WO2021109639A1 - 信道估计方法、装置、设备及存储介质 - Google Patents

信道估计方法、装置、设备及存储介质 Download PDF

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WO2021109639A1
WO2021109639A1 PCT/CN2020/111850 CN2020111850W WO2021109639A1 WO 2021109639 A1 WO2021109639 A1 WO 2021109639A1 CN 2020111850 W CN2020111850 W CN 2020111850W WO 2021109639 A1 WO2021109639 A1 WO 2021109639A1
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channel estimation
power
path
training sequence
paths
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PCT/CN2020/111850
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English (en)
French (fr)
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梁立宏
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深圳市中兴微电子技术有限公司
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Priority to EP20897338.8A priority Critical patent/EP4072088A4/en
Publication of WO2021109639A1 publication Critical patent/WO2021109639A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0204—Channel estimation of multiple channels
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0212—Channel estimation of impulse response
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0224—Channel estimation using sounding signals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0224—Channel estimation using sounding signals
    • H04L25/0228—Channel estimation using sounding signals with direct estimation from sounding signals

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  • the present disclosure relates to wireless communication networks, and in particular to a channel estimation method, device, equipment and storage medium.
  • the receiving end will perform channel estimation and perform channel equalization based on the results of the channel estimation to compensate for the influence of channel fading.
  • the accuracy of channel estimation determines the channel equalization performance of the receiver to a large extent.
  • the existing channel estimation methods have the problem of insufficient noise suppression or filtering out part of the signal, and the channel estimation accuracy is poor.
  • the present disclosure provides methods, devices, equipment and storage media for channel estimation, which can improve the accuracy of channel estimation.
  • An embodiment of the present disclosure provides a channel estimation method, including: determining an initial channel estimate according to a received time domain training sequence and a local time domain training sequence, and calculating the power of each path of the initial channel estimation; in the initial channel estimation Set a channel estimation window of a set length, and determine the noise threshold according to the power of the inner diameter of the channel estimation window and/or the power of the outer diameter of the channel estimation window; according to the noise threshold and the power of each diameter in the channel estimation window Determine the number of effective paths; perform path selection on the initial channel estimate according to the number of effective paths to obtain a target channel estimate.
  • An embodiment of the present disclosure provides a channel estimation device, including: an initial channel estimation determining module, configured to determine an initial channel estimate according to a received time domain training sequence and a local time domain training sequence, and calculate the power of each path of the initial channel estimate A noise threshold determination module, configured to set a channel estimation window of a set length in the initial channel estimation, and determine the noise threshold according to the power of the inner diameter of the channel estimation window and/or the power of the outer diameter of the channel estimation window; effective The path quantity determination module is configured to determine the effective path quantity according to the noise threshold and the power of each path in the channel estimation window; the target channel estimation acquisition module is configured to select the initial channel estimate according to the effective path quantity Path to obtain the target channel estimate.
  • an initial channel estimation determining module configured to determine an initial channel estimate according to a received time domain training sequence and a local time domain training sequence, and calculate the power of each path of the initial channel estimate
  • a noise threshold determination module configured to set a channel estimation window of a set length in the initial channel estimation, and determine the noise threshold
  • the embodiment of the present disclosure provides a communication device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor.
  • the processor implements the channel as described in the embodiment of the present disclosure when the program is executed. Estimate method.
  • the embodiment of the present disclosure provides a storage medium that stores a computer program that, when executed by a processor, implements the channel estimation method described in the embodiment of the present disclosure.
  • Fig. 1 is a flowchart of a channel estimation method in an embodiment of the disclosure
  • Fig. 2 is a structural block diagram of a channel estimation device in an embodiment of the disclosure
  • FIG. 3 is a schematic structural diagram of a device in an embodiment of the disclosure.
  • PBCH physical broadcast channel
  • DMRS Demodulation Reference Signal
  • Fig. 1 is a flowchart of a channel estimation method provided by an embodiment of the present disclosure, and this embodiment is suitable for the case of channel estimation.
  • This embodiment can be executed by the receiving end.
  • the receiving end may be a scheduling node (for example, a base station, an access point, etc.) or a user terminal (User Equipment, UE).
  • the method provided in this embodiment includes S110-S140.
  • S110 Determine an initial channel estimate according to the received time domain training sequence and the local time domain training sequence, and calculate the power of each path of the initial channel estimate.
  • the initial channel estimation is the initial channel estimation in the time domain.
  • the method of determining the initial channel estimation according to the received time domain training sequence and the local time domain training sequence may be: correlating the received time domain training sequence and the local time domain training sequence to obtain the initial channel estimation.
  • the cell ID and cell timing can be obtained.
  • 256 samples of 1 symbol in the time domain are received.
  • Point PBCH DMRS that is, the received time-domain training sequence. Perform correlation calculations on the received time domain training sequence and the local time domain training sequence to obtain an initial channel estimate.
