WO2019127930A1 - 一种半盲信道估计方法和装置 - Google Patents

一种半盲信道估计方法和装置 Download PDF

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WO2019127930A1
WO2019127930A1 PCT/CN2018/079728 CN2018079728W WO2019127930A1 WO 2019127930 A1 WO2019127930 A1 WO 2019127930A1 CN 2018079728 W CN2018079728 W CN 2018079728W WO 2019127930 A1 WO2019127930 A1 WO 2019127930A1
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data
training sequence
parameter matrix
channel
channel parameter
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PCT/CN2018/079728
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English (en)
French (fr)
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刘若鹏
季春霖
尤琳
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深圳超级数据链技术有限公司
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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
    • 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
    • H04L25/0238—Channel estimation using blind estimation
    • 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
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/0242—Channel estimation channel estimation algorithms using matrix methods

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  • the present invention relates to the field of communications, and in particular to a method and apparatus for estimating a semi-blind channel.
  • the data is encoded by the overlapping multiplexing coding method, which can greatly improve the transmission spectrum efficiency of the system.
  • the overlapping multiplexing system OvXDM (X stands for any domain, including: time domain T, spatial domain S, frequency domain F , code domain C or mixed domain H, etc., while using multi-antenna technology, can increase the transmission rate of the system.
  • the multi-antenna overlapping multiplexing system needs to perform channel estimation processing when the channel parameters are unknown, and the performance of the least squares channel estimation based on the training sequence needs to be improved.
  • the embodiment of the invention provides a method and a device for estimating a semi-blind channel, so as to at least solve the problem that the multi-antenna overlapping multiplexing system in the related art adopts a training sequence based least square channel estimation performance to be improved when the channel parameters are unknown. .
  • a semi-blind channel estimation method is provided, which is applied to a receiving end of a multi-antenna overlapping multiplexing system.
  • the method for estimating a semi-blind channel includes the following steps: Step S1: acquiring data received by the receiving end and containing the first training sequence; and step S2, performing minimum according to the data including the first training data and the known first training sequence Separating the channel estimation to obtain a channel parameter matrix; step S3, detecting the first training sequence according to the channel parameter matrix and the least squares detection algorithm, thereby obtaining estimation data; and step S4, using the estimation data as the second training sequence And replacing the first training sequence with the second training sequence, and performing the above step S2 - step S3 on the second training sequence until the channel parameter matrix obtained last time is the same as the channel parameter matrix obtained this time, stopping the loop , thereby estimating the channel parameter matrix.
  • the least square channel estimation is performed according to the data including the first training data and the known first training sequence, thereby obtaining the channel parameter matrix, including: determining the data including the first training data and a first relationship between the first training sequences, wherein the first training sequence on the data including the first training data is a training sequence containing noise, and the first relationship is:
  • R is the data containing the first training data
  • H is the channel parameter matrix
  • S is the first training sequence
  • N is the noise matrix
  • performing least square channel estimation based on data including first training data and a known first training sequence includes determining a channel parameter matrix according to the first relation and the least square channel estimation algorithm And identifying the number of times the channel parameter matrix is estimated by using the identifier bit, wherein the channel parameter matrix is:
  • the first training sequence is detected according to the channel parameter matrix and the least squares detection algorithm, so that the estimated data includes:
  • the estimated number of times of the estimated data is identified by the identifier bit, wherein the estimated data is:
  • the method for estimating a semi-blind channel further includes: determining, by using the second relationship, that the channel parameter matrix obtained last time is the same as the channel parameter matrix obtained this time, wherein the second relationship is:
  • the channel parameter matrix obtained for the k-1th time For the estimated data obtained for the k-1th time, The channel parameter matrix obtained for the kth time, The estimated data obtained for the kth time.
  • a semi-blind channel estimating apparatus is provided, which is applied to a receiving end of a multi-antenna overlapping multiplexing system.
  • the semi-blind channel estimating apparatus includes: an acquiring module, configured to acquire data that is received by the receiving end and includes the first training sequence; and an estimating module, configured to use the data that includes the first training data and the known first training sequence Performing a least square channel estimation to obtain a channel parameter matrix; the detecting module is configured to detect the first training sequence according to the channel parameter matrix and the least squares detection algorithm, thereby obtaining estimation data; and a loop module for estimating the data And used as the second training sequence, and replaces the first training sequence with the second training sequence, and performs the above step S2 - step S3 on the second training sequence until the channel parameter matrix obtained last time and the channel parameter obtained this time. If the matrices are the same, the loop is stopped and the channel parameter matrix is estimated.
  • the semi-blind channel estimation apparatus includes: a first determining module, configured to determine a first relationship between the data including the first training data and the first training sequence, wherein the first relationship is included
  • the first training sequence on the data of the training data is a training sequence containing noise, and the first relationship is:
  • R is the data containing the first training data
  • H is the channel parameter matrix
  • S is the first training sequence
  • N is the noise matrix
  • the estimating module includes: a second determining module, configured to perform least square channel estimation according to the data including the first training data and the known first training sequence, according to the first relationship and a least square channel estimation algorithm, determining a channel parameter matrix, and using an identification bit to identify an estimated number of channel parameter matrices, wherein the channel parameter matrix is:
  • the detecting module comprises: an identifying module, configured to identify the estimated number of times of the estimated data by using the identifier bit, wherein the estimated data is:
  • the loop module includes: a third determining module, configured to determine, by using the second relationship, that the channel parameter matrix obtained last time is the same as the channel parameter matrix obtained this time, wherein the second relationship is:
  • the channel parameter matrix obtained for the k-1th time For the estimated data obtained for the k-1th time, The channel parameter matrix obtained for the kth time, The estimated data obtained for the kth time.
  • the multi-antenna overlapping multiplexing system of the present invention employs a semi-blind channel estimation based on least squares.
  • it uses a minimum of training symbols to estimate and initialize channel coefficients using a least square channel estimation algorithm, and uses a blind channel.
  • the iterative loop algorithm sacrifices the minimum bandwidth to transmit useful information as accurately as possible.
  • it uses the estimated estimation data as much as possible, as a known training sequence, which can highlight the advantages of channel estimation based on training sequences.
  • the actual known training information is used to restore the originally transmitted information sequence, so that the random channel parameter matrix in the multi-antenna overlapping multiplexing system can be estimated, and the performance is better than the training sequence based least square channel estimation performance. .
  • FIG. 1 is a flow chart of an alternative semi-blind channel estimation method according to an embodiment of the present invention.
