WO2024051452A1 - 数据处理的方法、终端及可读存储介质 - Google Patents

数据处理的方法、终端及可读存储介质 Download PDF

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Publication number
WO2024051452A1
WO2024051452A1 PCT/CN2023/113108 CN2023113108W WO2024051452A1 WO 2024051452 A1 WO2024051452 A1 WO 2024051452A1 CN 2023113108 W CN2023113108 W CN 2023113108W WO 2024051452 A1 WO2024051452 A1 WO 2024051452A1
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Prior art keywords
subspace
target
value
receiving antennas
subspaces
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PCT/CN2023/113108
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English (en)
French (fr)
Inventor
李文斌
董展谊
林伟
芮华
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中兴通讯股份有限公司
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Publication of WO2024051452A1 publication Critical patent/WO2024051452A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0697—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using spatial multiplexing

Definitions

  • Embodiments of the present application relate to the field of communications, and in particular, to a data processing method, terminal, and readable storage medium.
  • the fifth-generation mobile communication technology (5th-Generation Mobile Communication Technology, 5G) is a broadband mobile communication technology characterized by high speed, low latency and large connections. , providing a more extreme experience for mobile Internet users.
  • Massive Multiple-Input Multiple-Output (Massive MIMO) technology configures a large number of antennas on the base station side, which can support the access of a large number of wireless network devices at the same time.
  • the method adopted is to process all data from all receiving antennas separately, such as front-end processing, channel estimation, equalization, demodulation, and bit-level processing. In this way, the energy consumption of the receiver and the complexity of data processing are relatively large.
  • Embodiments of the present application provide a data processing method terminal and a readable storage medium, which can solve the problems of energy consumption of the receiver and complexity of data processing.
  • a data processing method which includes: performing spatial decomposition on received data of multiple receiving antennas to obtain multiple subspaces; measuring the signal quality of at least one of the subspaces; according to the signal of the subspace Quality: select a target subspace from multiple subspaces; process the received data through the target subspace.
  • a data processing device including: a decomposition module for spatially decomposing the received data of multiple receiving antennas to obtain multiple subspaces; and a measurement module for measuring at least one of the subspaces. signal quality; a selection module for selecting a target subspace from multiple subspaces according to the signal quality of the subspace; a processing module for processing the received data through the target subspace.
  • a terminal in a third aspect, includes a processor and a memory.
  • the memory stores programs or instructions that can be run on the processor.
  • the program or instructions are executed by the processor, the following implementations are implemented: The steps of the method described in one aspect.
  • a readable storage medium is provided. Programs or instructions are stored on the readable storage medium. When the programs or instructions are executed by a processor, the steps of the method described in the first aspect are implemented.
  • a chip in a fifth aspect, includes a processor and a communication interface.
  • the communication interface is coupled to the processor.
  • the processor is used to run programs or instructions to implement the method described in the first aspect. .
  • a computer program/program product is provided, the computer program/program product is stored in a storage medium, and the computer program/program product is executed by at least one processor to implement the method described in the first aspect. Data processing method steps.
  • the received data of multiple receiving antennas are spatially decomposed to obtain multiple subspaces, the signal quality of at least one subspace is measured, and based on the signal quality of the subspace, multiple subspaces are obtained. Select the target subspace in the subspace, and process the received data through the target subspace.
  • Figure 1 shows a schematic diagram of a wireless communication system to which embodiments of the present application are applicable.
  • Figures 2 to 5 show a schematic flow chart of the data processing method provided by the embodiment of the present application
  • Figure 6 shows a schematic structural diagram of a data processing device provided by an embodiment of the present application.
  • Figure 7 shows a schematic structural diagram of a terminal provided by an embodiment of the present application.
  • first, second, etc. in the description and claims of this application are used to distinguish similar objects and are not used to describe a specific order or sequence. It is to be understood that the terms so used are interchangeable under appropriate circumstances so that the embodiments of the present application can be practiced in sequences other than those illustrated or described herein, and that "first" and “second” are distinguished objects It is usually one type, and the number of objects is not limited.
  • the first object can be one or multiple.
  • “and/or” in the description and claims indicates at least one of the connected objects, and the character “/" generally indicates that the related objects are in an "or” relationship.
  • LTE Long Term Evolution
  • LTE-Advanced, LTE-A Long Term Evolution
  • LTE-A Long Term Evolution
  • CDMA Code Division Multiple Access
  • TDMA Time Division Multiple Access
  • FDMA Frequency Division Multiple Access
  • OFDMA Orthogonal Frequency Division Multiple Access
  • SC-FDMA Single-carrier Frequency-Division Multiple Access
  • NR New Radio
  • FIG. 1 shows a schematic diagram of a wireless communication system to which embodiments of the present application can be applied.
  • the wireless communication system includes a terminal 11 and a network side device 12.
  • the terminal 11 may be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer), or a notebook computer, a personal digital assistant (Personal Digital Assistant, PDA), a palmtop computer, a netbook, or a super mobile personal computer.
  • Tablet Personal Computer Tablet Personal Computer
  • laptop computer laptop computer
  • PDA Personal Digital Assistant
  • PDA Personal Digital Assistant
  • UMPC ultra-mobile personal computer
  • UMPC mobile Internet device
  • MID mobile Internet Device
  • AR augmented reality
  • VR virtual reality
  • robots wearable devices
  • WUE Vehicle User Equipment
  • PUE Pedestrian User Equipment
  • smart home home equipment with wireless communication functions, such as refrigerators, TVs, washing machines or furniture, etc.
  • game consoles personal computers (personal computer, PC), teller machine or self-service machine and other terminal-side devices.
  • Wearable devices include: smart watches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets) bracelets, smart anklets, etc.), smart wristbands, smart clothing, etc.
  • the network side device 12 may include an access network device or a core network device, where the access network device 12 may also be called a radio access network device, a radio access network (Radio Access Network, RAN), a radio access network function or Wireless access network unit.
  • the access network device 12 may include a base station, a Wireless Local Area Network (WLAN) access point or a WiFi node, etc.