  • the method of determining the initial channel estimation according to the received time domain training sequence and the local time domain training sequence may be: performing Fourier transform on the received time domain training sequence and local time domain training sequence to obtain the first A frequency domain training sequence and a second frequency domain training sequence; the first frequency domain training sequence and the second frequency domain training sequence are multiplied, and the product is subjected to an inverse Fourier transform to obtain an initial channel estimate.
  • a 256-sample fast Fourier transform is performed to obtain a frequency-domain training sequence.
  • OFDM Orthogonal Frequency Division Multiplexing
  • Hi [Hi,30,Hi,31,..., Hi,59,zeros(1,68),H i,0,Hi,1,...,H i,29].
  • H i,0 represents the descrambling result of the 0th subcarrier among the 60 subcarriers of the symbol i
  • Hi,1 represents the descrambling result of the 1st subcarrier among the 60 subcarriers of the symbol i, others are similar
  • Zeros( 1,68) represents 68 zeros.
  • the calculation method of the power of each path of the initial channel estimation is: the real part of the path and the virtual sum of squares are then squared.
  • S120 Set a channel estimation window of a set length in the initial channel estimation, and determine a noise threshold according to the power of the inner diameter of the channel estimation window and/or the power of the outer diameter of the channel estimation window.
  • the set length can be understood as the number of samples in the channel estimation window, which can be determined by the protocol or determined by the number of samples in the time-domain training sequence of the received signal.
  • the method of determining the noise threshold according to the power of the inner diameter of the channel estimation window may be: calculating the power average of the first number of paths with the smallest power in the channel estimation window and determining it as the noise power; The noise power is weighted to obtain the noise threshold.
  • the first number can be determined according to the length of the channel estimation window.
  • the noise threshold factor can be set to any value between 3-5.
  • the power of the W paths in the channel estimation window is sorted from large to small
  • the power average is determined as the noise power. Then multiply the noise power by the set noise threshold factor to obtain the noise threshold.
  • the method of determining the noise threshold according to the power of the outer diameter of the channel estimation window may be: calculating the average power of each diameter outside the channel estimation window and determining it as the noise power; weighting the noise power according to the set noise threshold factor to obtain the noise Threshold.
  • the set noise threshold factor can be set to any value between 3-5.
  • the noise power is multiplied by the set noise threshold factor to obtain the noise threshold.
  • S130 Determine the number of effective paths according to the noise threshold and the power of each path in the channel estimation window.
  • the method of determining the number of effective paths according to the noise threshold and the power of each path may be: comparing the power of each path in the channel estimation window with the noise threshold respectively to obtain the number of paths whose power is greater than the noise threshold, Determine the number of effective diameters.
  • the method further includes the following steps: determining the path with power greater than the noise threshold in the channel estimation window as the initial effective path, and calculating the total power of the initial effective path as the first 1.
  • Total power calculate the total power of the second number of paths with the largest power in the initial effective path as the second total power; calculate the total power of the third number of paths with the largest power in the initial effective path as the third total power;
  • the second number is 1 greater than the third number; if the ratio of the second total power to the first total power is greater than or equal to the first set value, and the ratio of the third total power to the first total power is less than the first set Value, the second quantity is determined as the effective path quantity.
  • the first set value is a positive number less than 1.
  • S140 Perform path selection on the initial channel estimate according to the number of effective paths to obtain a target channel estimate.
  • the method further includes the following step: returning to execute determining the initial time domain channel according to the received time domain training sequence and the local time domain training sequence The steps of estimating until the number of effective paths reaches the set number of times.
  • the number of effective paths is N0, N1,..., N8, respectively.
  • the initial channel estimation path is selected according to the number of effective paths, and the way to obtain the target channel estimation can be: calculating the average value of the number of effective paths for a set number of times, and selecting the initial channel estimation according to the average value of the number of effective paths to obtain the target channel estimation .
  • the formula for calculating the average value of the number of effective diameters of the above C times is: among them, Indicates rounding up.
  • the initial channel estimation is selected according to the mean value of the number of effective paths, and the way to obtain the target channel estimation may be: if the mean value is greater than the second set value, the maximum power quantity in the channel estimation window is the mean value The path is determined as the signal path, and the paths other than the signal path in the initial channel estimation are determined as the noise path; if the average value is less than the second set value, the maximum power in the channel estimation window is set to the second set value The path is determined as the signal path, and the other paths except the signal path in the initial channel estimation are determined as the noise path.
  • the second set value S1 can be set to 3. If the average value is greater than the second set value, the S paths with the largest power in the channel estimation window are determined as signal paths, and the other paths except the signal path in the initial channel estimation are determined as noise paths, and set to 0. If the average value is less than the second set value, the S1 path with the largest power in the channel estimation window is determined as the signal path, and the other paths except the signal path in the initial channel estimation are determined as the noise path, and set to 0.