  • FIG 2 is an iterative relationship diagram between channel estimation and data detection in an optional semi-blind channel estimation, in accordance with an embodiment of the present invention
  • FIG. 3 is a block diagram of an alternative multi-antenna overlapping multiplexing system in accordance with an embodiment of the present invention.
  • FIG. 5 is a schematic diagram of an optional K-way waveform multiplexing according to an embodiment of the present invention.
  • FIG. 6 is a block diagram of a transmit signal of an optional overlapping time division multiplexing system in accordance with an embodiment of the present invention
  • FIG. 7 is a block diagram of a received signal of an optional overlapping time division multiplexing system in accordance with an embodiment of the present invention.
  • FIG. 10 is an optional overlapping time division multiplexing Trellis diagram in accordance with an embodiment of the present invention.
  • FIG. 11 is a block diagram of an alternative semi-blind channel estimation apparatus in accordance with an embodiment of the present invention.
  • a semi-blind channel estimation method is provided. It should be noted that the channel estimation method is applicable to the receiving end of the multi-antenna overlapping multiplexing system, and the multi-antenna overlapping multiplexing system includes a transmitting end and a receiving end.
  • the method for estimating a semi-blind channel includes: Step S101: acquiring data including a first training sequence received by a receiving end; and step S103, according to data and data including first training data.
  • the first training sequence is known to perform least square channel estimation, thereby obtaining a channel parameter matrix; in step S105, the first training sequence is detected according to the channel parameter matrix and the least squares detection algorithm, thereby obtaining estimation data; and step S107 is performed.
  • the multi-antenna overlapping multiplexing system of the present invention uses a least squares based semi-blind channel estimation. On the one hand, it estimates and initializes the channel with a least square channel estimation algorithm using few training symbols. Coefficient, using the repeated loop algorithm of blind channel, sacrifices the minimum bandwidth to transmit useful information as accurately as possible; on the other hand, it uses the estimated estimation data as much as possible, as a known training sequence, which can be highlighted based on training.
  • the advantage of the channel estimation of the sequence is to use the actually known training information to restore the originally transmitted information sequence, so that the random channel parameter matrix in the multi-antenna overlapping multiplexing system can be estimated, and the performance is better than that based on the training sequence. Least square channel estimation performance.
  • the multi-antenna overlapping multiplexing system is specifically as follows:
  • the source outputs a sequence of bits ⁇ 0, 1 ⁇ , and then performs a series of processes, including inputting a bit stream for modulation, OvXDM encoding, and then dividing the data into multiple streams according to spatial multiplexing.
  • the training sequence is added to the multi-channel data stream, and then sent by multiple transmitting antennas, and the data is received by multiple receiving antennas, and then the semi-blind channel estimation is performed. After the received training sequence is removed, the multi-channel data is correspondingly processed. Detection, decoding and demodulation, final decision output, note: If OvFDM encoding is used, IFFT needs to be added after the encoding. After the detection, FFT operation is needed. The same training sequence needs to be transformed accordingly to make the training sequence and the encoded The data is in the same domain. The above steps are described in detail below:
  • BPSK modulation As an example.
  • Data 1 is outputted by BPSK modulation as 1; data 0 is BPSK modulated. The output is -1.
  • modulation envelope waveforms are superimposed in the modulation domain to obtain a complex modulation envelope waveform in the modulation domain.
  • OvTDM encoding takes OvTDM encoding as an example.
  • the encoding is as follows:
  • the encoding process is as shown in FIG. 4, and the symbol superposition process is arranged in a parallelogram shape, as shown in FIG. 5, and the specific process includes the following steps:
  • the transmitted signal can be expressed as:
  • s(t) represents the encoded output data after OvTDM encoding.
  • the spatial multiplexing technology divides the data to be transmitted into several data streams and then transmits them on different antennas to improve the transmission rate of the system.
  • the common space-time multiplexing technology is the layered space-time code proposed by Bell Labs. .
  • the data will be divided into M sets of data streams for transmission after encoding.
  • the following uses two transmit antennas and OvTDM code as an example to illustrate the data offload process, as follows:
  • the training sequence is designed to satisfy the orthogonality principle of the training sequence, and to ensure that the channel estimation process has a lower computational complexity.
  • the following describes the training sequence used in the spatial multiplexing system by taking two transmitting antennas and two receiving antennas as an example, as follows:
  • the length of the training sequence is M1, where the length of the non-zero element is M1/2.
  • the form of the training sequence is [M1/2 non-zero elements, M1/2 zero elements; M1/2 Zero element, M1/2 non-zero elements] or [M1/2 zero elements, M1/2 non-zero elements; M1/2 non-zero elements, M1/2 zero elements], the structure can guarantee the training sequence Orthogonality, where a non-zero element refers to +1, -1.
  • the training sequence is placed in front of the two-way data of the spatial multiplexing output to form the following structure [M1/2 non-zero elements, M1/2 zero elements, S 1 ; M1/2 zero elements, M1/2 A non-zero element, S 2 ], transmits data corresponding to the structure via two transmit antennas (the training sequence is known at the receiving end).
  • the first 8 positions of the 2 channels are spatially multiplexed, and the first structure is taken as an example.
  • the training sequence corresponding to the two channels of data is [-1, 1, 1, 1, -1, 0, 0, 0. , 0; 0, 0, 0, 0, -1, 1, 1, -1], plus the space-multiplexed output data after the corresponding data is [-1,1,1,-1,0,0,0 ,0,0.0150-0.0150i,0.0410-0.0410i,0.0911-0.0911i,0.1475-0.1474i;0,0,0,0,-1,1,1,-1,0.0240-0.0240i,0.0641-0.0641i , 0.1197-0.1197i, 0.1719-0.1719i], the two channels of data are sent out by two transmitting antennas (note that the length of the training sequence is generally smaller than the length of the transmitted data, here for the purpose of simply describing the channel coding based on the training sequence, Take too much space to reuse the output data).
  • the transmitting end transmits the coded modulated signal through the antenna, and the signal is transmitted through the wireless channel.
  • the process of receiving the signal is shown in FIG. 6 and FIG. 7.
  • the data is received by multiple receiving antennas, and the receiving end first performs semi-blind channel estimation according to the training sequence, then removes the training sequence, detects other remaining transmission data by using a corresponding detection algorithm, and then decodes the detected output data to solve Tune, the final decision output bit stream.
  • the semi-blind estimation is a channel estimation method combining the blind estimation (the blind estimation mainly uses the potential structural features of the channel or the characteristics of the input signal to achieve channel estimation, which is not described in detail herein) and the advantages of the two methods based on the training sequence estimation. It utilizes few training sequences.