  • WLAN Wireless Local Area Network
  • the base station may be called a Node B, an evolved Node B (eNB), an access point, Base Transceiver Station (BTS), radio base station, radio transceiver, Basic Service Set (BSS), Extended Service Set (ESS), home B-node, home evolved B-node, transmitting receiving point (Transmitting Receiving Point, TRP) or some other suitable one in the field Terminology, as long as the same technical effect is achieved, the base station is not limited to specific technical terms. It should be noted that in the embodiment of this application, only the base station in the NR system is used as an example for introduction, and the specific type of the base station is not limited.
  • the frequency band (Frequency band) is 2.6G, the bandwidth is 100M, the system mode is Time Division Duplexing (TDD), the subcarrier spacing is configured as 30kHz, and the user equipment (User Equipment, UE) is 1, the number of receiving antennas is 32, the number of transmitting antennas is 1, the number of resource blocks (Resource Block, RB) allocated to the UE is 273, and the number of symbols allocated to the UE is 12 , the resource element (Resource Element, RE) data on each RB is 12, and the type of demodulation reference signal (Demodulation Reference Signal, DMRS) is type A.
  • the frequency band Frequency band
  • the bandwidth is 100M
  • the system mode is Time Division Duplexing (TDD)
  • TDD Time Division Duplexing
  • the subcarrier spacing is configured as 30kHz
  • the user equipment User Equipment, UE
  • the number of receiving antennas is 32
  • the number of transmitting antennas is 1
  • one embodiment of the present application provides a data processing method 200.
  • the method can be executed by a terminal device.
  • the method can be executed by software or hardware installed on the terminal device.
  • the method includes the following step:
  • Step S201 Perform spatial decomposition on the received data of multiple receiving antennas to obtain multiple subspaces.
  • the received data received by multiple receiving antennas are spatially decomposed and reconstructed.
  • the received data received by multiple receiving antennas are spatially decomposed, for example, by using Eigen decomposition (EVD) or singular value decomposition (EVD).
  • ELD Eigen decomposition
  • EVD singular value decomposition
  • Singular Value Decomposition (SVD) Quadrature Rectangle (QR) decomposition
  • Schmidt Orthogonalization Schot Orthogonalization
  • spatial decomposition of received data from multiple receiving antennas includes:
  • the element Convert the received data to the frequency domain to obtain the frequency domain received data, based on the resources of multiple receiving antennas
  • the element calculates the covariance matrix of the received data in the frequency domain, and performs the first decomposition process on the covariance matrix to obtain multiple subspaces.
  • the received data can be subjected to related processing such as Fourier transform and cyclic prefix removal (CP removal) to obtain the frequency domain received data.
  • CP removal cyclic prefix removal
  • its dimensions can be the number of RBs allocated by the UE, the UE's The product of the number of allocated symbols, the number of REs on each RB, and the number of receiving antennas.
  • R is the covariance matrix
  • N re is the number of resource elements of multiple receiving antennas
  • Y i is the vector composed of resource elements on multiple receiving antennas
  • Y is the received data of multiple receiving antennas.
  • the dimension of each Y i is 1 ⁇ 32, and the final covariance matrix R is a 32 ⁇ 32 symmetric matrix.
  • the first decomposition process includes one of the following:
  • R is the covariance matrix
  • U is the left singular matrix
  • V is the right singular matrix
  • each column of V is a subspace
  • each column is the weighting coefficient of the received data of multiple receiving antennas.
  • R is the covariance matrix
  • Q is the orthogonal matrix
  • T is the upper triangular matrix
  • Q represents the decomposed subspace
  • each column of Q can be used as a subspace.
  • Step S203 Measure the signal quality of at least one subspace.
  • each column in the above-mentioned V matrix can be used as a subspace.
  • the signal quality of at least one subspace it can be measured by the signal-to-noise ratio of the channel estimate of each subspace or the maximum power of each subspace. Measure the signal quality of at least one subspace.
  • the signal-to-noise ratio or maximum power of the channel estimate can be used to characterize the signal quality of the subspace. The greater the signal-to-noise ratio and maximum power of the channel estimate, the better the signal quality of the subspace. The better.
  • Step S205 Select a target subspace from multiple subspaces according to the signal quality of the subspace.
  • a target subspace with a better signal is found from multiple subspaces.
  • Step S207 Process the received data through the target subspace.
  • spatial filtering is performed through the target subspace to obtain the received data in the target subspace, thereby filtering the received data to achieve the purpose of dimensionality reduction of the received data.
  • the After receiving the data in the target subspace perform subsequent processing on the received data in the target subspace, such as channel estimation processing, equalization processing, demodulation processing, bit-level descrambling processing, rate decoding processing, decoding processing and CRC decoding. Processing etc.
  • the data processing method provided by the embodiment of the present application spatially decomposes the received data of multiple receiving antennas to obtain multiple subspaces, measures the signal quality of at least one subspace, and based on the signal quality of the subspace, obtains multiple subspaces from the signal quality of the subspace. Select the target subspace, process the received data through the target subspace, and use the selected target subspace to process the received data, thereby filtering the received data, discarding useless data, reducing the dimension of the received data, and in subsequent processing In the process, the complexity and energy consumption of the receiver in processing the received data are reduced.
  • selecting a target subspace from multiple subspaces according to the signal quality of the subspace includes the following steps:
  • Step S301 Select a target subspace from multiple subspaces based on the signal-to-noise ratio of the channel estimate of each subspace.
  • the signal-to-noise ratio of the channel estimate of each subspace is used as a measurement quantity to measure the signal quality of each subspace, thereby selecting a target subspace with a better signal.
  • selecting the target subspace from multiple subspaces according to the signal-to-noise ratio of the channel estimate value of each subspace includes: selecting a subspace whose signal-to-noise ratio of the channel estimate value is greater than a threshold value as the target subspace.
  • the threshold value may be determined based on the average of the signal-to-noise ratios of the channel estimates of multiple subspaces.
  • the signal-to-noise ratio can be used
  • An integer multiple of the average ANR mean is used as the threshold value.
  • N can take the value 2
  • the signal-to-noise ratio of the channel estimate is calculated by the following formula:
  • ANR h,j is the signal-to-noise ratio of the channel estimate of the j-th subspace, is the time domain channel estimate value after noise reduction in the jth subspace, for The maximum value of the module, for average of.