  • the technical solution of the embodiment of the present disclosure first determines the initial channel estimation according to the received time domain training sequence and the local time domain training sequence, and calculates the power of each path of the initial channel estimation, and then sets the channel estimation of the set length in the initial channel estimation Window, and determine the noise threshold according to the channel estimation window, and then determine the number of effective paths according to the noise threshold and the power of each path in the channel estimation window, and finally select the initial channel estimation path according to the number of effective paths to obtain the target channel estimation.
  • the number of effective paths is determined according to the noise threshold and the power of each path in the channel estimation window, which can improve the accuracy of channel estimation.
  • Fig. 2 is a structural block diagram of a channel estimation device provided by an embodiment of the present disclosure. As shown in FIG. 2, the device includes: an initial channel estimation determining module 210, a noise threshold determining module 220, an effective path number determining module 230, and a target channel estimation acquiring module 240.
  • the initial channel estimation determining module 210 is configured to determine the initial channel estimation according to the received time domain training sequence and the local time domain training sequence, and calculate the power of each path of the initial channel estimation;
  • the noise threshold determination module 220 is configured to set a channel estimation window of a set length in the initial channel estimation, and determine the noise threshold according to the power of the inner diameter of the channel estimation window and/or the power of the outer diameter of the channel estimation window;
  • the effective path quantity determining module 230 is configured to determine the effective path quantity according to the noise threshold and the power of each path in the channel estimation window;
  • the target channel estimation obtaining module 240 is configured to select the initial channel estimation path according to the number of effective paths to obtain the target channel estimation.
  • the initial channel estimation determining module 210 is further configured to:
  • the initial channel estimation determining module 210 is further configured to:
  • Fourier transform is performed on the received time domain training sequence and the local time domain training sequence, respectively, to obtain the first frequency domain training sequence and the second frequency domain training sequence;
  • the noise threshold determination module 220 is further configured to:
  • the noise power is weighted according to the set noise threshold factor to obtain the noise threshold.
  • the noise threshold determination module 220 is further configured to:
  • the noise power is weighted according to the set noise threshold factor to obtain the noise threshold.
  • the effective path quantity determining module 230 is further configured to:
  • the power of each path in the channel estimation window is compared with the noise threshold, and the number of paths whose power is greater than the noise threshold is obtained and determined as the number of effective paths.
  • the effective path quantity determining module 230 is further configured to:
  • the second number is determined as the effective path number .
  • the method further includes:
  • the target channel estimation acquisition module 240 is further configured to:
  • the target channel estimation acquisition module 240 is further configured to:
  • the path with the largest power in the channel estimation window as the second set value is determined as the signal path, and the other paths except the signal path in the initial channel estimation are determined as the noise path.
  • Fig. 3 is a schematic structural diagram of a device provided by an embodiment of the present disclosure.
  • the device provided by the present disclosure includes: a processor 310 and a memory 320.
  • the number of processors 310 in the device may be one or more.
  • One processor 310 is taken as an example in FIG. 3.
  • the number of memories 320 in the device may be one or more, and one memory 320 is taken as an example in FIG. 3.
  • the processor 310 and the memory 320 of the device may be connected through a bus or in other ways. In FIG. 3, the connection through a bus is taken as an example.
  • the device is the receiving end. Among them, the receiving end may be one of a scheduling node, a base station, or a UE.
  • the memory 320 can be configured to store software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the device of any embodiment of the present disclosure (for example, the encoding module and the module in the data transmission device).
  • the memory 320 may include a program storage area and a data storage area.
  • the program storage area may store an operating system and an application program required by at least one function; the data storage area may store data created according to the use of the device, and the like.
  • the memory 320 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices.
  • the memory 320 may further include a memory remotely provided with respect to the processor 310, and these remote memories may be connected to the device through a network.
  • networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.
  • the above-provided device can be configured to execute the channel estimation method provided in any of the above-mentioned embodiments, and has corresponding functions and effects.
  • the program stored in the corresponding memory 320 may be a program instruction/module corresponding to the signal processing method provided by the embodiment of the present disclosure.
  • the processor 310 executes the computer equipment by running the software program, instruction and module stored in the memory 320 One or more functional applications and data processing, that is, to realize the signal processing method applied in the above method embodiment. It can be understood that, when the above-mentioned device is a receiving end, it can execute the signal processing method provided by any embodiment of the present disclosure, and have corresponding functions and effects. Wherein, the device may be one of a base station or a UE.
  • the embodiment of the present disclosure also provides a storage medium containing computer-executable instructions.
  • the computer-executable instructions are executed by a computer processor, they are used to execute a signal processing method.
  • the method includes: according to the received time-domain training sequence and local The time-domain training sequence determines the initial channel estimation, and calculates the power of each path of the initial channel estimation; in the initial channel estimation, a channel estimation window of a set length is set, and the power and/or the inner diameter of the channel estimation window are set in the initial channel estimation.
  • the power of the outer diameter of the channel estimation window determines the noise threshold; the number of effective paths is determined according to the noise threshold and the power of each diameter in the channel estimation window; the initial channel estimation is selected according to the number of effective paths to obtain Target channel estimation.