  • the least square channel estimation algorithm is used to perform corresponding estimation and initialization of channel coefficients, and then the training sequence is detected by using a least squares detection algorithm, and the output data is detected as a virtual training sequence. The channel is estimated again until a certain effect is achieved.
  • the semi-blind estimation process at the receiving end is as follows:
  • the reception is The relationship between the data R corresponding to the training sequence and the known training sequence S is
  • the resulting channel parameter estimation matrix is:
  • the corresponding data is estimated to be:
  • steps (2) and (3) Repeat steps (2) and (3) to cycle through channel estimation and data detection until the following stopping criteria are met: among them, The channel parameter matrix obtained for the k-1th time, For the estimated data obtained for the k-1th time, The channel parameter matrix obtained for the kth time, The estimated data obtained for the kth time.
  • the channel parameter matrix is estimated to be [-0.1002-0.1215i, -0.0225+0.0772i; 0.0805-0.2988i, 0.0424-0.3161i] for the first time, and then The least squares detection method is used to estimate
  • Common detection algorithms are: traditional detection algorithms, such as Maximum Likelihood (ML) detection. Zero Forcing (ZF) detection, Minimum Mean Square Error (MMSE) detection, serial interference cancellation + combination of traditional detection algorithms, etc., the combination of serial interference cancellation + traditional detection algorithms includes strings Successive Interference Cancellation-Zero Forcing (SCI-ZF) detection, Serial Interference Cancellation-Minimum Mean Square Error (SCI-MMSE) detection, etc.
  • ML Maximum Likelihood
  • ZF Zero Forcing
  • MMSE Minimum Mean Square Error
  • SCI-ZF Successive Interference Cancellation-Zero Forcing
  • SCI-MMSE Serial Interference Cancellation-Minimum Mean Square Error
  • the multi-channel detection output data is combined into one way. If two receiving antennas are assumed, the corresponding detection output is two-way data, and then the first-channel data is used as the corresponding data at the odd-numbered position of the output, and The second data is used as the corresponding data in the even position of the output.
  • FIG. 9 corresponds to its node state transition relationship diagram, and
  • Demodulation is the process of recovering information from a modulated signal carrying a message, which is the inverse of modulation.
  • BPSK demodulation the real part of the output signal value of the receiving end is intuitive (the modulation constellation mapping of the BPSK signal, the imaginary part is always 0).
  • the demodulated output is subjected to a corresponding decision output, such as a hard decision.
  • a corresponding decision output such as a hard decision.
  • the decision output is 1; when the output data is less than 0, the decision output is 0.
  • a semi-blind channel estimating apparatus is also provided.
  • a semi-blind channel estimation apparatus includes: an acquisition module, configured to acquire data that is received by a receiving end and includes a first training sequence; and an estimation module 1102, configured to include the first The data of the training data and the known first training sequence are subjected to least square channel estimation to obtain a channel parameter matrix; the detecting module 1103 is configured to detect the first training sequence according to the channel parameter matrix and the least squares detection algorithm, Thereby obtaining estimation data; a looping module 1104, configured to use the estimated data as the second training sequence, and replace the first training sequence with the second training sequence, and perform the above step S2-step S3 on the second training sequence, until When the channel parameter matrix obtained last time is the same as the channel parameter matrix obtained this time, the loop is stopped, and the channel parameter matrix is estimated.
  • a semi-blind channel estimating apparatus includes: a first determining module (not shown) for determining a first relationship between data including first training data and a first training sequence, wherein The first training sequence on the data including the first training data is a training sequence containing noise, and the first relationship is:
  • R is the data containing the first training data
  • H is the channel parameter matrix
  • S is the first training sequence
  • N is the noise matrix
  • the estimating module 1102 includes: a second determining module (not shown), and performing least square channel estimation based on the data including the first training data and the known first training sequence includes: Determining a channel parameter matrix according to the first relational and least square channel estimation algorithm, and identifying an estimated number of times of the channel parameter matrix by using the identifier bit, wherein the channel parameter matrix is:
  • the detecting module 1103 includes: an identifying module (not shown) for identifying the estimated number of times of the estimated data by using the identifier bit, wherein the estimated data is:
  • the loop module 1104 includes: a third determining module (not shown), configured to determine, by using the second relationship, that the channel parameter matrix obtained last time is the same as the channel parameter matrix obtained this time, wherein The second relationship is:
  • the channel parameter matrix obtained for the k-1th time For the estimated data obtained for the k-1th time, The channel parameter matrix obtained for the kth time, The estimated data obtained for the kth time.
  • the multi-antenna overlapping multiplexing system adopts a least squares based semi-blind channel estimation, on the one hand, it utilizes few training symbols, and uses a least square channel estimation algorithm.
  • To estimate and initialize the channel coefficients use the repeated loop algorithm of the blind channel to transmit the useful information as accurately as possible by sacrificing the minimum bandwidth; on the other hand, it uses the estimated estimation data as much as possible as the known training sequence.
  • the advantage of the channel estimation based on the training sequence can be highlighted, and the originally transmitted information sequence can be restored by using the actually known training information, so that the random channel parameter matrix in the multi-antenna overlapping multiplexing system can be estimated, and the performance is better.
  • the least squares channel estimation performance based on the training sequence is employed.