  • time domain channel estimate value Calculated by the following formula:
  • hf time is the time domain channel estimate value before noise reduction, where hf time is obtained by converting the frequency domain channel estimate value to the time domain.
  • the frequency domain channel estimate value is calculated in the following way:
  • data dmrs is the demodulation reference signal
  • G is the weighting coefficient in the subspace
  • data' dmrs is the target demodulation reference signal.
  • the weighting coefficient of the current subspace in the V matrix is obtained.
  • the number of receiving antennas is 32, then the G of the current subspace taken out is a 32 ⁇ 1 vector.
  • the obtained target demodulation reference signal is a 1638 ⁇ 1 vector.
  • the frequency domain channel estimate is calculated by the following formula:
  • pilot is the pilot sequence
  • conj(pilot) is the value of the pilot sequence. conjugate.
  • pilot is a pilot sequence generated according to the system parameters of the NR system.
  • the dimension of the pilot sequence generated according to the above parameters of the NR system is 1638 ⁇ 1.
  • selecting a target subspace from multiple subspaces according to the signal quality of the subspace includes the following steps:
  • Step S401 Select a target subspace from multiple subspaces based on the maximum power of each subspace.
  • the maximum power of each subspace is used as a measurement quantity to measure the signal quality of each subspace, thereby selecting a target subspace with a better signal.
  • selecting a target subspace from multiple subspaces according to the maximum power of each subspace includes the following steps:
  • Step S501 Calculate the maximum power value of each subspace based on the frequency domain channel estimate value of each subspace.
  • the maximum power value is calculated by:
  • Step S503 Sort the power maximum values in descending order, and select the subspaces corresponding to the top M power maximum values as the target subspace.
  • M is determined based on the number of transmission blocks for each transmission, or based on the number of receiving antennas and each time The number of transmitted transport blocks is determined.
  • a more specific value range of M can be the number of streams ⁇ min (the number of receiving antennas, the number of streams + 2), where the number of streams is the Transport Block for each transmission , TB) number, min (number of receiving antennas, number of streams + 2) represents the minimum value of "number of receiving antennas" and "number of streams + 2", and the value of M is the number of TB per transmission
  • the execution subject of the data processing method provided by the embodiments of the present application can be a data processing device.
  • the method of loading data processing performed by a data processing device is used as an example to illustrate the method provided by the embodiments of the present application. data processing device.
  • Figure 6 is a schematic structural diagram of a data processing device according to an embodiment of the present application.
  • the data processing device 600 includes: a decomposition module 601, which is used to spatially decompose the received data of multiple receiving antennas to obtain multiple subspaces; a measurement module 602, which is used to measure the signal quality of at least one subspace. ;
  • the selection module 603 is used to select a target subspace from multiple subspaces according to the signal quality of the subspace; the processing module 604 is used to process the received data through the target subspace.
  • multiple subspaces are obtained by spatially decomposing the received data of multiple receiving antennas, measuring the signal quality of at least one subspace, and selecting a target subspace from the multiple subspaces based on the signal quality of the subspace.
  • the received data is processed through the target subspace, and the selected target subspace can be used to process the received data, thereby filtering the received data, discarding useless data, reducing the dimension of the received data, and in the subsequent processing process, reducing The complexity of processing the received data by the receiver
  • the decomposition module 601 is also used to convert the received data into the frequency domain to obtain the frequency domain received data; calculate the covariance matrix of the frequency domain received data according to the resource elements of multiple receiving antennas, and calculate The covariance matrix is subjected to the first decomposition process to obtain multiple subspaces.
  • the first decomposition process includes singular value decomposition process
  • R is the covariance matrix
  • U is the left singular matrix
  • each column of V is a subspace
  • each column is the weighting coefficient of the received data of multiple receiving antennas.
  • the first decomposition process includes regular triangle decomposition process
  • R is the covariance matrix
  • Q is the orthogonal matrix
  • T is the upper triangular matrix
  • the covariance matrix is calculated by:
  • N re is the number of resource elements of multiple receiving antennas
  • Y i is the number of multiple receiving antennas
  • a vector composed of resource elements above, Y is the received data of multiple receiving antennas.
  • the selection module 603 is also used to select a target subspace from multiple subspaces based on the signal-to-noise ratio of the channel estimate of each subspace.
  • the selection module 603 is also used to select a target subspace from multiple subspaces based on the maximum power of each subspace.
  • the selection module 603 is also used to select a subspace in which the signal-to-noise ratio of the channel estimate is greater than the threshold value as the target subspace;
  • the signal-to-noise ratio of the channel estimate is calculated by:
  • ANR h,j is the signal-to-noise ratio of the channel estimate of the j-th subspace, is the time domain channel estimate value after noise reduction in the jth subspace, for The maximum value of the module, for average of.
  • time domain channel estimate value Calculated by the following formula:
  • hf time is the time domain channel estimate value before noise reduction, where hf time is obtained by converting the frequency domain channel estimate value to the time domain.
  • the frequency domain channel estimate value is calculated in the following way:
  • data dmrs is the demodulation reference signal
  • G is the weighting coefficient in the subspace
  • data' dmrs is the target demodulation reference signal
  • the frequency domain channel estimate is calculated by the following formula:
  • pilot is the pilot sequence
  • conj(pilot) is the conjugate of the pilot sequence
  • the threshold value is determined based on the average of the signal-to-noise ratios of channel estimates of multiple subspaces.
  • the selection module 603 is also used to calculate the maximum power value of each subspace based on the frequency domain channel estimation value of each subspace; arrange the maximum power values in descending order, and select the top M powers The subspace corresponding to the maximum value is the target subspace.
  • the value of M is determined based on the number of transmission blocks for each transmission, or based on the number of receiving antennas and the number of transmission blocks for each transmission.
  • the maximum power value is calculated by:
  • the data processing device in the embodiment of the present application may be an electronic device, such as an electronic device with an operating system, or may be a component in the electronic device, such as an integrated circuit or chip.
  • the electronic device may be a terminal or other devices other than the terminal.
  • terminals may include but are not limited to the types of terminals 11 listed above, and other devices may be servers, network attached storage (Network Attached Storage, NAS), etc., which are not specifically limited in the embodiment of this application.