  • user equipment encompasses any suitable type of wireless user equipment, such as a mobile phone, a portable data processing device, a portable web browser, or a vehicle-mounted mobile station.
  • the various embodiments of the present disclosure may be implemented in hardware or special purpose circuits, software, logic or any combination thereof.
  • some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device, although the present disclosure is not limited thereto.
  • Computer program instructions can be assembly instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or written in any combination of one or more programming languages Source code or object code.
  • ISA Instruction Set Architecture
  • the block diagram of any logic flow in the drawings of the present disclosure may represent program steps, or may represent interconnected logic circuits, modules, and functions, or may represent a combination of program steps and logic circuits, modules, and functions.
  • the computer program can be stored on the memory.
  • the memory can be of any type suitable for the local technical environment and can be implemented using any suitable data storage technology, such as but not limited to read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), optical Memory devices and systems (Digital Video Disc (DVD) or Compact Disk (CD)), etc.
  • Computer-readable media may include non-transitory storage media.
  • the data processor can be any type suitable for the local technical environment, such as but not limited to general-purpose computers, special-purpose computers, microprocessors, digital signal processors (Digital Signal Processing, DSP), application specific integrated circuits (ASICs) ), programmable logic devices (Field-Programmable Gate Array, FGPA), and processors based on multi-core processor architecture.
  • DSP Digital Signal Processing
  • ASICs application specific integrated circuits
  • FGPA programmable logic devices

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Abstract

一种信道估计方法、装置、设备及存储介质。该方法包括：根据接收的时域训练序列和本地时域训练序列确定初始信道估计，并计算所述初始信道估计各个径的功率；在所述初始信道估计中设置设定长度的信道估计窗，并根据所述信道估计窗内径的功率和/或所述信道估计窗外径的功率确定噪声门限；根据所述噪声门限及所述信道估计窗内各个径的功率确定有效径数量；根据所述有效径数量对所述初始信道估计进行选径，获得目标信道估计。

Description

信道估计方法、装置、设备及存储介质 技术领域
本公开涉及无线通信网络，具体涉及一种信道估计方法、装置、设备及存储介质。
背景技术
在典型的无线移动通信系统中，由于受到通信环境的影响，接收信号中会存在多径衰落和噪声等，影响了接收信号的质量。为了能提升接收机的性能，接收端会进行信道估计，基于信道估计的结果进行信道均衡，弥补信道衰落的影响。信道估计的准确度在很大程度上决定性了接收机的信道均衡性能。现有信道估计方法存在对噪声抑制不够或者滤掉了部分信号的问题，信道估计精度差。