Abstract

本发明公开了一种半盲信道估计方法和装置，该半盲信道估计方法包括：步骤S1，获取接收端接收到的包含有第一训练序列的数据；步骤S2，根据包含有第一训练数据的数据和已知的第一训练序列进行最小二乘信道估计，从而得到信道参数矩阵；步骤S3，根据信道参数矩阵和最小二乘检测算法，对第一训练序列进行检测，从而得到估计数据；步骤S4，将估计数据用作第二训练序列，以及将第一训练序列替换为第二训练序列，并对第二训练序列循环执行上述步骤S2-步骤S3，直至上次得到的信道参数矩阵和本次得到的信道参数矩阵相同，则停止循环，从而估计出信道参数矩阵。本发明通过上述技术方案，能够估计出多天线重叠复用系统中随机的信道参数矩阵，并且性能优于采用基于训练序列的最小二乘信道估计性能。

Description

一种半盲信道估计方法和装置 技术领域
本发明涉及通信领域，具体来说，涉及一种半盲信道估计方法和装置。
背景技术
在通信系统中采用重叠复用编码方式进行数据编码，能够大幅度提高系统的传输频谱效率，而目前重叠复用系统OvXDM(X代表任何域，包括：时间域T，空间域S，频率域F，码分域C或混合域H等)，同时采用多天线技术，能够提高系统的传输速率。多天线重叠复用系统在信道参数未知时，需要进行信道估计处理，而采用基于训练序列的最小二乘信道估计性能有待提升。
针对相关技术中的问题，目前尚未提出有效的解决方案。
发明内容
本发明实施例提供了一种半盲信道估计方法和装置，以至少解决相关技术中多天线重叠复用系统在信道参数未知情况下，采用基于训练序列的最小二乘信道估计性能有待提升的问题。
根据本发明实施例的一个方面，提供了一种半盲信道估计方法，该信道估计方法应用于多天线重叠复用系统的接收端。
该半盲信道估计方法,方法包括：步骤S1，获取接收端接收到的包含有第一训练序列的数据；步骤S2，根据包含有第一训练数据的数据和已知的第一训练序列进行最小二乘信道估计，从而得到信道参数矩阵；步骤S3，根据信道参数矩阵和最小二乘检测算法，对第一训练序列进行检测，从而得到估计数据；步骤S4，将估计数据用作第二训练序列，以及将第一训练序列替换为第二训练序列，并对第二训练序列循环执行上述步骤S2-步骤S3，直至上次得到的信道参数矩阵和本次得到的信道参数矩阵相同，则停 止循环，从而估计出信道参数矩阵。
根据本发明的一个实施例，根据包含有第一训练数据的数据和已知的第一训练序列进行最小二乘信道估计，从而得到信道参数矩阵之前包括：确定包含有第一训练数据的数据和第一训练序列之间的第一关系式，其中，包含有第一训练数据的数据上的第一训练序列为含有噪声的训练序列，第一关系式为：
R＝HS+N
其中，R为包含有第一训练数据的数据，H为信道参数矩阵，S为第一训练序列，N为噪声矩阵。
根据本发明的一个实施例，根据包含有第一训练数据的数据和已知的第一训练序列进行最小二乘信道估计包括：根据第一关系式和最小二乘信道估计算法，确定信道参数矩阵，以及利用标识位标识信道参数矩阵的估计次数，其中，信道参数矩阵为：
Figure PCTCN2018079728-appb-000001
其中，
Figure PCTCN2018079728-appb-000002
为
Figure PCTCN2018079728-appb-000003
的逆运算，
Figure PCTCN2018079728-appb-000004
为第k-1次估计的训练序列，并且在第一次估计时，
Figure PCTCN2018079728-appb-000005
以及
Figure PCTCN2018079728-appb-000006
为第k次估计出的信道参数矩阵。
根据本发明的一个实施例，根据信道参数矩阵和最小二乘检测算法，对第一训练序列进行检测，从而得到估计数据包括：
利用标识位标识估计数据的估计次数，其中，估计数据为：
Figure PCTCN2018079728-appb-000007
其中，
Figure PCTCN2018079728-appb-000008
为
Figure PCTCN2018079728-appb-000009
的共轭转置运算，
Figure PCTCN2018079728-appb-000010
为
Figure PCTCN2018079728-appb-000011
的逆运算，
Figure PCTCN2018079728-appb-000012
为第k次的估计的训练数据。
根据本发明的一个实施例，半盲信道估计方法还包括：通过第二关系式确定上次得到的信道参数矩阵和本次得到的信道参数矩阵相同，其中，第二关系式为：
Figure PCTCN2018079728-appb-000013
其中，
Figure PCTCN2018079728-appb-000014
为第k-1次得到的信道参数矩阵，
Figure PCTCN2018079728-appb-000015
为第k-1次得到的估计数据，
Figure PCTCN2018079728-appb-000016
为第k次得到的信道参数矩阵，
Figure PCTCN2018079728-appb-000017
为第k次得到的估计数据。
根据本发明的另一方面，提供了一种半盲信道估计装置，该估计装置应用于多天线重叠复用系统的接收端，。
该半盲信道估计装置包括：获取模块，用于获取接收端接收到的包含有第一训练序列的数据；估计模块，用于根据包含有第一训练数据的数据和已知的第一训练序列进行最小二乘信道估计，从而得到信道参数矩阵；检测模块，用于根据信道参数矩阵和最小二乘检测算法，对第一训练序列进行检测，从而得到估计数据；循环模块，用于将估计数据用作第二训练序列，以及将第一训练序列替换为第二训练序列，并对第二训练序列循环执行上述步骤S2-步骤S3，直至上次得到的信道参数矩阵和本次得到的信道参数矩阵相同，则停止循环，从而估计出信道参数矩阵。
根据本发明的一个实施例，半盲信道估计装置包括：第一确定模块，用于确定包含有第一训练数据的数据和第一训练序列之间的第一关系式，其中，包含有第一训练数据的数据上的第一训练序列为含有噪声的训练序列，第一关系式为：
R＝HS+N
其中，R为包含有第一训练数据的数据，H为信道参数矩阵，S为第一训练序列，N为噪声矩阵。
根据本发明的一个实施例，估计模块包括：第二确定模块，用于根据包含有第一训练数据的数据和已知的第一训练序列进行最小二乘信道估计包括：根据第一关系式和最小二乘信道估计算法，确定信道参数矩阵，以及利用标识位标识信道参数矩阵的估计次数，其中，信道参数矩阵为：
Figure PCTCN2018079728-appb-000018
其中，
Figure PCTCN2018079728-appb-000019
为
Figure PCTCN2018079728-appb-000020
的逆运算，
Figure PCTCN2018079728-appb-000021
为第k-1次估计的训练序列，并且在第一次估计时，
Figure PCTCN2018079728-appb-000022
以及
Figure PCTCN2018079728-appb-000023
为第k次估计出的信道参数矩阵。
根据本发明的一个实施例，检测模块包括：标识模块，用于利用标识位标识估计数据的估计次数，其中，估计数据为：
Figure PCTCN2018079728-appb-000024
