042] Figure 1A A schematic diagram of a communication system provided in an embodiment of this application is shown;

[0043] Figure 1B This illustration shows another schematic diagram of a communication system provided in an embodiment of this application;

[0044] Figure 2 A schematic block diagram of a data processing method provided in an embodiment of this application is shown;

[0045] Figure 3 A schematic flowchart of a data processing method provided in an embodiment of this application is shown;

[0046] Figure 4 A schematic block diagram of a method for obtaining a second matrix provided in an embodiment of this application is shown;

[0047] Figure 5 A schematic block diagram of a communication device provided in an embodiment of this application is shown;

[0048] Figure 6 This illustration shows another schematic block diagram of a communication device provided in an embodiment of the present application. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this application clearer, a further detailed description of this application will be provided below in conjunction with the accompanying drawings. The specific operational methods in the method embodiments can also be applied to the device embodiments or system embodiments. In the description of this application, unless otherwise stated, "multiple" means two or more. Therefore, implementations of the device and method can be referred to mutually, and repeated details will not be repeated.

[0050] This application can be applied to 5G (5th generation mobile networks) New Radio (NR) systems, as well as other communication systems, such as next-generation communication systems. The following describes a communication system applicable to this application. In this system, the first communication device can be a terminal device, and the second communication device can be a network device. In practical applications, this application does not impose specific limitations.

[0051] Figure 1A A communication system 100 applicable to this application is shown. The communication system 100 includes a network device 110, a terminal device 120, and a terminal device 130. The transmission of data from the network device 110 to the terminal device 120 can be understood as downlink data transmission. Specifically, the network device transmits downlink data and a downlink reference signal, and the terminal device receives the downlink data transmitted by the network device and provides feedback to the network device regarding whether the downlink data reception was successful.

[0052] Figure 1B Another communication system 200 applicable to this application is shown. This communication system 200 includes network devices 210, 220, and 230, and a terminal device 240. The terminal device 240 sending data to network device 210 can be understood as uplink data transmission. Specifically, the terminal device uses the downlink reference signal sent by the network devices to perform downlink channel quality measurements and feeds back the relevant measurement information to the network devices. The terminal device sends uplink data and the uplink reference signal to the network devices. The network devices receive the uplink data sent by the terminal devices and provide feedback to the terminal devices regarding whether the uplink data reception was successful. The network devices can use the uplink reference signal sent by the terminal devices to perform channel estimation and channel measurement.

[0053] The data processing method provided in this application is applicable to both... Figure 1A The downlink communication system shown can also be applied to Figure 1B The communication system for uplink communication shown is not specifically limited herein.

[0054] The aforementioned network device is a device deployed in a wireless access network to provide wireless communication functions for terminal devices. The network device includes or may contain a chip with wireless transceiver capabilities. This device includes, but is not limited to: evolved node B (eNB), baseband unit (BBU), access point (AP), wireless relay node, wireless backhaul node, transmission and reception point (TRP or transmission point (TP)) in a wireless fidelity (WIFI) system, and can also be a gNB in ​​a 5G (e.g., NR) system, or a transmission point (TRP or TP), one or a group of antenna panels (including multiple antenna panels) of a base station in a 5G system, or a network node constituting a gNB or transmission point, such as a baseband unit (BBU), distributed unit (DU), satellite, drone, etc.

[0055] In some deployments, a gNB may include a centralized unit (CU) and a distribution unit (DU). A gNB may also include a radio unit (RU). The CU implements some of the gNB's functions, and the DU implements others. For example, the CU implements radio resource control (RRC) and packet data convergence protocol (PDCP) layer functions, while the DU implements radio link control (RLC), media access control (MAC), and physical (PHY) layer functions. For instance, since RRC layer information ultimately becomes PHY layer information (i.e., is transmitted through the PHY layer), or is transformed from PHY layer information, in this architecture, higher-layer signaling, such as RRC layer signaling or PDCP layer signaling, can also be considered as being transmitted by the DU, or by the DU+RU. It is understood that network devices can be CU nodes, DU nodes, or devices including both CU and DU nodes. Furthermore, the CU can be classified as a network device in the access network RAN ​​or as a network device in the core network CN; no restriction is imposed here.

[0056] The terminal device involved in the embodiments of this application, also referred to as a terminal, is a user-side entity used to receive or transmit signals, for sending uplink signals to network devices or receiving downlink signals from network devices. It includes devices that provide voice and / or data connectivity to users, such as handheld devices with wireless connectivity or processing devices connected to a wireless modem. This terminal device can communicate with the core network via a radio access network (RAN) and exchange voice and / or data with the RAN. The terminal equipment may include UE, vehicle-to-x (V2X) terminal equipment, wireless terminal equipment, mobile terminal equipment, device-to-device (D2D) terminal equipment, machine-to-machine / machine-type communications (M2M / MTC) terminal equipment, Internet of Things (IoT) terminal equipment, subscriber unit, subscriber station, mobile station, remote station, access point (AP), remote terminal, access terminal, user terminal, user agent, or user device, wearable device, vehicle-mounted equipment, drone, etc.