发明内容
本公开提供用于信道估计方法、装置、设备及存储介质，可以提高信道估计的准确性。
本公开实施例提供一种信道估计方法，包括：根据接收的时域训练序列和本地时域训练序列确定初始信道估计，并计算所述初始信道估计各个径的功率；在所述初始信道估计中设置设定长度的信道估计窗，并根据所述信道估计窗内径的功率和/或所述信道估计窗外径的功率确定噪声门限；根据所述噪声门限及所述信道估计窗内各个径的功率确定有效径数量；根据所述有效径数量对所述初始信道估计进行选径，获得目标信道估计。
本公开实施例提供一种信道估计装置，包括：初始信道估计确定模块，设置为根据接收的时域训练序列和本地时域训练序列确定初始信道估计， 并计算所述初始信道估计各个径的功率；噪声门限确定模块，设置为在所述初始信道估计中设置设定长度的信道估计窗，并根据所述信道估计窗内径的功率和/或所述信道估计窗外径的功率确定噪声门限；有效径数量确定模块，设置为根据所述噪声门限及所述信道估计窗内各个径的功率确定有效径数量；目标信道估计获取模块，设置为根据所述有效径数量对所述初始信道估计进行选径，获得目标信道估计。
本公开实施例提供一种通信设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现如本公开实施例所述的信道估计方法。
本公开实施例提供了一种存储介质，所述存储介质存储有计算机程序，所述计算机程序被处理器执行时实现本公开实施例所述的信道估计方法。
附图说明
图1为本公开实施例中的一种信道估计方法的流程图；
图2为本公开实施例中的一种信道估计装置的结构框图；
图3为本公开实施例中的一种设备的结构示意图。
具体实施方式
为使本公开的目的、技术方案和优点更加清楚明白，下文中将结合附图对本公开的实施例进行详细说明。需要说明的是，在不冲突的情况下，本公开中的实施例及实施例中的特征可以相互任意组合。
在5G系统的小区搜索中，需要对物理广播信道(Physical broadcast channe，PBCH)解调参考信号(Demodulation Reference Signal，DMRS)进行盲检测，其中关键的一步是对接收的PBCH DMRS信号进行信道估计。
在一实施例中，图1是本公开实施例提供的一种信道估计方法的流程 图，本实施例适用于对信道进行估计的情况。本实施例可以通过接收端来执行。其中，接收端可以为调度节点(例如，基站，接入点等)或用户终端(User Equipment，UE)。如图1所示，本实施例提供的方法包括S110-S140。
S110，根据接收的时域训练序列和本地时域训练序列确定初始信道估计，并计算初始信道估计各个径的功率。
其中，初始信道估计为时域初始信道估计。
在一个实施例中，根据接收的时域训练序列和本地时域训练序列确定初始信道估计的方式可以是：对接收到的时域训练序列和本地时域训练序列进行相关，获得初始信道估计。
根据小区搜索的主同步信号(Primary Synchronization Signal，PSS)和辅同步信号(Secondary Synchronization Signal，SSS)的检测过程，可以获得小区ID和小区定时，在此基础上接收时域上1个符号256样点的PBCH DMRS，即接收到的时域训练序列。将接收的时域训练序列和本地时域训练序列进行相关计算，获得初始信道估计。
在一个实施例中，根据接收的时域训练序列和本地时域训练序列确定初始信道估计的方式可以是：对接收的时域训练序列和本地时域训练序列分别进行傅里叶变换，获得第一频域训练序列和第二频域训练序列；将第一频域训练序列和第二频域训练序列进行相乘，并对乘积进行傅里叶逆变换，获得初始信道估计。
在接收到时域上1个符号256样点的PBCH DMRS后，做256样点的快速傅里叶变换(fast Fourier transform，FFT)，获得频域训练序列。
根据协议3GPP38.211的7.4.3节上定义的PBCH DMRS和SSS子载波序号，抽取正交频分复用(Orthogonal Frequency Division Multiplexing，OFDM)OFDM符号1、2、3上的60个子载波信号R1，R2，R3，其中符号2上PBCH DMRS、SSS统一按照PBCH DMRS子载波偏移来抽取，和本地的频域训练序列S1,S2,S3的共轭相乘，得到频域的解扰结果Hi＝Ri*conj(Si)，i＝1,2,3，其中符号2上保护子载波的解扰结果设置为0， 或者通过相邻的解扰结果线性内插得到。
要对Hi做128样点的快速傅里叶逆变换(Inverse Fast Fourier Transform，IFFT)，需要首先对包含60个子载波的解扰结果补零：Hi＝[Hi,30,Hi,31,…,Hi,59,zeros(1,68),H i,0,Hi,1,…,H i,29]。其中，H i,0表示符号i的60个子载波中的第0个子载波的解扰结果，Hi,1表示符号i的60个子载波中的第1个子载波的解扰结果，其他类似，Zeros(1,68)表示68个0。对补零后的解扰结果做128样点的IFFT变换，获得各符号对应的初始信道估计hi＝ifft(Hi)。然后计算3个符号初始信道估计的平均值，h＝(h1+h2+h3)/3＝[h0,h1,…,h127]。
在一个实施例中，初始信道估计各个径的功率的计算方式为：径的实部和虚拟的平方和再开方。
S120，在初始信道估计中设置设定长度的信道估计窗，并根据信道估计窗内径的功率和/或所述信道估计窗外径的功率确定噪声门限。