其中，
Figure PCTCN2018079728-appb-000025
为
Figure PCTCN2018079728-appb-000026
的共轭转置运算，
Figure PCTCN2018079728-appb-000027
为
Figure PCTCN2018079728-appb-000028
的逆运算，
Figure PCTCN2018079728-appb-000029
为第k次的估计的训练数据。
根据本发明的一个实施例，循环模块包括：第三确定模块，用于通过第二关系式确定上次得到的信道参数矩阵和本次得到的信道参数矩阵相同，其中，第二关系式为：
Figure PCTCN2018079728-appb-000030
其中，
Figure PCTCN2018079728-appb-000031
为第k-1次得到的信道参数矩阵，
Figure PCTCN2018079728-appb-000032
为第k-1次得到的估计数据，
Figure PCTCN2018079728-appb-000033
为第k次得到的信道参数矩阵，
Figure PCTCN2018079728-appb-000034
为第k次得到的估计数据。
本发明的有益技术效果在于：
本发明的多天线重叠复用系统通过采用基于最小二乘的半盲信道估计，一方面，它利用很少的训练符号，用最小二乘信道估计算法来估计和初始化信道系数，运用盲信道的反复循环算法，牺牲极小的带宽而尽可能准确的传输有用信息；另一方面，它尽可能的利用估算出的估算数据，作为已知的训练序列，可突出基于训练序列的信道估计的优势，尽可能利用实际已知的训练信息，还原最初传递的信息序列，从而能够估计出多天线重叠复用系统中随机的信道参数矩阵，并且性能优于采用基于训练序列的最小二乘信道估计性能。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是根据本发明实施例的一种可选的半盲信道估计方法的流程图；
图2是根据本发明实施例的一种可选的半盲信道估计中的信道估计与数据检测之间的迭代关系图；
图3是根据本发明实施例的一种可选的多天线重叠复用系统的框图；
图4是根据本发明实施例的一种可选的重叠时分复用系统的等效波形卷积编码模型；
图5是根据本发明实施例的一种可选的K路波形复用的示意图；
图6是根据本发明实施例的一种可选的重叠时分复用系统的发射信号框图；
图7是根据本发明实施例的一种可选的重叠时分复用系统的接收信号框图；
图8是根据本发明实施例的一种可选的重叠时分复用输入-输出关系图；
图9是根据本发明实施例的一种可选的节点状态转移图；
图10是根据本发明实施例的一种可选的重叠时分复用Trellis图；
图11是根据本发明实施例的一种可选的半盲信道估计装置的框图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员所获得的所有其他实施例，都属于本发明保护的范围。
需要说明的是，本发明的说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的本发明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。此外，术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。
根据本发明的实施例，提供了一种半盲信道估计方法。需要说明是的，该信道估计方法适用于多天线重叠复用系统的接收端，同时，该多天线重叠复用系统包括发送端和接收端。
如图1所示，根据本发明实施例的半盲信道估计方法包括：步骤S101，获取接收端接收到的包含有第一训练序列的数据；步骤S103，根据包含有第一训练数据的数据和已知的第一训练序列进行最小二乘信道估计，从而 得到信道参数矩阵；步骤S105，根据信道参数矩阵和最小二乘检测算法，对第一训练序列进行检测，从而得到估计数据；步骤S107，将估计数据用作第二训练序列，以及将第一训练序列替换为第二训练序列，并对第二训练序列循环执行上述步骤S103至步骤S105，直至上次得到的信道参数矩阵和本次得到的信道参数矩阵相同，则停止循环，从而估计出信道参数矩阵。
借助于上述技术方案，本发明的多天线重叠复用系统通过采用基于最小二乘的半盲信道估计，一方面，它利用很少的训练符号，用最小二乘信道估计算法来估计和初始化信道系数，运用盲信道的反复循环算法，牺牲极小的带宽而尽可能准确的传输有用信息；另一方面，它尽可能的利用估算出的估算数据，作为已知的训练序列，可突出基于训练序列的信道估计的优势，尽可能利用实际已知的训练信息，还原最初传递的信息序列，从而能够估计出多天线重叠复用系统中随机的信道参数矩阵，并且性能优于采用基于训练序列的最小二乘信道估计性能。
为了更好的描述本发明的技术方案下面通过具体的实施例进行详细的描述。
在多天线重叠复用系统中使用本发明的半盲信道估计方法，能够估计出多天线重叠复用系统中随机的信道参数矩阵，并且性能优于采用基于训练序列的最小二乘信道估计性能，其中，该多天线重叠复用系统具体如下所示：
如图3所示，信源输出比特{0，1}序列，再对其进行一系列流程处理，其中包含输入比特流进行调制、OvXDM编码，然后按照空间复用将数据分成多路数据流，在多路数据流中加上训练序列，再由多个发送天线发送出去，由多个接收天线接收数据，然后进行半盲信道估计，去掉接收到的训练序列后，接着对多路数据进行相应检测、译码和解调，最后判决输出，注意：如果采用OvFDM编码时，需要在编码后面加上IFFT，经过检测后需要进行FFT运算，同样训练序列需要进行相应变换使得训练序列与编码后的数据处于同一域内，下面对上述步骤进行详细的介绍：
发送过程
1、调制
将各种数字基带信号转换成适于信道传输的已调信号，常见的调制方式有BPSK、QPSK、16QAM等，下面以BPSK调制为例，数据1经BPSK调制输出为1；数据0经BPSK调制输出为-1。
2、OvXDM编码
该OvXDM编码的具体编码过程如下所示：
(1)根据设计参数在调制域内生成包络波形；
(2)根据重叠复用次数将所述包络波形在调制域内按预定的移位间隔进行移位，得到调制域内的各移位包络波形；
(3)将待调制序列中的符号与各自对应的移位包络波形相乘，得到调制域内的各调制包络波形；
(4)将各调制包络波形在调制域内进行叠加，得到调制域内的复调制包络波形。
下面以OvTDM编码为例，该编码具体如下所示：
编码过程如附图4所示，符号叠加过程呈平行四边形排列，如附图5所示，具体过程包括以下步骤：
(1)首先设计生成发送信号的包络波形h(t)；
(2)将(1)中所设计的包络波形h(t)经特定时间移位后，形成其它各个时刻发送信号包络波形h(t-i×ΔT)；
(3)将所要发送的符号x i与(2)生成的相应时刻的包络波形h(t-i×ΔT)相乘，得到各个时刻的待发送信号波形x ih(t-i×ΔT)；
(4)将(3)所形成的各个待发送波形进行x ih(t-i×ΔT)叠加，形成发射信号波形；
(5)发送的信号可以表示为：
Figure PCTCN2018079728-appb-000035
其中，s(t)表示经过OvTDM编码后的编码输出数据。
3、空间复用