[0057] By way of example and not limitation, in this embodiment, the terminal device can also be a wearable device. Wearable devices, also known as wearable smart devices or smart wearable devices, are a general term for devices that utilize wearable technology to intelligently design and develop everyday wearables, such as glasses, gloves, watches, clothing, and shoes. Wearable devices are portable devices that are worn directly on the body or integrated into the user's clothing or accessories. Wearable devices are not merely hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are feature-rich, large in size, and can achieve complete or partial functions without relying on a smartphone, such as smartwatches or smart glasses, as well as those that focus on a specific type of application function and require the use of other devices such as smartphones, such as various smart bracelets, smart helmets, and smart jewelry for vital sign monitoring.

[0058] The various terminal devices described above, if located in a vehicle (e.g., placed inside or installed inside a vehicle), can be considered as vehicle-mounted terminal devices, also known as on-board units (OBUs).

[0059] In frequency division duplex (FDD) systems, the uplink and downlink channels are not reciprocal because they operate on different carrier frequency bands. Network devices require downlink channel information from the terminal for weight calculation. However, the downlink channel information fed back by the terminal contains quantization errors, resulting in significant discrepancies with the actual downlink channel information. This leads to substantial errors in the weight calculation by the network devices, impacting weight performance.

[0060] To address the aforementioned problems, this application provides a data processing method that can improve the accuracy of obtained channel information, enhance weighting performance, and thereby improve data processing efficiency. This method can be applied to network devices, such as TRPs and gNBs, where a network device can be understood as a module (e.g., a chip) within a network device. It can also be applied to terminal devices, such as UEs and vehicle-mounted devices, where a terminal device can also be understood as a module (e.g., a chip) within a terminal device. This application does not impose any specific limitations on this method.

[0061] The data processing method provided in this application is applicable to scenarios with multiple antennas, especially those with eight or more antennas. Multiple antennas can be arranged in a specific order to form an antenna array, which includes horizontal and vertical antenna columns. The channels in the horizontal antenna columns are row channels; the more row channels there are, the greater the beam gain and the better the performance. Furthermore, the antennas can be single-polarized or dual-polarized; this is not specifically limited here. Please refer to [link to relevant documentation]. Figure 2 , Figure 2 A schematic block diagram of a data processing method 200 according to an embodiment of this application is shown. In practical applications, this data processing method 200 can be applied to downlink data processing and uplink data processing. The data processing method 200 includes, but is not limited to, the following steps:

[0062] S201 receives a reference signal from the communication device.

[0063] The aforementioned reference signal can be a channel sounding reference signal (SRS), a demodulation reference signal (DMRS), a channel state information reference signal (CSI-RS), or other reference signals.

[0064] S202, Based on the reference signal, a first matrix is ​​obtained. The first matrix is ​​an autocorrelation matrix with dimensions of N rows and N columns, where N indicates the number of channels of the antenna array.

[0065] Network devices or terminal devices estimate the channel conditions, such as channel coefficients H, by measuring reference signals. Then, they perform autocorrelation operations on the channel coefficients H to obtain a first matrix related to the channel conditions. If this operation is performed by a network device, the first matrix is ​​the uplink channel autocorrelation matrix; if this operation is performed by a terminal device, the first matrix is ​​the downlink channel autocorrelation matrix.

[0066] The dimension of the first matrix can be related to the number of channels in the antenna array. For example, if the antenna array of a network device has 32 channels, with 2 rows and 8 columns, and the antenna array is a dual-polarized antenna array, then the dimension of the antenna array is a 2-row, 8-column dual-polarized antenna array, and the corresponding dimension of the first matrix is ​​32×32, that is, 32 rows and 32 columns.

[0067] S203. Based on the first matrix, the channels between different rows of the antenna array are averaged to obtain the second matrix. The second matrix has N / (x×R) rows and N / (x×R) columns. N, R, and x are all positive integers. R represents the number of channels in the antenna array, and x represents the polarization of the antenna array. When the antenna array is a single-polarized antenna array, x is 1, and when the antenna array is a dual-polarized antenna array, x is 2.

[0068] The first matrix has a dimension of N×N, and the second matrix has a dimension of N / (x×R) rows and N / (x×R) columns. For example, if a network device has 32 antenna channels (2 rows and 8 columns) and is a dual-polarized antenna, then the first matrix has a dimension of 32 rows and 32 columns (32×32), and the second matrix has a dimension of 32 / (2×2) rows and 32 / (2×2) columns (8×8). Since the dimension of the second matrix is ​​lower than that of the first matrix, the amount of data processed is reduced, thus improving data processing efficiency. Furthermore, the second matrix is ​​obtained by averaging the channels across different rows of the antenna array based on the first matrix. This effectively reduces the influence of inter-row correlation between antenna channels and improves the performance of the weights.

[0069] When processing data using the data processing method provided in this application embodiment, the dimension of the second matrix is ​​not only related to the number of channels in the antenna array, but also to the number of channel rows and the polarization of the antenna array. Compared to the first matrix, whose dimension is only related to the number of channels in the antenna array, the second matrix obtained in this application embodiment considers more comprehensive information. Furthermore, by reducing the number of channel rows and the polarization of the antenna array, the dimension of the first matrix is ​​reduced. With a reduced dimension, the computational load during data processing is decreased, and the data processing efficiency is improved. In addition, since the second matrix is ​​obtained by averaging the channels across different rows of the first matrix, the impact of row correlation interference is reduced, effectively improving the performance of the weights.