其中，设定长度可以理解为信道估计窗中的样点数，可以由协议确定或者接收到的信号的时域训练序列中的样点数确定。
在一个实施例中，根据信道估计窗内径的功率确定噪声门限的方式可以是：计算信道估计窗内功率最小的第一数量的径的功率均值，确定为噪声功率；根据设定噪声门限因子对噪声功率进行加权，获得噪声门限。
其中，第一数量可以根据信道估计窗的长度确定。设定噪声门限因子可以设置为3-5之间的任意值。
在一个实施例中，信道估计窗的长度为W＝24个样点，对信道估计窗内W个径的功率从大到小排序，并计算信道估计窗内功率最小的M＝8个径的功率均值，确定为噪声功率。然后将噪声功率乘以设定噪声门限因子，获得噪声门限。
在一个实施例中，根据信道估计窗外径的功率确定噪声门限的方式可以是：计算信道估计窗外各个径的功率均值，确定为噪声功率；根据设定噪声门限因子对噪声功率进行加权，获得噪声门限。
其中，设定噪声门限因子可以设置为3-5之间的任意值。将噪声功率与设定噪声门限因子相乘获得噪声门限。
S130，根据噪声门限及信道估计窗内各个径的功率确定有效径数量。
在一个实施例中，根据噪声门限及各个径的功率确定有效径数量的方式可以是：将信道估计窗内的各个径的功率与噪声门限分别进行比较，获取功率大于噪声门限的径的数量，确定为有效径数量。
在一个实施例中，在获取功率大于噪声门限的径的数量之后，还包括如下步骤：将信道估计窗内功率大于噪声门限的径确定为初始有效径，计算初始有效径的总功率，作为第一总功率；计算初始有效径中功率最大的第二数量的径的总功率，作为第二总功率；计算初始有效径中功率最大的第三数量的径的总功率，作为第三总功率；其中，第二数量比第三数量大1；若第二总功率与第一总功率的比值大于或等于第一设定值，且第三总功率与第一总功率的比值小于第一设定值，则将第二数量确定为有效径数量。
其中，第一设定值为小于1的正数。
在一个实施例中，计算功率大于噪声门限的N个径的总功率：
Figure PCTCN2020111850-appb-000001
其中，P i为第i个功率大于噪声门限的径的功率。计算大于噪声门限的径中功率最大的L(1≤L≤N)个径的总功率Q1，计算大于噪声门限的径中功率最大的L-1个径的总功率Q2，L从1开始取值，直到满足如下条件：
Figure PCTCN2020111850-appb-000002
且
Figure PCTCN2020111850-appb-000003
将L确定为有效径数量。
S140，根据所述有效径数量对所述初始信道估计进行选径，获得目标信道估计。
在一个实施例中，在根据噪声门限及信道估计窗内各个径的功率确定有效径数量之后，还包括如下步骤：返回执行根据接收的时域训练序列和 本地时域训练序列确定时域初始信道估计的步骤，直到获得有效径数量的次数达到设定次数。
在一个实施例中，连续执行C＝8次S110-S130，那么有效径个数分别为N0，N1，…，N8。
根据有效径数量对初始信道估计进行选径，获得目标信道估计的方式可以是：计算设定次数的有效径数量的均值，根据有效径数量的均值对初始信道估计进行选径，获得目标信道估计。
计算上述C次有效径个数的均值的公式为：
Figure PCTCN2020111850-appb-000004
其中，
Figure PCTCN2020111850-appb-000005
表示向上取整。
在一个实施例中，根据有效径数量的均值对初始信道估计进行选径，获得目标信道估计的方式可以是：若均值大于第二设定值，则将信道估计窗内功率最大的数量为均值的径确定为信号径，将初始信道估计中除信号径外的其他径确定为噪声径；若均值小于第二设定值，则将信道估计窗内功率最大的数量为第二设定值的径确定为信号径，将初始信道估计中除信号径外的其他径确定为噪声径。
其中，第二设定值S1可以设置为3。若均值大于第二设定值，则将信道估计窗内功率最大的S个径确定为信号径，将初始信道估计中除信号径外的其他径确定为噪声径，并置0。若均值小于第二设定值，则将信道估计窗内功率最大的S1个径确定为信号径，将初始信道估计中除信号径外的其他径确定为噪声径，并置0。
本公开实施例的技术方案，首先根据接收的时域训练序列和本地时域训练序列确定初始信道估计，并计算初始信道估计各个径的功率，然后在初始信道估计中设置设定长度的信道估计窗，并根据信道估计窗确定噪声门限，再然后根据噪声门限及信道估计窗内各个径的功率确定有效径数量，最后根据有效径数量对初始信道估计进行选径，获得目标信道估计。根据噪声门限及信道估计窗内各个径的功率确定有效径数量，可以提高信道估 计的准确性。
图2是本公开实施例提供的一种信道估计装置的结构框图。如图2所示，该装置包括：初始信道估计确定模块210，噪声门限确定模块220，有效径数量确定模块230和目标信道估计获取模块240。
初始信道估计确定模块210，设置为根据接收的时域训练序列和本地时域训练序列确定初始信道估计，并计算初始信道估计各个径的功率；
噪声门限确定模块220，设置为在初始信道估计中设置设定长度的信道估计窗，并根据信道估计窗内径的功率和/或所述信道估计窗外径的功率确定噪声门限；
有效径数量确定模块230，设置为根据噪声门限及信道估计窗内各个径的功率确定有效径数量；
目标信道估计获取模块240，设置为根据有效径数量对初始信道估计进行选径，获得目标信道估计。
在一个实施例中，初始信道估计确定模块210，还设置为：
对接收到的时域训练序列和本地时域训练序列进行相关，获得初始信道估计。
在一个实施例中，初始信道估计确定模块210，还设置为：
对接收的时域训练序列和本地时域训练序列分别进行傅里叶变换，获得第一频域训练序列和第二频域训练序列；
将第一频域训练序列和第二频域训练序列进行相乘，并对乘积进行傅里叶逆变换，获得初始信道估计。