空间复用技术是将要传送的数据分成几个数据流，然后在不同的天线上进行传输，从而提高系统的传输速率，其常见的空时复用技术是贝尔实 验室提出的分层空时码。
如果有M个发送天线，则数据在进行编码之后将被分成M组数据流进行传送。下面以两个发送天线，OvTDM编码为例，说明数据分流过程，具体如下所示：
假设经过OvTDM编码后数据长度为L，以前8位数据为例来说明数据分流过程：而前8位OvTDM编码输出为：{0.0150-0.0150i，0.0240-0.0240i，0.0410-0.0410i，0.0641-0.0641i，0.0911-0.0911i，0.1197-0.1197i，0.1475-0.1474i，0.1719-0.1719i}，将该8数据的奇数和偶数位置上的数据分离：S 1＝{0.0150-0.0150i，0.0410-0.0410i，0.0911-0.0911i，0.1475-0.1474i}；S 2＝{0.0240-0.0240i，0.0641-0.0641i，0.1197-0.1197i，0.1719-0.1719i}，其中S 1为调制输出数据奇数位置上对应的数据，而S 2为调制输出数据偶数位置上对应的数据。将S 1，S 2作为的两路输出，由两个发送天线发送出去。
4、加训练序列
训练序列的设计既要满足训练序列的正交性原理，又要保证实现信道估计过程有较低的计算复杂度。下面以2个发送天线，2个接收天线为例说明空间复用系统中采用的训练序列，具体如下：
假设训练序列长度为M1，其中非零元素长度为M1/2，对于两路数据而言，其训练序列的形式为[M1/2个非零元素，M1/2个零元素；M1/2个零元素，M1/2个非零元素]或者[M1/2个零元素，M1/2个非零元素；M1/2个非零元素，M1/2个零元素]，该结构能够保证训练序列的正交性，其中非零元素指的是+1，-1。然后，将该训练序列置于空间复用输出的两路数据前面，构成如下结构[M1/2个非零元素，M1/2个零元素，S 1；M1/2个零元素，M1/2个非零元素，S 2]，将该结构对应的数据经由2个发送天线发送出去(接收端已知训练序列)。
此外，以M1＝8，空间复用输出2路的前8位置数据，第一种结构为例，两路数据对应的训练序列为[-1，1，1，-1，0，0，0，0；0，0，0，0，-1，1，1，-1]，加上空时复用输出数据后对应的数据为[-1，1，1，-1，0，0，0，0，0.0150-0.0150i，0.0410-0.0410i，0.0911-0.0911i，0.1475-0.1474i；0，0，0，0，-1，1，1，-1，0.0240-0.0240i，0.0641-0.0641i，0.1197-0.1197i， 0.1719-0.1719i]，将该两路数据由两个发送天线发送出去(注意一般情况下训练序列长度小于传输数据长度，在此为了简单说明基于训练序列的信道编码，未取太多的空间复用输出数据)。
接收过程
发送端将编码调制后的信号经过天线发射出去，信号经过无线信道传输，如图6和图7示出了接收信号的过程。同时，由多个接收天线接收数据，接收端首先根据训练序列进行半盲信道估计，然后去掉训练序列，对其他剩余传输数据用相应的检测算法进行检测，然后对检测输出数据进行译码，解调，最终判决输出比特流。
5、基于最小二乘的半盲信道估计
半盲估计是结合盲估计(盲估计主要利用信道潜在的结构特征或者输入信号的特征达到信道估计的目的，在此不详细介绍)与基于训练序列估计这两种方法优点的信道估计方法。它利用很少的训练序列，本发明中采用最小二乘信道估计算法进行相应的估计、初始化信道系数，然后利用最小二乘检测算法对训练序列进行检测，检测输出数据作为一种虚拟训练序列进行信道再次估计，直至达到一定的效果为止。接收端半盲估计过程如下所示：
(1)初始化估计次数k，根据接收端接收到的数据中训练序列对应的数据(含噪声的训练序列)与已知的训练序列(不含噪声的训练序列)进行相应的最小二乘信道估计，
假设接收到的数据中训练序列对应的数据为R(含有噪声的训练序列)，已知的训练序列(不含噪声的训练序列)为S，信道参数矩阵为H，噪声矩阵为N，则接收训练序列对应的数据R与已知的训练序列S之间的关系为
R＝HS+N
最小二乘信道估计即为求得
Figure PCTCN2018079728-appb-000036
使得噪声方差最小，即为：
Figure PCTCN2018079728-appb-000037
得出信道参数估计矩阵为：
Figure PCTCN2018079728-appb-000038
(2)根据信道参数估计矩阵
Figure PCTCN2018079728-appb-000039
采用最小二乘检测算法进行相应的检测，即求得
Figure PCTCN2018079728-appb-000040
使得噪声方差最小：
Figure PCTCN2018079728-appb-000041
得到相应的数据估计为：
Figure PCTCN2018079728-appb-000042
其中，
Figure PCTCN2018079728-appb-000043
为
Figure PCTCN2018079728-appb-000044
的共轭转置运算，
Figure PCTCN2018079728-appb-000045
为
Figure PCTCN2018079728-appb-000046
的逆运算，
Figure PCTCN2018079728-appb-000047
为第k次的估计的训练数据。
(3)令k＝k+1，将估计出的数据作为一种虚拟的训练序列进行信道估计，即
Figure PCTCN2018079728-appb-000048
其中，
Figure PCTCN2018079728-appb-000049
为
Figure PCTCN2018079728-appb-000050
的逆运算，
Figure PCTCN2018079728-appb-000051
为第k-1次估计的训练序列，并且在第一次估计时，
Figure PCTCN2018079728-appb-000052
以及
Figure PCTCN2018079728-appb-000053
为第k次估计出的信道参数矩阵；
(4)重复第(2)步和第(3)步，循环进行信道估计和数据检测，直至满足以下的停止准则：
Figure PCTCN2018079728-appb-000054
其中，
Figure PCTCN2018079728-appb-000055
为第k-1次得到的信道参数矩阵，
Figure PCTCN2018079728-appb-000056
为第k-1次得到的估计数据，
Figure PCTCN2018079728-appb-000057
为第k次得到的信道参数矩阵，
Figure PCTCN2018079728-appb-000058
为第k次得到的估计数据。
接下来举例说明盲估计过程：