[0070] Please see Figure 3 , Figure 3 A schematic flowchart of a data processing method 300 according to an embodiment of this application is shown. Figure 3 uses downstream data processing as an example to specifically illustrate a data processing method applicable to this application. This data processing method is implemented through the interaction between a base station and a terminal. The data processing method 300 includes, but is not limited to, the following steps:

[0071] S301, The base station sends configuration information for the reference signal to the terminal.

[0072] The configuration information for the reference signal may include: the reference transmission period, the type of reference signal, etc., which are not specifically limited here.

[0073] S302, the terminal periodically sends reference signals according to the configuration information of the reference signals.

[0074] The reference signal can be SRS, DMRS, or other reference signals, which are not specifically limited here.

[0075] S303, the base station performs channel estimation based on the reference signal and calculates the first uplink matrix and the second uplink matrix.

[0076] The base station performs channel estimation based on the reference signal to obtain an uplink first matrix, which is an autocorrelation matrix with dimensions of N rows and N columns, where N indicates the number of channels in the antenna array. Based on the first matrix, a second matrix is ​​obtained, which is obtained by averaging the channels between different rows of the antenna array based on the first matrix. The second matrix has dimensions of N / (x×R) rows and N / (x×R) columns, where N, R, and x are positive integers, R represents the number of channel rows in the antenna array, and x represents the polarization of the antenna array. When the antenna array is a single-polarized antenna array, x is 1, and when the antenna array is a dual-polarized antenna array, x is 2.

[0077] For example, the base station's antenna array has a total of 32 channels, with 2 channel rows and 8 channel columns. The antenna array is a dual-polarized antenna. The base station performs channel estimation based on the reference signal to obtain channel coefficients H, which are 32×1 column vectors. Autocorrelation is then performed on these channel coefficients H to obtain the first uplink matrix R. u1 The first matrix R u1 The first matrix is ​​an autocorrelation matrix with dimensions of 32×32 (i.e., 32 rows and 32 columns). Based on this first matrix, the base station averages the channels across different rows of the antenna array to obtain a second matrix R. u2 , where the second matrix R u2 The dimensions are 8×8, that is, 8 rows and 8 columns.

[0078] In one alternative approach, the first matrix is ​​subjected to singular value decomposition (SVD), and eigenvalues ​​less than a preset threshold are set to zero to obtain a corrected first matrix. Based on the corrected first matrix, the base station averages the channels between different rows of the antenna array to obtain a second matrix.

[0079] The preset threshold can be defined as a certain proportion where the eigenvalue is less than the sum of all eigenvalues ​​(such as the result of multiplying the sum of all eigenvalues ​​by a coefficient a2). The corresponding proportion can be defined by a preset, preferably 1%.

[0080] For example, the first matrix is ​​decomposed using SVD:

[0081]

[0082] Where, ∑ UL As a diagonal matrix, U UL It is a unitary matrix. For U UL The conjugate transpose of Σ. UL The diagonal elements of the middle are the eigenvalues, and Σ UL Values ​​of the main diagonal elements that are less than a preset threshold are set to zero, and then multiplied by U on the left. UL And right multiplication The corrected first matrix is ​​obtained.

[0083] The above-mentioned SVD of the first matrix can effectively reduce the mutual interference caused by actual sub-path energy leakage, reduce the impact of correction error, and improve the accuracy of the first matrix.

[0084] In one alternative approach, the base station, based on the first matrix, obtains x×R matrices with dimensions N / (x×R) rows and N / (x×R) columns along the main diagonal of the first matrix, and averages these x×R matrices to obtain the second matrix. The first matrix can be either a first matrix corrected by SVD decomposition or a first matrix without SVD decomposition correction.

[0085] For example, such as Figure 4 As shown, Figure 4 A schematic block diagram of a method for obtaining a second matrix according to an embodiment of this application is shown. Wherein, the first matrix R... u1 The dimensions are 32×32, i.e., 32 rows and 32 columns, corresponding to a total of 32 channels in the antenna array. This antenna array includes 2 row channels and 8 column channels. The antenna array is dual-polarized, meaning N is 32, R is 2, and x is 2. The base station follows R... u1 The main diagonal yields four 8×8 (8 rows, 8 columns) matrices, each representing the same row and polarization in the antenna array. Averaging these four matrices yields the second matrix R. u2 The second matrix R u2 The dimensions are 8×8, that is, 8 rows and 8 columns.

[0086] The method for obtaining a second matrix based on a first matrix in this application embodiment has the following advantages: First, the second matrix has a lower matrix dimension than the first matrix, thus reducing data processing complexity and improving data processing efficiency. Second, since the channels between different rows of the antenna array are averaged based on the first matrix, the influence of row-related interference of the antenna array is reduced, thereby improving the accuracy of the second matrix.

[0087] The S304 base station obtains the downlink third matrix based on the uplink second matrix and the transformation matrix.

[0088] For example, the base station multiplies the second matrix and the transformation matrix to obtain the downlink third matrix:

[0089] R d =TR u2 (2)

[0090] Among them, R d Let R be the third matrix in the downward direction, T be the transformation matrix, and R be the third matrix in the downward direction. u2 This is the second matrix in the upper row.