  • NAS Network Attached Storage
  • the data processing device provided by the embodiments of the present application can implement each process implemented by the method embodiments in Figures 2 to 5 and achieve the same technical effect. To avoid duplication, the details will not be described here.
  • this embodiment of the present application also provides a terminal 700, including a processor 701 and memory 702.
  • the memory 702 stores programs or instructions that can be run on the processor 701.
  • each step of the above-mentioned data processing method embodiment is implemented, and the same technology can be achieved. Effect.
  • Embodiments of the present application also provide a readable storage medium.
  • Programs or instructions are stored on the readable storage medium.
  • the program or instructions are executed by a processor, each process of the above-mentioned data processing method embodiment is implemented, and can achieve The same technical effects are not repeated here to avoid repetition.
  • the processor is the processor in the terminal described in the above embodiment.
  • the readable storage media includes computer-readable storage media, such as computer read-only memory (Read Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disks or optical disks.
  • An embodiment of the present application further provides a chip.
  • the chip includes a processor and a communication interface.
  • the communication interface is coupled to the processor.
  • the processor is used to run programs or instructions to implement the above data processing method embodiments. Each process can achieve the same technical effect. To avoid repetition, we will not go into details here.
  • chips mentioned in the embodiments of this application may also be called system-on-chip, system-on-a-chip, system-on-chip or system-on-chip, etc.
  • Embodiments of the present application further provide a computer program/program product.
  • the computer program/program product is stored in a storage medium.
  • the computer program/program product is executed by at least one processor to implement the above data processing method.
  • Each process in the example can achieve the same technical effect. To avoid repetition, we will not repeat it here.
  • the methods of the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is better. implementation.
  • the technical solution of the present application can be embodied in the form of a computer software product that is essentially or contributes to the existing technology.
  • the computer software product is stored in a storage medium (such as ROM/RAM, disk , CD), including several instructions to cause a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of this application.

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Abstract

本申请公开了一种数据处理的方法、装置及终端，属于通信领域，本申请实施例的数据处理的方法，包括：对多个接收天线的接收数据进行空间分解，得到多个子空间；测量至少一个所述子空间的信号质量；根据所述子空间的信号质量，从多个所述子空间中选取目标子空间；通过所述目标子空间对所述接收数据进行处理。

Description

数据处理的方法、终端及可读存储介质
交叉引用
本申请要求在2022年09月06日提交中国专利局、申请号为202211084695.7、名称为“数据处理的方法、终端及可读存储介质”的中国专利申请的优先权，该申请的全部内容通过引用结合在本申请中。
技术领域