假设接收到的数据为2×L1的矩阵，以前面12列为例，接收到的数据为r＝[0.2849+0.1618i，-0.1829-0.4103i，-0.2963-0.1999i，0.0376+0.1996i，0.2577-0.3266i，-0.3208+0.0813i，0.0270+0.1280i，-0.3714-0.0813i，-0.1212-0.1790i，0.0549+0.0599i，0.0088+0.0143i，-0.1676+0.2476i；0.0282+0.5712i，0.2349-0.6366i，0.2396-0.8315i，-0.1979+0.3508i，0.1621+0.7298i，0.1593-0.7183i，0.5067-0.5767i，0.1649+0.5036i，0.1949+0.3310i，-0.0511-0.0522i，-0.3634+0.0459i，-0.2519-0.2717i]，根据已知的训练序列，第一次估计出信道参数矩阵为[-0.1002-0.1215i，-0.0225+0.0772i；0.0805-0.2988i，0.0424-0.3161i]，然后按照最小二乘检测方式进行估计训练序列，估计出对应的数据为[-1.8475+0.7422i，2.7404+0.4583i，2.1675-0.29i，-1.2446-0.5739i，0.1934+1.4716i， 0.8385-1.0174i，0.8385-1.0174i，-0.3034+0.7995i，0.3417-1.6896i；0.1073-0.1560i，-0.5025-0.3208i，0.5624+0.4046i，-0.0475+0.2398i，-2.2018-0.6323i，1.3642+1.0693i，2.3961+0.6035i，-2.0379+2.3051i]，将估计出来的训练序列作为虚拟的训练序列，再进行信道估计，得出信道参数估计矩阵为[-0.3149-0.4642i，0.0034+0.3558i；0.2612-0.6911i，0.0267-0.7277i]，如此反复进行信道估计和检测，直至最后估计出的信道参数矩阵趋于不变。
6、检测算法
去除接收数据中训练序列位置所对应的数据，按照估计出的信道参数矩阵对剩余传输的数据进行相应检测，常见的检测算法有：传统的检测算法，如最大似然(Maximum Likelihood，ML)检测、迫零(Zero Forcing，ZF)检测、最小均方误差(Minimum Mean Square Error，MMSE)检测、串行干扰抵消+传统检测算法的组合等，该串行干扰抵消+传统检测算法的组合包括串行干扰抵消+迫零(Successive Interference Cancellation-Zero Forcing，SCI-ZF)检测、串行干扰抵消+最小均方误差(Successive Interference Cancellation-Minimum Mean Square Error，SCI-MMSE)检测等。
此外，还将多路检测输出数据合并为一路，如假设有2个接收天线，对应的检测输出为2路数据，然后将其中的第一路数据作为输出的奇数位置上对应的数据，而将第二路数据作为输出的偶数位置上对应的数据。
7、译码
对检测输出，进行译码，一般译码实现算法包括map、log map、max log map、sova等，实现方法很多，如图8对应为K＝3时重叠时分复用系统输入-输出关系图，附图9对应其节点状态转移关系图，附图10对应为K＝3时，重叠复用系统Trellis图。
8、解调
解调是从携带消息的已调信号中恢复信息的过程，它是调制的逆过程。以BPSK解调为例，直观的就是接收端输出信号值的实部(BPSK信号的调制星座映射，虚部总是为0)。
9、判决输出
对解调的输出进行相应的判决输出，如，硬判决，当输出数据大于0，判决输出为1；当输出数据小于0，判决输出为0。
根据本发明的实施例，还提供了一种半盲信道估计装置。
如图11所示，根据本发明实施例的半盲信道估计装置包括：获取模块，用于获取接收端接收到的包含有第一训练序列的数据；估计模块1102，用于根据包含有第一训练数据的数据和已知的第一训练序列进行最小二乘信道估计，从而得到信道参数矩阵；检测模块1103，用于根据信道参数矩阵和最小二乘检测算法，对第一训练序列进行检测，从而得到估计数据；循环模块1104，用于将估计数据用作第二训练序列，以及将第一训练序列替换为第二训练序列，并对第二训练序列循环执行上述步骤S2-步骤S3，直至上次得到的信道参数矩阵和本次得到的信道参数矩阵相同，则停止循环，从而估计出信道参数矩阵。
根据本发明的一个实施例，半盲信道估计装置包括：第一确定模块(未示出)，用于确定包含有第一训练数据的数据和第一训练序列之间的第一关系式，其中，包含有第一训练数据的数据上的第一训练序列为含有噪声的训练序列，第一关系式为：
R＝HS+N
其中，R为包含有第一训练数据的数据，H为信道参数矩阵，S为第一训练序列，N为噪声矩阵。
根据本发明的一个实施例，估计模块1102包括：第二确定模块(未示出)，用于根据包含有第一训练数据的数据和已知的第一训练序列进行最小二乘信道估计包括：根据第一关系式和最小二乘信道估计算法，确定信道参数矩阵，以及利用标识位标识信道参数矩阵的估计次数，其中，信道参数矩阵为：
Figure PCTCN2018079728-appb-000059
其中，
Figure PCTCN2018079728-appb-000060
为
Figure PCTCN2018079728-appb-000061
的逆运算，
Figure PCTCN2018079728-appb-000062
为第k-1次估计的训练序列，并且在第一次估计时，
Figure PCTCN2018079728-appb-000063
以及
Figure PCTCN2018079728-appb-000064
为第k次估计出的信道参数矩阵。
根据本发明的一个实施例，检测模块1103包括：标识模块(未示出)，用于利用标识位标识估计数据的估计次数，其中，估计数据为：
Figure PCTCN2018079728-appb-000065
其中，
Figure PCTCN2018079728-appb-000066
为
Figure PCTCN2018079728-appb-000067
的共轭转置运算，
Figure PCTCN2018079728-appb-000068
为
Figure PCTCN2018079728-appb-000069
的逆运算，
Figure PCTCN2018079728-appb-000070
为第k次的估计的训练数据。
根据本发明的一个实施例，循环模块1104包括：第三确定模块(未示出)，用于通过第二关系式确定上次得到的信道参数矩阵和本次得到的信道参数矩阵相同，其中，第二关系式为：
Figure PCTCN2018079728-appb-000071
其中，
Figure PCTCN2018079728-appb-000072
为第k-1次得到的信道参数矩阵，
Figure PCTCN2018079728-appb-000073
为第k-1次得到的估计数据，
Figure PCTCN2018079728-appb-000074
为第k次得到的信道参数矩阵，
Figure PCTCN2018079728-appb-000075
为第k次得到的估计数据。
综上所述，借助于本发明的上述技术方案，多天线重叠复用系统通过采用基于最小二乘的半盲信道估计，一方面，它利用很少的训练符号，用最小二乘信道估计算法来估计和初始化信道系数，运用盲信道的反复循环算法，牺牲极小的带宽而尽可能准确的传输有用信息；另一方面，它尽可能的利用估算出的估算数据，作为已知的训练序列，可突出基于训练序列的信道估计的优势，尽可能利用实际已知的训练信息，还原最初传递的信息序列，从而能够估计出多天线重叠复用系统中随机的信道参数矩阵，并且性能优于采用基于训练序列的最小二乘信道估计性能。