[0091] This transformation matrix is ​​related to both uplink and downlink frequencies and serves as the link between the second uplink matrix and the third downlink matrix. The base station can perform the transformation of the uplink and downlink channel correlation matrices based on the frequency difference between the uplink and downlink frequencies. Furthermore, the transformation matrix can be related to other parameters, such as the antenna array element arrangement, element structure, element spacing, and phase and amplitude information radiated by different elements, etc., which are not specifically limited here. The antenna element arrangement refers to the arrangement of the antenna elements, such as whether it is a linear or area array; the antenna element structure refers to the radiation pattern of the elements; and the element spacing represents the row spacing between antenna elements.

[0092] The transformation matrix can be obtained based on the actual radiation pattern of the antenna channel and a predetermined mathematical theorem, such as the projection theorem or the series theorem. A simple derivation of the transformation matrix is ​​as follows:

[0093] The second matrix in the upper row can be represented as:

[0094] R u2 =∫ρ(θ)a u (θ)a u (θ) H dθ (3)

[0095] Among them, R u2 Let be the second matrix in the upper row, ρ(θ) be the sub-diameter angular power, and a u (θ) is the guide vector of the upward sub-path, and θ is the arrival angle of the sub-path. Formula (3) can also be transformed into the following form:

[0096]

[0097] in, for The element, r u It is R u2 A column vector expanded according to the real and imaginary parts, where m is the index of the relevant element. Similarly, For a u (θ)a u (θ) H The vectorized elements. In practical applications, r u No need for 2N 2 Each element represents because R uIt has structural characteristics, namely, it satisfies Hermitian and Toeplitz characteristics. Hermitian matrices are also called self-conjugate matrices. Every element in the i-th row and j-th column of the matrix is ​​conjugate to the element in the j-th row and i-th column. The elements on the main diagonal of the Toeplitz matrix are equal, and the elements on the lines parallel to the main diagonal are also equal. The elements in the matrix are symmetric about the second diagonal.

[0098] make To define in domain L 2 The Hilbert space of real functions on (-π,π) can be indicated by the following formula:

[0099]

[0100] Where f(θ) and g(θ) are Members.

[0101] Let ρ, for The members are:

[0102]

[0103] The solution to ρ can be expressed as the following problem:

[0104] Search

[0105]

[0106] Take the minimum norm solution of the elements in V, that is

[0107]

[0108] According to vector space theory, space V can be represented as a linear variety as follows:

[0109]

[0110] in, express Zhang Cheng's orthogonal space.

[0111] It is easy to verify using formula (9) that we can let ρ′=ρ * When +g′, where Then we have:

[0112]

[0113] make V = ρ * +W, then make ||ρ* The smallest solution is ρ * In W ⊥ The projection on

[0114] The following proves that ||ρ|| * The smallest solution is ρ * In W ⊥ The projection onto the surface, i.e., if w0 = argmin w∈W ‖ρ * +w‖, then (ρ * +w0)⊥W, where w0 is a set value, which is mainly used to prove the above conclusion.

[0115] set up Right now therefore It can be represented as:

[0116]

[0117] Where, α m For means Projected into space The coefficients of each component.

[0118] According to the projection theorem for vector spaces,

[0119]

[0120] After unfolding, we get:

[0121]

[0122] Written in matrix form:

[0123]

[0124] The corrected third matrix element is:

[0125]

[0126] in, for The element, r d It is R d A column vector is expanded according to the real and imaginary parts, where m is the index of the relevant element, and R is the column vector. d This is the downward autocorrelation matrix. For a d (θ)a d (θ) H Vectorized elements.

[0127] Written in matrix form:

[0128]

[0129] The transformation matrix can be derived from the above derivation as follows:

[0130]

[0131] Because the transformation matrix T includes and In other words, the transformation matrix T is related to the uplink and downlink steering vectors, and the steering vectors are related to the frequency points, so the transformation matrix is ​​related to the uplink and downlink frequencies. Optionally, since the steering vectors can also be related to the antenna element arrangement, antenna element structure, antenna element spacing, and the phase and amplitude information radiated by different elements, the transformation matrix is ​​also related to the above parameters.

[0132] In one alternative approach, the base station performs SVD on the aforementioned transformation matrix T, setting eigenvalues ​​less than a preset threshold to zero to obtain a corrected transformation matrix. This SVD process effectively reduces mutual interference caused by actual sub-path energy leakage, mitigates the impact of correction errors, improves the accuracy of the transformation matrix, and consequently enhances the performance of the weights.

[0133] In one alternative approach, during the process of the base station obtaining the downlink third matrix based on the uplink second matrix and the transformation matrix, when the sub-path power contained in the uplink second matrix is ​​negative, the sub-path power is set to zero. By forcibly adjusting the sub-path angle power to 0, the negative impact of negative power paths can be effectively reduced, the accuracy of the downlink third matrix can be effectively improved, and thus the performance of the weights can be enhanced.

[0134] To reduce the impact of sub-path angle power on the downlink third matrix, the following algorithms 1 and 2 can be used to mitigate the impact.

[0135] Optionally, in Algorithm 1, when obtaining the second matrix in the upper row, if the fitted power obtained when fitting the sub-diameter angle power is less than 0, the sub-diameter angle power is forcibly adjusted to 0.

[0136]

[0137] As shown in formula (18), since This represents the fitted value of the sub-diameter angle power, but during the calculation, there was no effective constraint that the correlation value was zero, which led to the correction error.