本申请实施例涉及通信领域，尤其涉及一种数据处理的方法、终端及可读存储介质。
背景技术
随着无线网络接入设备数量的激增，各种无线服务蓬勃发展，第五代移动通信技术(5th-Generation Mobile Communication Technology，5G)作为高速率、低时延和大连接特点的宽带移动通信技术，为移动互联网用户提供了更加极致的体验。
作为5G技术的关键技术之一，大规模多输入多输出(Massive Multiple-Input Multiple-Output，Massive MIMO)技术在基站侧配置很多数量的天线，能够同时支持大量无线网络设备的接入。在接收机对大规模的天线所接收到的数据进行处理时，采用的方式是对所有接收天线的所有数据分别进行处理，如前端处理、信道估计、均衡、解调以及比特级处理等。如此，接收机的能耗和数据处理的复杂度较大。
发明内容
本申请实施例提供一种数据处理的方法终端及可读存储介质，能够解决接收机的能耗和数据处理的复杂度较大的问题。
第一方面，提供了一种数据处理的方法，包括：对多个接收天线的接收数据进行空间分解，得到多个子空间；测量至少一个所述子空间的信号质量；根据所述子空间的信号质量，从多个所述子空间中选取目标子空间；通过所述目标子空间对所述接收数据进行处理。
第二方面，提供了一种数据处理的装置，包括：分解模块，用于对多个接收天线的接收数据进行空间分解，得到多个子空间；测量模块，用于测量至少一个所述子空间的信号质量；选取模块，用于根据所述子空间的信号质量，从多个所述子空间中选取目标子空间；处理模块，用于通过所述目标子空间对所述接收数据进行处理。
第三方面，提供了一种终端，该终端包括处理器和存储器，所述存储器存储可在所述处理器上运行的程序或指令，所述程序或指令被所述处理器执行时实现如第一方面所述的方法的步骤。
第四方面，提供了一种可读存储介质，所述可读存储介质上存储程序或指令，所述程序或指令被处理器执行时实现如第一方面所述的方法的步骤。
第五方面，提供了一种芯片，所述芯片包括处理器和通信接口，所述通信接口和所述处理器耦合，所述处理器用于运行程序或指令，实现如第一方面所述的方法。
第六方面，提供了一种计算机程序/程序产品，所述计算机程序/程序产品被存储在存储介质中，所述计算机程序/程序产品被至少一个处理器执行以实现如第一方面所述的数据处理的方法的步骤。
在本申请实施例中，对多个接收天线的接收数据进行空间分解，得到多个子空间，测量至少一个子空间的信号质量，根据子空间的信号质量，从多 个子空间中选取目标子空间，通过目标子空间对接收数据进行处理。
附图说明
图1示出本申请实施例可应用的一种无线通信系统的示意图。
图2-图5示出本申请实施例的提供的数据处理的方法的示意性流程图；
图6示出本申请实施例提供的数据处理的装置的结构示意图；
图7示出本申请实施例提供的终端的结构示意图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚地描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员所获得的所有其他实施例，都属于本申请保护的范围。
本申请的说明书和权利要求书中的术语“第一”、“第二”等是用于区别类似的对象，而不用于描述特定的顺序或先后次序。应该理解这样使用的术语在适当情况下可以互换，以便本申请的实施例能够以除了在这里图示或描述的那些以外的顺序实施，且“第一”、“第二”所区别的对象通常为一类，并不限定对象的个数，例如第一对象可以是一个，也可以是多个。此外，说明书以及权利要求中“和/或”表示所连接对象的至少其中之一，字符“/”一般表示前后关联对象是一种“或”的关系。
值得指出的是，本申请实施例所描述的技术不限于长期演进型(Long Term Evolution，LTE)/LTE的演进(LTE-Advanced，LTE-A)系统，还可用于其他无线通信系统，诸如码分多址(Code Division Multiple Access，CDMA)、时分多址(Time Division Multiple Access，TDMA)、频分多址(Frequency Division Multiple Access，FDMA)、正交频分多址(Orthogonal Frequency  Division Multiple Access，OFDMA)、单载波频分多址(Single-carrier Frequency-Division Multiple Access，SC-FDMA)和其他系统。本申请实施例中的术语“系统”和“网络”常被可互换地使用，所描述的技术既可用于以上提及的系统和无线电技术，也可用于其他系统和无线电技术。以下描述出于示例目的描述了新空口(New Radio，NR)系统，并且在以下大部分描述中使用NR术语，但是这些技术也可应用于NR系统应用以外的应用，如第6代(6th Generation，6G)通信系统。
图1示出本申请实施例可应用的一种无线通信系统的示意图。无线通信系统包括终端11和网络侧设备12。其中，终端11可以是手机、平板电脑(Tablet Personal Computer)、膝上型电脑(Laptop Computer)或称为笔记本电脑、个人数字助理(Personal Digital Assistant，PDA)、掌上电脑、上网本、超级移动个人计算机(ultra-mobile personal computer，UMPC)、移动上网装置(Mobile Internet Device，MID)、增强现实(augmented reality，AR)/虚拟现实(virtual reality，VR)设备、机器人、可穿戴式设备(Wearable Device)、车载设备(Vehicle User Equipment，VUE)、行人终端(Pedestrian User Equipment，PUE)、智能家居(具有无线通信功能的家居设备，如冰箱、电视、洗衣机或者家具等)、游戏机、个人计算机(personal computer，PC)、柜员机或者自助机等终端侧设备，可穿戴式设备包括：智能手表、智能手环、智能耳机、智能眼镜、智能首饰(智能手镯、智能手链、智能戒指、智能项链、智能脚镯、智能脚链等)、智能腕带、智能服装等。需要说明的是，在本申请实施例并不限定终端11的具体类型。网络侧设备12可以包括接入网设备或核心网设备，其中，接入网设备12也可以称为无线接入网设备、无线接入网(Radio Access Network,RAN)、无线接入网功能或无线接入网单元。接入网设备12可以包括基站、无线局域网(Wireless Local Area Network，WLAN)接入点或WiFi节点等，基站可被称为节点B、演进节点B(evolved Node B，eNB)、接入点、基收发机站(Base Transceiver Station，BTS)、无线电基站、无线电收发机、 基本服务集(Basic Service Set，BSS)、扩展服务集(Extended Service Set，ESS)、家用B节点、家用演进型B节点、发送接收点(Transmitting Receiving Point，TRP)或领域中其他某个合适的术语，只要达到相同的技术效果，基站不限于特定技术词汇，需要说明的是，在本申请实施例中仅以NR系统中的基站为例进行介绍，并不限定基站的具体类型。