在一个实施例中，噪声门限确定模块220，还设置为：
计算信道估计窗内功率最小的第一数量的径的功率均值，确定为噪声功率；
根据设定噪声门限因子对噪声功率进行加权，获得噪声门限。
在一个实施例中，噪声门限确定模块220，还设置为：
计算信道估计窗外各个径的功率均值，确定为噪声功率；
根据设定噪声门限因子对噪声功率进行加权，获得噪声门限。
在一个实施例中，有效径数量确定模块230，还设置为：
将信道估计窗内的各个径的功率与噪声门限分别进行比较，获取功率大于噪声门限的径的数量，确定为有效径数量。
在一个实施例中，有效径数量确定模块230，还设置为：
将信道估计窗内功率大于噪声门限的径确定为初始有效径，计算初始有效径的总功率，作为第一总功率；
计算初始有效径中功率最大的第二数量的径的总功率，作为第二总功率；
计算初始有效径中功率最大的第三数量的径的总功率，作为第三总功率；其中，第二数量比第三数量大1；
若第二总功率与第一总功率的比值大于或等于第一设定值，且第三总功率与第一总功率的比值小于第一设定值，则将第二数量确定为有效径数量。
在一个实施例中，在根据噪声门限及信道估计窗内各个径的功率确定有效径数量之后，还包括：
返回执行根据接收的时域训练序列和本地时域训练序列确定时域初始信道估计的步骤，直到获得有效径数量的次数达到设定次数；
在一个实施例中，目标信道估计获取模块240，还设置为：
计算设定次数的有效径数量的均值，根据有效径数量的均值对初始信道估计进行选径，获得目标信道估计。
在一个实施例中，目标信道估计获取模块240，还设置为：
若均值大于第二设定值，则将信道估计窗内功率最大的数量为均值的径确定为信号径，将初始信道估计中除信号径外的其他径确定为噪声径；
若均值小于第二设定值，则将信道估计窗内功率最大的数量为第二设定值的径确定为信号径，将初始信道估计中除信号径外的其他径确定为噪声径。
图3是本公开实施例提供的一种设备的结构示意图。如图3所示，本公开提供的设备，包括：处理器310以及存储器320。该设备中处理器310的数量可以是一个或者多个，图3中以一个处理器310为例。该设备中存储器320的数量可以是一个或者多个，图3中以一个存储器320为例。该设备的处理器310以及存储器320可以通过总线或者其他方式连接，图3中以通过总线连接为例。实施例中，该设备为接收端。其中，接收端可以为调度节点、基站或UE中的其中一个。
存储器320作为一种计算机可读存储介质，可设置为存储软件程序、计算机可执行程序以及模块，如本公开任意实施例的设备对应的程序指令/模块(例如，数据传输装置中的编码模块和第一发送模块)。存储器320可包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序；存储数据区可存储根据设备的使用所创建的数据等。此外，存储器320可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实例中，存储器320可进一步包括相对于处理器310远程设置的存储器，这些远程存储器可以通过网络连接至设备。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
上述提供的设备可设置为执行上述任意实施例提供的应用于信道估计方法，具备相应的功能和效果。
对应存储器320中存储的程序可以是本公开实施例所提供应用于信号处理方法对应的程序指令/模块，处理器310通过运行存储在存储器320中的软件程序、指令以及模块，从而执行计算机设备的一种或多种功能应用以及数据处理，即实现上述方法实施例中应用于信号处理方法。可以理解的是，上述设备为接收端时，可执行本公开任意实施例所提供的应用于 信号处理方法，且具备相应的功能和效果。其中，设备可以为基站或UE中的其中一个。
本公开实施例还提供一种包含计算机可执行指令的存储介质，计算机可执行指令在由计算机处理器执行时用于执行一种信号处理方法，该方法包括：根据接收的时域训练序列和本地时域训练序列确定初始信道估计，并计算所述初始信道估计各个径的功率；在所述初始信道估计中设置设定长度的信道估计窗，并根据所述信道估计窗内径的功率和/或所述信道估计窗外径的功率确定噪声门限；根据所述噪声门限及所述信道估计窗内各个径的功率确定有效径数量；根据所述有效径数量对所述初始信道估计进行选径，获得目标信道估计。
本领域内的技术人员应明白，术语用户设备涵盖任何适合类型的无线用户设备，例如移动电话、便携数据处理装置、便携网络浏览器或车载移动台。
一般来说，本公开的多种实施例可以在硬件或专用电路、软件、逻辑或其任何组合中实现。例如，一些方面可以被实现在硬件中，而其它方面可以被实现在可以被控制器、微处理器或其它计算装置执行的固件或软件中，尽管本公开不限于此。
本公开的实施例可以通过移动装置的数据处理器执行计算机程序指令来实现，例如在处理器实体中，或者通过硬件，或者通过软件和硬件的组合。计算机程序指令可以是汇编指令、指令集架构(Instruction Set Architecture，ISA)指令、机器指令、机器相关指令、微代码、固件指令、状态设置数据、或者以一种或多种编程语言的任意组合编写的源代码或目标代码。