[0138] use:

[0139]

[0140] After obtaining α, calculate the different sub-diameters. If the corresponding value is negative, then the corresponding value is set to zero.

[0141]

[0142] By forcibly adjusting the sub-path angle power to 0, the impact of negative power paths on system performance can be effectively reduced, the accuracy of the downlink third matrix can be improved, and the performance of the weights can be enhanced.

[0143] Algorithm 2: Based on the series theorem, the sub-diameter angle power is forcibly adjusted to 0.

[0144] The transformation matrix is ​​calculated using Fourier series.

[0145]

[0146]

[0147] Among them, R u Let R be the uplink autocorrelation matrix. d Let α be the downlink autocorrelation matrix. i f(θ) is the sub-diameter power coefficient. i The antenna pattern at angle θ i Antenna gain, a u (θ i ) is the guide vector of the upward sub-path, a d (θ i ) is the guide vector for the downlink sub-path.

[0148] Assuming that for sub-paths with the same time delay within the angular range Uniformly distributed within, and the number of multipaths p is sufficiently large, θ i Within the scope of delivery Densely distributed within.

[0149] but:

[0150]

[0151]

[0152] Where σ(θ) is the density function of the multipath, and S is a parameter controlling the range of the integral angle. σ(θ) in the domain For a continuous function, satisfying:

[0153]

[0154] Replacing σ(θ) with a periodic function σ1(θ) with a period of 2π / s as the basis function, equations (23) and (24) still hold, and σ1(θ) can be expressed as:

[0155]

[0156]

[0157] Approximating σ1(θ) by order K: Then (23) and (24) can be expressed as:

[0158]

[0159]

[0160] in,

[0161]

[0162]

[0163] Where k = -K, -K+1, ..., K.

[0164] r u =R u (:)r d =R d (:) (32)

[0165]

[0166]

[0167] but:

[0168] Q u c = r u Q d c = r d (35)

[0169] Where c = [c(-K), c(-K+1), ..., c(K)] T The choice of K satisfies Q. u If the column is full, then:

[0170] r d =Ar u (36)

[0171]

[0172] Where A is m 2 ×m 2 A is a matrix. A can be viewed as a simple frequency correction matrix (FC matrix).

[0173]

[0174] Among them, z u =2πf u z / c, due to The imaginary part is the integral of an odd function of θ, so For real values, The same calculation method can be used, z d =2πf d z / c.

[0175] The following characteristics can be derived:

[0176]

[0177] Since c(k), k = -K, -K+1, ..., K, in order to obtain a unique solution to c(k), Q must be... u The column is full rank. Because R... u and R d It satisfies the Hermitian and Toeplitz properties, and therefore can be defined as a (2m-1)-dimensional linear space. Considering Q... u and Q d Due to the symmetry, c(-k) = c * The number of unknowns in (k) for k = 1, 2, ..., K, c should be limited to 2m-1. Therefore, K ≤ m-1 is required. Since σ1(θ) is obtained by truncating the Fourier series, the larger the value of K, the better the approximation performance; therefore, K = m-1.

[0178] The simplified solution is as follows:

[0179]

[0180]

[0181]

[0182]

[0183] but:

[0184] Q u / d,r c r =p u / d,r Q u / d,i c i =p u / d,i (44)

[0185] thereby:

[0186] p d,r =B r p u,r p d,i =B i p u,i (45)

[0187] in,

[0188]

[0189] Among them, c r Used to indicate the real part of a vector, c i The vector used to indicate the imaginary part of a vector, p u,r For

[0190] p refers to the real part vector of the autocorrelation matrix. u,i is the imaginary part vector of the autocorrelation matrix.

[0191]

[0192]

[0193] Hermitian and Toeplitz operations are only for R u Perform the corresponding operation, and R will be restored after the operation. u The dimensions can remain unchanged or be reduced.

[0194] This method can effectively reduce the impact of negative power paths on system performance, improve the accuracy of the downlink third matrix, and thus enhance the performance of the weights.

[0195] S305, the base station calculates the downlink weights based on the downlink third matrix.

[0196] In one alternative approach, the base station can calculate downlink weights based on the downlink third matrix, the precoding matrix indication (PMI), and the channel quality information (CQI).

[0197] In one alternative approach, the base station can obtain corrected weights based on a third matrix; and use the third matrix and the corrected weights to obtain signal increments and interference increments. The performance of the weights is determined by judging whether the signal energy of the target user is greater than a third preset threshold, and whether the interference increment of the target user to other users is greater than a fourth preset threshold.

[0198] This application provides an indicator that can determine whether a significant improvement in weight performance occurs without a significant increase in interference to simultaneously scheduled adjacent users. Generally, the correlation between the weights and the channel autocorrelation matrix is ​​used to characterize the correlation between the weights and the channel: if the weights originate from the signal, a higher correlation is better; if the weights originate from interference, a lower correlation is better.

[0199]

[0200] Among them, W k R represents the weights. iLet be the autocorrelation matrix of the downlink or uplink channel for user i. Here, i can be understood as the index of the user, that is, the index of the terminal or base station, and k can also be understood as the index of the user. User k and user i are not the same user. Coef(i,k) is the index mentioned above, which can determine that CSI correction significantly improves the accuracy of data weights while ensuring that the interference to neighboring MC users scheduled at the same time does not significantly increase.