例如，以NR系统为例，其参数如下：频带(Frequency band)为2.6G，带宽为100M，系统模式为时分双工(Time Division Duplexing，TDD)、子载波间隔配置为30kHz，用户设备(User Equipment，UE)为1个，接收天线的个数为32个，发射天线的个数为1个，UE分配的资源块(Resource Block，RB)数为273个，UE分配的符号数为12个，每个RB上的资源元素(Resource Element，RE)数据为12个，解调参考信号(Demodulation Reference Signal，DMRS)的类型为type A。
下面结合附图，通过一些实施例及其应用场景对本申请实施例提供的数据处理的方法进行详细地说明。
如图2所示，本申请的一个实施例提供一种数据处理的方法200，该方法可以由终端设备执行，换言之，该方法可以由安装在终端设备的软件或硬件来执行，该方法包括如下步骤：
步骤S201：对多个接收天线的接收数据进行空间分解，得到多个子空间。
具体来讲，对多个接收天线接收到的接收数据进行空间分解和重构，对多个接收天线的接收数据进行空间分解例如可以是采用特征值分解(Eigen decomposition，EVD)、奇异值分解(Singular Value Decomposition，SVD)，正三角(Quadrature Rectangle，QR)分解、施米特正交化(Schmidt Orthogonalization)等方式。
在一种可能的实现方式中，对多个接收天线的接收数据进行空间分解包括：
将接收数据转换到频域，得到频域接收数据，根据多个接收天线的资源 元素计算频域接收数据的协方差矩阵，并对协方差矩阵进行第一分解处理，得到多个子空间。
具体来讲，可以对接收数据进行傅里叶变换以及去循环前缀(去CP)等相关处理，得到频域接收数据，对于频域接收数据而言，其维度可以为UE分配的RB数、UE分配的符号数、每个RB上的RE数以及接收天线的个数四者的乘积，例如以上述NR系统的参数为例，UE分配的RB数、UE分配的符号数、每个RB上的RE数以及接收天线的个数分别为273、12、12和32，则得到的频域接收数据的维度则为(273×12×12)×32＝45864×32。
在一种可能的实现方式中，在计算频域接收数据的协方差矩阵时，可以按照下式进行计算：
其中，R为协方差矩阵，Nre为多个接收天线的资源元素的数量，Yi为多个接收天线上资源元素组成的矢量，Y为多个接收天线的接收数据。
例如，按照上述给出的NR系统的参数，Nre可以为273×12＝3276，对于32根天线而言，每一个Yi的维度是1×32，最终得到的协方差矩阵R是一个32×32的对称矩阵。
其中，第一分解处理包括以下一者：
(1)奇异值分解(Singular Value Decomposition，SVD)处理。
(2)正三角分解处理。
进一步，在第一分解处理包括上述(1)奇异值分解处理的情况下，对协方差矩阵进行第一分解处理包括：根据以下公式进行第一分解处理：
[U,S,V]＝svd(R)
其中，R为协方差矩阵，U为左奇异矩阵，V为右奇异矩阵，V的每一列为一个子空间，每一列为多个接收天线的接收数据的加权系数。
在第一分解处理包括上述(2)正三角分解处理的情况下，对协方差矩阵进行第一分解处理包括：根据以下公式进行第一分解处理：
[Q,T]＝qr(R)
其中，R为协方差矩阵，Q为正交矩阵，T为上三角矩阵，Q代表的是分解后的子空间，Q的每一列可以作为一个子空间。
步骤S203：测量至少一个子空间的信号质量。
具体来讲，上述的V矩阵中的每一列可以作为一个子空间，在测量至少一个子空间的信号质量时，可以通过每个子空间的信道估计值的信噪比或者每个子空间的最大功率来测量至少一个子空间的信号质量，信道估计值的信噪比或者最大功率可以用来表征子空间的信号质量，信道估计值的信噪比和最大功率的值越大，子空间的信号质量则越好。
步骤S205：根据子空间的信号质量，从多个子空间中选取目标子空间。
具体来讲，借助于上述步骤中的信号质量的测量量，从多个子空间中找到信号较优的目标子空间。
步骤S207：通过目标子空间对接收数据进行处理。
具体来讲，通过目标子空间进行空间过滤处理，获取目标子空间中的接收数据，从而对接收数据进行过滤，达到对接收数据进行降维的目的，在对接收数据进行过滤降维后，获取到目标子空间中的接收数据，对目标子空间中的接收数据做后续处理，如信道估计处理、均衡处理、解调处理、比特级的解扰处理、解速率匹配处理、解码处理以及解CRC处理等。
本申请实施例提供的数据处理的方法，通过对多个接收天线的接收数据进行空间分解，得到多个子空间，测量至少一个子空间的信号质量，根据子空间的信号质量，从多个子空间中选取目标子空间，通过目标子空间对接收数据进行处理，能够利用选择的目标子空间对接收数据进行处理，从而对接收数据进行过滤，丢弃无用的数据，降低接收数据的维度，在后续处理的过程中，减少接收机对接收数据进行处理的复杂度和能耗。
在一种可能的实现方式中，如图3所示的，另一种数据处理的方法300中根据子空间的信号质量，从多个子空间中选取目标子空间包括以下步骤：
步骤S301：根据每个子空间的信道估计值的信噪比，从多个子空间中选取目标子空间。
具体来讲，以每个子空间的信道估计值的信噪比作为测量每个子空间的信号质量的测量量，从而选取出信号较优的目标子空间。
其中，根据每个子空间的信道估计值的信噪比，从多个子空间中选取目标子空间包括：选取信道估计值的信噪比大于门限值的子空间为目标子空间。
其中，门限值可以根据多个子空间的信道估计值的信噪比的平均值确定。
具体来讲，对各个子空间的信道估计值的信噪比的平均值ANRmean可以按下式计算：ANRmean＝mean(ANRh,j)，在确定门限值时，可以取信噪比的平均值ANRmean的整数倍作为门限值，例如，N可以取值为2，门限值Thranr可以如下式表示：Thranr＝2ANRmean，找出各个子空间所有的ANRh,j大于该门限值的索引，得到集合Index，其中，Index＝find(ANRh,j>Thranr)，再从上述V矩阵中取出集合Index中索引对应的列作为较优的目标子空间，例如，集合Index中包含2个ANRh,j大于门限值的索引，则从V矩阵中取出该2个索引对应的列作为目标子空间，目标子空间的集合Gs的维度则为32×2，具体可以采用下式表示：Gs＝V(:,Index)。
得到Gs之后，将全部接收天线与搜索得到的目标子空间的集合进行空间过滤处理，获取到目标子空间的接收数据data'rx，具体如下式：data'rx＝datarxGs，其中，datarx为接收天线所接收到的接收数据。
在一种可能的实现方式中，信道估计值的信噪比通过下式计算：
其中，ANRh,j为第j个子空间的信道估计值的信噪比，为第j个子空间经降噪之后的时域信道估计值，为的模的最大值，为的平均值。
在一种可能的实现方式中，时域信道估计值通过下式计算：
其中，hftime为降噪前的时域信道估计值，其中，hftime是通过将频域信道估计值转换到时域得到的。
在一种可能的实现方式中，频域信道估计值通过以下方式计算得到：
根据多个接收天线的解调参考信号和每个子空间中的加权系数，计算多个接收天线在每个子空间的目标解调参考信号；对每个子空间的目标解调参考信号进行信道估计，得到每个子空间的频域信道估计值。
具体的，目标解调参考信号通过下式计算：