本公开附图中的任何逻辑流程的框图可以表示程序步骤，或者可以表示相互连接的逻辑电路、模块和功能，或者可以表示程序步骤与逻辑电路、模块和功能的组合。计算机程序可以存储在存储器上。存储器可以具有任何适合于本地技术环境的类型并且可以使用任何适合的数据存储技术实 现，例如但不限于只读存储器(Read-Only Memory，ROM)、随机访问存储器(Random Access Memory，RAM)、光存储器装置和系统(数码多功能光碟(Digital Video Disc，DVD)或光盘(Compact Disk，CD))等。计算机可读介质可以包括非瞬时性存储介质。数据处理器可以是任何适合于本地技术环境的类型，例如但不限于通用计算机、专用计算机、微处理器、数字信号处理器(Digital Signal Processing，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、可编程逻辑器件(Field-Programmable Gate Array，FGPA)以及基于多核处理器架构的处理器。
通过示范性和非限制性的示例，上文已提供了对本公开的示范实施例的详细描述。但结合附图和权利要求来考虑，对以上实施例的多种修改和调整对本领域技术人员来说是显而易见的，但不偏离本发明的范围。因此，本发明的恰当范围将根据权利要求确定。

Claims (12)

  1. 一种信道估计方法，包括：
    根据接收的时域训练序列和本地时域训练序列确定初始信道估计，并计算所述初始信道估计各个径的功率；
    在所述初始信道估计中设置设定长度的信道估计窗，并根据所述信道估计窗内径的功率和/或所述信道估计窗外径的功率确定噪声门限；
    根据所述噪声门限及所述信道估计窗内各个径的功率确定有效径数量；
    根据所述有效径数量对所述初始信道估计进行选径，获得目标信道估计。
  2. 根据权利要求1所述的方法，其中，根据接收的时域训练序列和本地时域训练序列确定初始信道估计，包括：
    对接收到的时域训练序列和本地时域训练序列进行相关，获得初始信道估计。
  3. 根据权利要求1所述的方法，其中，根据接收的时域训练序列和本地时域训练序列确定初始信道估计，包括：
    对接收的时域训练序列和本地时域训练序列分别进行傅里叶变换，获得第一频域训练序列和第二频域训练序列；
    将所述第一频域训练序列和所述第二频域训练序列进行相乘，并对乘积进行傅里叶逆变换，获得初始信道估计。
  4. 根据权利要求1所述的方法，其中，根据所述信道估计窗内径的功率确定噪声门限，包括：
    计算所述信道估计窗内功率最小的第一数量的径的功率均值，确定为噪声功率；
    根据设定噪声门限因子对所述噪声功率进行加权，获得噪声门限。
  5. 根据权利要求1所述的方法，其中，根据所述信道估计窗外径的 功率确定噪声门限，包括：
    计算所述信道估计窗外各个径的功率均值，确定为噪声功率；
    根据设定噪声门限因子对所述噪声功率进行加权，获得噪声门限。
  6. 根据权利要求1所述的方法，其中，根据所述噪声门限及各个径的功率确定有效径数量，包括：
    将所述信道估计窗内的各个径的功率与所述噪声门限分别进行比较，获取功率大于所述噪声门限的径的数量，确定为有效径数量。
  7. 根据权利要求6所述的方法，其中，在获取功率大于所述噪声门限的径的数量之后，还包括：
    将所述信道估计窗内功率大于所述噪声门限的径确定为初始有效径，计算所述初始有效径的总功率，作为第一总功率；
    计算所述初始有效径中功率最大的第二数量的径的总功率，作为第二总功率；
    计算所述初始有效径中功率最大的第三数量的径的总功率，作为第三总功率；其中，第二数量比第三数量大1；
    若所述第二总功率与所述第一总功率的比值大于或等于第一设定值，且所述第三总功率与所述第一总功率的比值小于所述第一设定值，则将所述第二数量确定为有效径数量。
  8. 根据权利要求1所述的方法，其中，在根据所述噪声门限及所述信道估计窗内各个径的功率确定有效径数量之后，还包括：
    返回执行根据接收的时域训练序列和本地时域训练序列确定时域初始信道估计的步骤，直到获得有效径数量的次数达到设定次数；
    相应的，根据所述有效径数量对所述初始信道估计进行选径，获得目标信道估计，包括：
    计算设定次数的有效径数量的均值，根据所述有效径数量的均值对所述初始信道估计进行选径，获得目标信道估计。
  9. 根据权利要求8所述的方法，其中，根据所述有效径数量的均值对所述初始信道估计进行选径，获得目标信道估计，包括：
    若所述均值大于第二设定值，则将所述信道估计窗内功率最大的数量为所述均值的径确定为信号径，将所述初始信道估计中除所述信号径外的其他径确定为噪声径；
    若所述均值小于第二设定值，则将所述信道估计窗内功率最大的数量为所述第二设定值的径确定为信号径，将所述初始信道估计中除所述信号径外的其他径确定为噪声径。
  10. 一种信道估计装置，包括：
    初始信道估计确定模块，设置为根据接收的时域训练序列和本地时域训练序列确定初始信道估计，并计算所述初始信道估计各个径的功率；
    噪声门限确定模块，设置为在所述初始信道估计中设置设定长度的信道估计窗，并根据所述信道估计窗内径的功率和/或所述信道估计窗外径的功率确定噪声门限；
    有效径数量确定模块，设置为根据所述噪声门限及所述信道估计窗内各个径的功率确定有效径数量；
    目标信道估计获取模块，设置为根据所述有效径数量对所述初始信道估计进行选径，获得目标信道估计。
  11. 一种通信设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现如权利要求1至9中任一所述的信道估计方法。
  12. 一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现如权利要求1至9中任一所述的信道估计方法。
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