[0201] S306, the base station weights the downlink data based on the downlink weight to obtain the weighted downlink data.

[0202] S307, the base station sends weighted downlink data to the terminal.

[0203] S308, the downlink data received by the terminal demodulation.

[0204] When processing data using the data processing method provided in this application embodiment, the dimension of the second matrix is ​​related not only to the number of channels in the antenna array, but also to the number of channel rows and the polarization of the antenna array. Compared to the first matrix, whose dimension is only related to the number of channels in the antenna array, the second matrix obtained in this application embodiment considers more comprehensive information. Furthermore, by reducing the number of channel rows and the polarization of the antenna array, the dimension of the first matrix is ​​reduced. With a reduced dimension, the computational load during data processing is decreased, and the data processing efficiency is improved. In addition, since the second matrix is ​​obtained by averaging the channels across different rows of the first matrix, the impact of row correlation interference is reduced, effectively improving the performance of the weights.

[0205] Please see Figure 5 , Figure 5 This is a schematic block diagram of a communication device provided in an embodiment of this application. The communication device can be understood as a network device, such as a TRP or gNB, or as a module (e.g., a chip) within a network device, or as a terminal device, such as a user UE or in-vehicle device, or as a module (e.g., a chip) within a terminal device. This application does not impose specific limitations here. The communication device may include a processing unit 501 and a transceiver unit 502.

[0206] It should be understood that the transceiver unit can be referred to as an input / output unit, communication unit, etc. When the communication device is a terminal device, the input / output unit can be a transceiver; the processing unit can be a processor. When the communication device is a module (e.g., a chip) in a terminal device, the input / output unit can be an input / output interface, input / output circuit, or input / output pin, etc., and can also be referred to as an interface, communication interface, or interface circuit, etc.; the processing unit can be a processor, processing circuit, or logic circuit, etc.

[0207] The transceiver unit 502 is used to receive reference signals.

[0208] Processing unit 501 is used to obtain a first matrix based on a reference signal. The first matrix is ​​an autocorrelation matrix with dimensions of N rows and N columns, where N represents the number of channels in the antenna array. Based on the first matrix, the channels in different rows of the antenna array are averaged to obtain a second matrix with dimensions of N / (x×R) rows and N / (x×R) columns, where N, R, and x are all positive integers. R represents the number of channel rows in the antenna array, and x represents the polarization of the antenna array. When the antenna array is a single-polarized antenna array, x is 1, and when the antenna array is a dual-polarized antenna array, x is 2.

[0209] The aforementioned reference signal may be SRS, CSI-RS, or DMRS, or other reference signals. The network device or terminal device estimates the channel condition by measuring the reference signal and obtains a first matrix related to the channel condition. If the operation is performed by the network device, the first matrix is ​​the uplink channel autocorrelation matrix; if the operation is performed by the terminal device, the first matrix is ​​the downlink channel autocorrelation matrix.

[0210] Typically, the dimension of the first matrix is ​​related to the number of channels in the antenna array. For example, if the antenna array of a network device has 32 channels, then the dimension of the first matrix is ​​32×32 (32 rows and 32 columns). In this method, the dimension of the first matrix is ​​relatively large, resulting in high computational complexity. This application, when obtaining the dimension of the second matrix, considers not only the number of channels in the antenna array but also the number of channel rows and the polarization of the antenna array. For example, if the antenna array of a network device has 32 channels, 2 channel rows, and is a dual-polarized antenna array, then the dimension of the second matrix is ​​32 / (2×2) rows, 32 / (2×2) rows (8 rows and 8 columns). The second matrix obtained in this application has a relatively low dimension, resulting in less data volume and higher data processing efficiency during data calculation. Furthermore, since the channels in different rows of the antenna array are averaged based on the first matrix, the influence of channel row correlation interference is reduced, improving the accuracy of the second matrix and thus improving the performance of the weights.

[0211] In one alternative embodiment, the processing unit 501 is configured to: obtain x×R matrices with dimensions N / (x×R) rows and N / (x×R) columns along the main diagonal of the first matrix, and average the x×R matrices with dimensions N / (x×R) rows and N / (x×R) columns to obtain a second matrix.

[0212] It should be noted that the matrix with x×R dimensions and N / (x×R) rows and N / (x×R) columns obtained by the above method is the same polarization matrix of the same row of the antenna array. The second matrix obtained by averaging the same polarization matrix of the same row reduces the influence of row correlation interference, improves the accuracy of the second matrix, and thus improves the performance of the weights.

[0213] In one alternative embodiment, the processing unit 501 is further configured to obtain a third matrix based on the second matrix and the transformation matrix, the transformation matrix being related to the uplink frequency point and the downlink frequency point, the third matrix being the product of the transformation matrix and the second matrix; obtain weights based on the third matrix, and weight the data based on the weights.

[0214] In one alternative approach, the transformation matrix is ​​obtained based on the actual radiation pattern of the antenna array's channels and a pre-defined mathematical theorem; the pre-defined mathematical theorem can be a projection theorem or a series theorem.

[0215] The transformation matrix is ​​obtained based on the actual radiation pattern of the antenna channel and a pre-defined mathematical theorem, which can guarantee the accuracy of the transformation matrix. When the accuracy of the transformation matrix is ​​high, the third matrix obtained based on the transformation matrix is ​​even more accurate. Calculation based on the accurate third matrix can improve the performance of the weights.