data'dmrs＝datadmrs·G
其中，datadmrs为解调参考信号，G为子空间中的加权系数，data'dmrs为目标解调参考信号。
例如，对于上述的V矩阵，取出V矩阵中当前子空间的加权系数，对于一个子空间而言，其可以通过方式G＝V(:,j)取出子空间内的加权系数，其中，j指的是当前子空间的索引，以上述NR系统给出的参数，接收天线的个数为32，则取出的当前子空间的G则为32×1向量。
再例如，对于上述所举例的NR系统，UE分配的RB数为273个，UE每个RB上的RE数为12个，接收天线的数量为32个，所有接收天线上的解调参考信号的数目则为273×12÷2＝1638个，解调参考信号的数据维度则为1638×32，将解调参考信号与G相乘后，得到的目标解调参考信号则为1638×1的向量。
在一种可能的实现方式中，频域信道估计值通过下式计算：
其中，为频域信道估计值，pilot为导频序列，conj(pilot)为导频序列的 共轭。
具体来讲，pilot为根据NR系统的系统参数生成的导频序列，例如按照上述NR系统的参数生成的导频序列的维度则为1638×1。
在一种可能的实现方式中，如图4所示的，另一种数据处理的方法400中根据子空间的信号质量，从多个子空间中选取目标子空间包括以下步骤：
步骤S401：根据每个子空间的最大功率，从多个子空间中选取目标子空间。
具体来讲，以每个子空间的最大功率作为测量每个子空间的信号质量的测量量，从而选取出信号较优的目标子空间。
在一种可能的实现方式中，如图5所示的，另一种数据处理的方法500中根据每个子空间的最大功率，从多个子空间中选取目标子空间包括以下步骤：
步骤S501：根据每个子空间的频域信道估计值计算每个子空间的功率最大值。
在一种可能的实现方式中，功率最大值通过下式计算：
其中，为频域信道估计值，PH,j为功率最大值。
步骤S503：对功率最大值进行降序排列，选取排序前M个的功率最大值对应的子空间为目标子空间。
具体来讲，对PH,j按照从大到小的顺序排序，取能量最高的M列，M的是根据每次传输的传输块的数量确定的，或者是根据接收天线的数量和每次传输的传输块的数量确定的，更为具体的M的取值范围可以为流数～min(接收天线的数量,流数+2)，其中，流数为每次传输的传输块(Transport Block，TB)数，min(接收天线的数量,流数+2)表示的是“接收天线的数量”和“流数+2”中的最小值，M的取值则处于每次传输的TB数与min(接收天线的数量,流数+2)所围成的区间，如M取1，接收天线的个数为32时，选取得到 的目标子空间的维度为32×1，即可以采用下式表示：Gs＝V(:,Index(1))。
需要说明的是，本申请实施例提供的数据处理的方法，执行主体可以为数据处理的装置，本申请实施例中以数据处理的装置执行加载数据处理的方法为例，说明本申请实施例提供的数据处理的装置。
图6是根据本申请实施例的数据处理的装置的结构示意图。如图6所示，数据处理的装置600包括：分解模块601，用于对多个接收天线的接收数据进行空间分解，得到多个子空间；测量模块602，用于测量至少一个子空间的信号质量；选取模块603，用于根据子空间的信号质量，从多个子空间中选取目标子空间；处理模块604，用于通过目标子空间对接收数据进行处理。
在本申请实施例中，通过对多个接收天线的接收数据进行空间分解，得到多个子空间，测量至少一个子空间的信号质量，根据子空间的信号质量，从多个子空间中选取目标子空间，通过目标子空间对接收数据进行处理，能够利用选择的目标子空间对接收数据进行处理，从而对接收数据进行过滤，丢弃无用的数据，降低接收数据的维度，在后续处理的过程中，减少接收机对接收数据进行处理的复杂
在一种可能的实现方式中，分解模块601，还用于将接收数据转换到频域，得到频域接收数据；根据多个接收天线的资源元素计算频域接收数据的协方差矩阵，并对协方差矩阵进行第一分解处理，得到多个子空间。
在一种可能的实现方式中，第一分解处理包括奇异值分解处理，分解模块601，还用于根据以下公式进行第一分解处理：
[U,S,V]＝svd(R)
其中，R为协方差矩阵，U为左奇异矩阵，V的每一列为一个子空间，每一列为多个接收天线的接收数据的加权系数。
在一种可能的实现方式中，第一分解处理包括正三角分解处理，分解模块601，还用于根据以下公式进行第一分解处理：
[Q,T]＝qr(R)
其中，R为协方差矩阵，Q为正交矩阵，T为上三角矩阵。
在一种可能的实现方式中，协方差矩阵通过下式计算：
其中，Nre为多个接收天线的资源元素的数量，Yi为多个接收天线
上资源元素组成的矢量，Y为多个接收天线的接收数据。
在一种可能的实现方式中，选取模块603，还用于根据每个子空间的信道估计值的信噪比，从多个子空间中选取目标子空间。
在一种可能的实现方式中，选取模块603，还用于根据每个子空间的最大功率，从多个子空间中选取目标子空间。
在一种可能的实现方式中，选取模块603，还用于选取信道估计值的信噪比大于门限值的子空间为目标子空间；
信道估计值的信噪比通过下式计算：
其中，ANRh,j为第j个子空间的信道估计值的信噪比，为第j个子空间经降噪之后的时域信道估计值，为的模的最大值，为的平均值。
在一种可能的实现方式中，时域信道估计值通过下式计算：
其中，hftime为降噪前的时域信道估计值，其中，hftime是通过将频域信道估计值转换到时域得到的。
在一种可能的实现方式中，频域信道估计值通过以下方式计算得到：
根据多个接收天线的解调参考信号和每个子空间中的加权系数，计算多个接收天线在每个子空间的目标解调参考信号；对每个子空间的目标解调参考信号进行信道估计，得到每个子空间的频域信道估计值。
在一种可能的实现方式中，目标解调参考信号通过下式计算：
data'dmrs＝datadmrs·G
其中，datadmrs为解调参考信号，G为子空间中的加权系数，data'dmrs为目标解调参考信号；
频域信道估计值通过下式计算：
其中，为频域信道估计值，pilot为导频序列，conj(pilot)为导频序列的共轭。
在一种可能的实现方式中，门限值根据多个子空间的信道估计值的信噪比的平均值确定。
在一种可能的实现方式中，选取模块603，还用于根据每个子空间的频域信道估计值计算每个子空间的功率最大值；对功率最大值进行降序排列，选取排序前M个的功率最大值对应的子空间为目标子空间，M的取值是根据每次传输的传输块的数量确定的，或者是根据接收天线的数量和每次传输的传输块的数量确定的。
在一种可能的实现方式中，功率最大值通过下式计算：
其中，为频域信道估计值，PH,j为功率最大值。
本申请实施例中的数据处理的装置可以是电子设备，例如具有操作系统的电子设备，也可以是电子设备中的部件，例如集成电路或芯片。该电子设备可以是终端，也可以为除终端之外的其他设备。示例性的，终端可以包括但不限于上述所列举的终端11的类型，其他设备可以为服务器、网络附属存储器(Network Attached Storage，NAS)等，本申请实施例不作具体限定。
本申请实施例提供的数据处理的装置能够实现图2至图5的方法实施例实现的各个过程，并达到相同的技术效果，为避免重复，这里不再赘述。
可选的，如图7所示，本申请实施例还提供一种终端700，包括处理器 701和存储器702，存储器702上存储有可在处理器701上运行的程序或指令，该程序或指令被处理器701执行时实现上述数据处理的方法实施例的各个步骤，且能达到相同的技术效果。
本申请实施例还提供一种可读存储介质，所述可读存储介质上存储有程序或指令，该程序或指令被处理器执行时实现上述数据处理的方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
其中，所述处理器为上述实施例中所述的终端中的处理器。所述可读存储介质，包括计算机可读存储介质，如计算机只读存储器(Read Only Memory，ROM)、随机存取存储器(Random Access Memory，RAM)、磁碟或者光盘等。
本申请实施例另提供了一种芯片，所述芯片包括处理器和通信接口，所述通信接口和所述处理器耦合，所述处理器用于运行程序或指令，实现上述数据处理的方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
应理解，本申请实施例提到的芯片还可以称为系统级芯片，系统芯片，芯片系统或片上系统芯片等。
本申请实施例另提供了一种计算机程序/程序产品，所述计算机程序/程序产品被存储在存储介质中，所述计算机程序/程序产品被至少一个处理器执行以实现上述数据处理的方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。此外，需要指出的是，本申 请实施方式中的方法和装置的范围不限按示出或讨论的顺序来执行功能，还可包括根据所涉及的功能按基本同时的方式或按相反的顺序来执行功能，例如，可以按不同于所描述的次序来执行所描述的方法，并且还可以添加、省去、或组合各种步骤。另外，参照某些示例所描述的特征可在其他示例中被组合。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以计算机软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本申请各个实施例所述的方法。
上面结合附图对本申请的实施例进行了描述，但是本申请并不局限于上述的具体实施方式，上述的具体实施方式仅仅是示意性的，而不是限制性的，本领域的普通技术人员在本申请的启示下，在不脱离本申请宗旨和权利要求所保护的范围情况下，还可做出很多形式，均属于本申请的保护之内。

Claims (16)

  1. 一种数据处理的方法，其中，所述方法包括：
    对多个接收天线的接收数据进行空间分解，得到多个子空间；
    测量至少一个所述子空间的信号质量；
    根据所述子空间的信号质量，从多个所述子空间中选取目标子空间；
    通过所述目标子空间对所述接收数据进行处理。
  2. 如权利要求1所述的方法，其中，所述对多个所述接收天线的接收数据进行空间分解包括：
    将所述接收数据转换到频域，得到频域接收数据；
    根据多个所述接收天线的资源元素计算所述频域接收数据的协方差矩阵，并对所述协方差矩阵进行第一分解处理，得到所述多个子空间。
  3. 如权利要求2所述的方法，其中，所述第一分解处理包括奇异值分解处理，所述对所述协方差矩阵进行第一分解处理包括：
    根据以下公式进行所述第一分解处理：
    [U,S,V]＝svd(R)
    其中，R为所述协方差矩阵，U为左奇异矩阵，V的每一列为一个子空间，每一列为多个接收天线的接收数据的加权系数。
  4. 如权利要求2所述的方法，其中，所述第一分解处理包括正三角分解处理，所述对所述协方差矩阵进行第一分解处理包括：
    根据以下公式进行所述第一分解处理：
    [Q,T]＝qr(R)
    其中，R为所述协方差矩阵，Q为正交矩阵，T为上三角矩阵。
  5. 如权利要求2所述的方法，其中，所述协方差矩阵通过下式计算：
    其中，R为协方差矩阵，Nre为多个所述接收天线的资源元素的数量，Yi为 多个所述接收天线上资源元素组成的矢量，Y为多个接收天线的接收数据。
  6. 根据权利要求1所述的方法，其中，所述根据所述子空间的信号质量，从多个所述子空间中选取目标子空间，包括：
    根据每个所述子空间的信道估计值的信噪比，从多个所述子空间中选取目标子空间。
  7. 根据权利要求1所述的方法，其中，所述根据所述子空间的信号质量，从多个所述子空间中选取目标子空间，包括：
    根据每个所述子空间的最大功率，从多个所述子空间中选取目标子空间。
  8. 根据权利要求6所述的方法，其中，所述根据每个所述子空间的信道估计值的信噪比，从多个所述子空间中选取目标子空间包括：
    选取所述信道估计值的信噪比大于门限值的子空间为所述目标子空间；
    所述信道估计值的信噪比通过下式计算：
    其中，ANRh,j为第j个子空间的信道估计值的信噪比，为第j个子空间经降噪之后的时域信道估计值，为的模的最大值，为的平均值。
  9. 根据权利要求8所述的方法，其中，所述时域信道估计值通过下式计算：
    其中，hftime为降噪前的时域信道估计值，其中，所述hftime是通过将频域信道估计值转换到时域得到的。
  10. 根据权利要求9所述的方法，其中，所述频域信道估计值通过以下方式计算得到：
    根据多个所述接收天线的解调参考信号和每个所述子空间中的加权系数， 计算多个所述接收天线在每个所述子空间的目标解调参考信号；
    对每个所述子空间的所述目标解调参考信号进行信道估计，得到每个所述子空间的频域信道估计值。
  11. 根据权利要求10所述的方法，其中，所述目标解调参考信号通过下式计算：
    data'dmrs＝datadmrs·G
    其中，datadmrs为所述解调参考信号，G为子空间中的加权系数，data'dmrs为目标解调参考信号；
    所述频域信道估计值通过下式计算：
    其中，为频域信道估计值，pilot为导频序列，conj(pilot)为导频序列的共轭。
  12. 根据权利要求8所述的方法，其中，所述门限值根据多个所述子空间的信道估计值的信噪比的平均值确定。
  13. 根据权利要求7所述的方法，其中，所述根据每个子空间的最大功率，从多个所述子空间中选取目标子空间，包括：
    根据每个所述子空间的频域信道估计值计算每个子空间的功率最大值；
    对所述功率最大值进行降序排列，选取排序前M个的功率最大值对应的子空间为所述目标子空间，所述M的取值是根据每次传输的传输块的数量确定的，或者是根据接收天线的数量和每次传输的传输块的数量确定的。
  14. 根据权利要求13所述的方法，其中，所述功率最大值通过下式计算：
    PH,j＝max(|Hj freq|2)
    其中，为频域信道估计值，PH,j为所述功率最大值。
  15. 一种终端，其中，包括处理器和存储器，所述存储器存储可在所述 处理器上运行的程序或指令，所述程序或指令被所述处理器执行时实现如权利要求1至14任一项所述的数据处理的方法的步骤。
  16. 一种可读存储介质，其中，所述可读存储介质上存储程序或指令，所述程序或指令被处理器执行时实现如权利要求1至14中任一项所述的数据处理的方法的步骤。
PCT/CN2023/113108 2022-09-06 2023-08-15 数据处理的方法、终端及可读存储介质 WO2024051452A1 (zh)

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