[0216] In one optional embodiment, the processing unit 501 is further configured to: perform SVD based on the first matrix, set eigenvalues ​​smaller than a first preset threshold to zero, and obtain a corrected first matrix; and obtain a second matrix based on the corrected first matrix. By performing SVD and setting small eigenvalues ​​to zero (i.e., removing eigenvalues ​​smaller than the first preset threshold), the error caused by insufficient statistics in the first matrix can be reduced, thereby improving the accuracy of the first matrix and thus improving the performance of the weights.

[0217] In an optional embodiment, the processing unit 501 is further configured to: perform SVD based on the transformation matrix, set eigenvalues ​​smaller than a second preset threshold to zero, and obtain a modified transformation matrix; and obtain a third matrix based on the second matrix and the modified transformation matrix. By performing SVD and setting small eigenvalues ​​to zero on the transformation matrix (i.e., removing eigenvalues ​​smaller than the second preset threshold), the cross-correlation effect between sub-paths in the transformation matrix can be reduced, thereby improving the accuracy of the transformation matrix and thus improving the performance of the weights.

[0218] In an optional embodiment, processing unit 501 is further configured to: set the sub-path power to zero when the sub-path power contained in the second matrix is ​​negative. By setting the negative sub-path power to zero, the performance impact of negative power paths can be effectively reduced, the accuracy of the third matrix can be improved, and thus the performance of the weights can be enhanced.

[0219] In an alternative embodiment, processing unit 501 is further configured to: obtain the corrected weights to acquire the signal increment and interference increment based on the third matrix. Based on the acquired channel increment and interference increment, it can adaptively select whether to perform a correction operation to obtain a better weight.

[0220] In an alternative embodiment, the processing unit 501 is further configured to: obtain a first matrix based on a reference signal, wherein the reference signal includes one of the following: SRS, CSI-RS, or DMRS.

[0221] Please see Figure 6 , Figure 6 This is a schematic block diagram of a communication device provided in an embodiment of this application. Exemplarily, the communication device 600 may be a chip or a chip system. Optionally, in this embodiment, the chip system may be composed of chips, or may include chips and other discrete components.

[0222] The communication device 600 may include at least one processor 610, and may also include at least one memory 620 for storing computer programs, program instructions, and / or data. The memory 620 and the processor 610 are coupled. The coupling in this embodiment is an indirect coupling or communication connection between devices, units, or modules, and may be electrical, mechanical, or other forms, used for information exchange between devices, units, or modules. The processor 610 may operate in conjunction with the memory 620. The processor 610 may execute the computer program stored in the memory 620. Optionally, the at least one memory 620 may also be integrated with the processor 610.

[0223] Optionally, in practical applications, the communication device 600 may or may not include a transceiver 630, as illustrated by the dashed box in the figure. The communication device 600 can exchange information with other devices through the transceiver 630. The transceiver 630 can be a circuit, a bus, a transceiver, or any other device that can be used for information exchange.

[0224] In one possible implementation, the communication device 600 can be applied to the aforementioned terminal device, the aforementioned first communication device, or the aforementioned second communication device. The memory 620 stores the necessary computer programs, program instructions, and / or data for implementing the functions of the first or second communication device in any of the above embodiments. The processor 610 can execute the computer program stored in the memory 620 to complete the methods in any of the above embodiments.

[0225] This application embodiment does not limit the specific connection medium between the transceiver 630, processor 610, and memory 620. This application embodiment... Figure 6The memory 620, processor 610, and transceiver 630 are connected via a bus, and the bus is in... Figure 6 The connections between other components are shown in bold and are for illustrative purposes only, not as limiting information. The bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, Figure 6 The text uses only a single thick line to represent a bus, but this does not imply that there is only one bus or one type of bus. In the embodiments of this application, the processor can be a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the methods disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0226] In the embodiments of this application, the memory can be non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), or it can be volatile memory, such as random-access memory (RAM). The memory can also be any other medium capable of carrying or storing desired program code in the form of instructions or data structures, and accessible by a computer, but is not limited thereto. The memory in the embodiments of this application can also be a circuit or any other device capable of implementing storage functions, used to store computer programs, program instructions, and / or data.

[0227] Based on the above embodiments, this application also provides a readable storage medium storing instructions that, when executed, cause the security detection method in any of the above embodiments to be implemented. The readable storage medium may include various media capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory, random access memory, magnetic disk, or optical disk.

[0228] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0229] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more flowchart illustrations and / or one or more block diagrams.

[0230] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0231] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

Claims

1. A data processing method, characterized in that, include: Obtain the reference signal fed back by the communication device; A first matrix is ​​obtained based on the reference signal, wherein the first matrix has a dimension of N OK N The autocorrelation matrix of the column, the N Indicates the number of channels in the antenna array; Based on the first matrix, the channels between different rows of the antenna array are averaged to obtain a second matrix, the second matrix having dimension [missing information]. N / ( x × R )OK, N / ( x × R ) column, wherein, the N , R , x All are positive integers, the R This indicates the number of channel rows in the antenna array. x This indicates the polarization of the antenna array. When the antenna array is a single-polarization antenna array, the... x When the value is 1, and the antenna array is a dual-polarized antenna array, the x The value is 2.

2. The method according to claim 1, characterized in that, The step of averaging the channels between different rows of the antenna array based on the first matrix to obtain the second matrix includes: Obtain along the main diagonal of the first matrix x × R Each dimension is N / ( x × R )OK, N / ( x × R A matrix of columns, containing the... x × R Each dimension is N / ( x × R )OK, N / ( x × R The second matrix is ​​obtained by averaging the matrices of the columns.

3. The method according to claim 1 or 2, characterized in that, The method further includes: A third matrix is ​​obtained based on the second matrix and the transformation matrix. The transformation matrix is ​​related to the uplink frequency point and the downlink frequency point. The third matrix is ​​the product of the transformation matrix and the second matrix. Based on the third matrix, weights are obtained, and the data is weighted based on the weights.

4. The method according to claim 3, characterized in that, The transformation matrix is ​​obtained based on the actual radiation pattern of the channels of the antenna array.

5. The method according to claim 1, 2, or 4, characterized in that, Before averaging the channels between different rows of the antenna array based on the first matrix to obtain the second matrix, the method further includes: The first matrix is ​​subjected to singular value decomposition (SVD), and the eigenvalues ​​smaller than a first preset threshold are set to zero to obtain the corrected first matrix.

6. The method according to claim 3, characterized in that, Before obtaining the third matrix based on the second matrix and the transformation matrix, the method further includes: Singular Value Decomposition (SVD) is performed based on the transformation matrix, and eigenvalues ​​smaller than a second preset threshold are set to zero to obtain a corrected transformation matrix.

7. The method according to claim 3, characterized in that, The process of obtaining the third matrix based on the second matrix and the transformation matrix includes: When the sub-path power contained in the second matrix is ​​negative, the sub-path power is set to zero.

8. The method according to any one of claims 1-2, 4, and 6-7, characterized in that, The reference signal includes one of the following: channel sounding reference signal (SRS), channel state information reference signal (CSI-RS), or demodulation reference signal (DMRS).

9. A communication device, characterized in that, include: The transceiver unit is used to receive feedback reference signals; Processing unit, configured to obtain a first matrix based on the reference signal, wherein the first matrix has dimension 1. N OK N The autocorrelation matrix of the column, the N This represents the number of channels in the antenna array; based on the first matrix, the channels in different rows of the antenna array are averaged to obtain a second matrix, the second matrix having dimension [missing information]. N / ( x × R )OK, N / ( x × R ) column, the N , R , x All are positive integers, the R This indicates the number of channel rows in the antenna array. x This indicates the polarization of the antenna array. When the antenna array is a single-polarization antenna array, the... x When the value is 1, and the antenna array is a dual-polarized antenna array, the x The value is 2.

10. The apparatus according to claim 9, characterized in that, The processing unit is used to average the channels between different rows of the antenna array based on the first matrix to obtain a second matrix, including: Obtain along the main diagonal of the first matrix x × R Each dimension is N / ( x × R )OK, N / ( x × R The matrix of ) will contain the x × R Each dimension is N / ( x × R )OK, N / ( x × R The second matrix is ​​obtained by averaging the matrices of the columns.

11. The apparatus according to claim 9 or 10, characterized in that, The processing unit is also used for: A third matrix is ​​obtained based on the second matrix and the transformation matrix. The transformation matrix is ​​related to the uplink frequency point and the downlink frequency point. The third matrix is ​​the product of the transformation matrix and the second matrix. Based on the third matrix, weights are obtained, and the data is weighted based on the weights.

12. The apparatus according to claim 11, characterized in that, The transformation matrix is ​​obtained based on the actual radiation pattern of the antenna channel and a preset mathematical theorem; the preset mathematical theorem is either the projection theorem or the series theorem.

13. The apparatus according to claim 9, 10, or 12, characterized in that, The processing unit is further configured to: Before averaging the channels between different rows of the antenna array based on the first matrix to obtain the second matrix, singular value decomposition (SVD) is performed on the first matrix to set eigenvalues ​​less than a first preset threshold to zero, thereby obtaining the corrected first matrix.

14. The apparatus according to claim 11, characterized in that, The processing unit is further configured to: Before obtaining the third matrix based on the second matrix and the transformation matrix, singular value decomposition (SVD) is performed based on the transformation matrix to set eigenvalues ​​less than a second preset threshold to zero, thereby obtaining a corrected transformation matrix.

15. The apparatus according to claim 11, characterized in that, The processing unit obtains a third matrix based on the second matrix and the transformation matrix, including: When the sub-path power contained in the second matrix is ​​negative, the processing unit sets the sub-path power to zero.

16. The apparatus according to any one of claims 9-10, 12, and 14-15, characterized in that, The reference signal includes one of the following: channel sounding reference signal (SRS), channel state information reference signal (CSI-RS), or demodulation reference signal (DMRS).

17. A communication device, characterized in that, include: A processor coupled to a memory for storing programs or instructions that, when executed by the processor, cause the apparatus to perform the method as described in any one of claims 1-8.

18. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed by a computer, cause the method as described in any one of claims 1-8 to be performed.

19. A computer program product comprising a computer program or instructions, characterized in that, When the computer program or instructions are run on a computer, the method as described in any one of claims 1-8 is performed.

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