以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (10)

  1. 一种半盲信道估计方法，所述估计方法应用于多天线重叠复用系统的接收端，其特征在于，所述半盲信道估计方法包括：
    步骤S1，获取所述接收端接收到的包含有第一训练序列的数据；
    步骤S2，根据所述包含有第一训练数据的数据和已知的第一训练序列进行最小二乘信道估计，从而得到信道参数矩阵；
    步骤S3，根据所述信道参数矩阵和最小二乘检测算法，对所述第一训练序列进行检测，从而得到估计数据；
    步骤S4，将所述所述估计数据用作第二训练序列，以及将所述第一训练序列替换为所述第二训练序列，并对所述第二训练序列循环执行上述步骤S2-步骤S3，直至上次得到的所述信道参数矩阵和本次得到的所述信道参数矩阵相同，则停止循环，从而估计出信道参数矩阵。
  2. 根据权利要求1所述的半盲信道估计算法，其特征在于，根据所述包含有第一训练数据的数据和所述已知的第一训练序列进行最小二乘信道估计，从而得到信道参数矩阵之前包括：
    确定所述包含有第一训练数据的数据和所述第一训练序列之间的第一关系式，其中，所述包含有第一训练数据的数据上的第一训练序列为含有噪声的训练序列，所述第一关系式为：
    R＝HS+N
    其中，R为所述包含有第一训练数据的数据，H为所述信道参数矩阵，S为所述第一训练序列，N为噪声矩阵。
  3. 根据权利要求2所述的半盲信道估计算法，其特征在于，根据所述包含有第一训练数据的数据和已知的第一训练序列进行最小二乘信道估计包括：
    根据所述第一关系式和最小二乘信道估计算法，确定所述信道参数矩阵，以及利用标识位标识所述信道参数矩阵的估计次数，其中，所述信道参数矩阵为：
    Figure PCTCN2018079728-appb-100001
    其中，
    Figure PCTCN2018079728-appb-100002
    为
    Figure PCTCN2018079728-appb-100003
    的逆运算，
    Figure PCTCN2018079728-appb-100004
    为第k-1次估计的训练序列，并且在第一次估计时，
    Figure PCTCN2018079728-appb-100005
    以及
    Figure PCTCN2018079728-appb-100006
    为第k次估计出的所述信道参数矩阵。
  4. 根据权利要求3所述的半盲信道估计算法，其特征在于，根据所述信道参数矩阵和最小二乘检测算法，对所述第一训练序列进行检测，从而得到估计数据包括：
    利用标识位标识所述估计数据的估计次数，其中，所述估计数据为：
    Figure PCTCN2018079728-appb-100007
    其中，
    Figure PCTCN2018079728-appb-100008
    为
    Figure PCTCN2018079728-appb-100009
    的共轭转置运算，
    Figure PCTCN2018079728-appb-100010
    为
    Figure PCTCN2018079728-appb-100011
    的逆运算，
    Figure PCTCN2018079728-appb-100012
    为第k次的估计的训练数据。
  5. 根据权利要求4所述的半盲信道估计算法，其特征在于，所述半盲信道估计方法还包括：
    通过第二关系式确定上次得到的所述信道参数矩阵和本次得到的信道参数矩阵相同，其中，所述第二关系式为：
    Figure PCTCN2018079728-appb-100013
    其中，
    Figure PCTCN2018079728-appb-100014
    为所述第k-1次得到的所述信道参数矩阵，
    Figure PCTCN2018079728-appb-100015
    为所述第k-1次得到的估计数据，
    Figure PCTCN2018079728-appb-100016
    为所述第k次得到的所述信道参数矩阵，
    Figure PCTCN2018079728-appb-100017
    为所述第k次得到的估计数据。
  6. 一种半盲信道估计装置，其特征在于，所述估计装置应用于多天线重叠复用系统的接收端，其特征在于，所述半盲信道估计装置包括：
    获取模块，用于获取所述接收端接收到的包含有第一训练序列的数据；
    估计模块，用于根据所述包含有第一训练数据的数据和已知的第一训练序列进行最小二乘信道估计，从而得到信道参数矩阵；
    检测模块，用于根据所述信道参数矩阵和最小二乘检测算法，对所述第一训练序列进行检测，从而得到估计数据；
    循环模块，用于将所述所述估计数据用作第二训练序列，以及将所述第一训练序列替换为所述第二训练序列，并对所述第二训练序列循环执行上述步骤S2-步骤S3，直至上次得到的所述信道参数矩阵和下次得到的所述信道参数矩阵相同，则停止循环，从而估计出信道参数矩阵。
  7. 根据权利要求6所述的半盲信道估计装置，其特征在于，所述半盲 信道估计装置包括：
    第一确定模块，用于确定所述包含有第一训练数据的数据和所述第一训练序列之间的第一关系式，其中，所述包含有第一训练数据的数据上的第一训练序列为含有噪声的训练序列，所述第一关系式为：
    R＝HS+N
    其中，R为所述包含有第一训练数据的数据，H为所述信道参数矩阵，S为所述第一训练序列，N为噪声矩阵。
  8. 根据权利要求7所述的半盲信道估计装置，其特征在于，所述估计模块包括：
    第二确定模块，用于根据所述包含有第一训练数据的数据和已知的第一训练序列进行最小二乘信道估计包括：
    根据所述第一关系式和最小二乘信道估计算法，确定所述信道参数矩阵，以及利用标识位标识所述信道参数矩阵的估计次数，其中，所述信道参数矩阵为：
    Figure PCTCN2018079728-appb-100018
    其中，
    Figure PCTCN2018079728-appb-100019
    为
    Figure PCTCN2018079728-appb-100020
    的逆运算，
    Figure PCTCN2018079728-appb-100021
    为第k-1次估计的训练序列，并且在第一次估计时，
    Figure PCTCN2018079728-appb-100022
    以及
    Figure PCTCN2018079728-appb-100023
    为第k次估计出的所述信道参数矩阵。
  9. 根据权利要求8所述的半盲信道估计装置，其特征在于，所述检测模块包括：
    标识模块，用于利用标识位标识所述估计数据的估计次数，其中，所述估计数据为：
    Figure PCTCN2018079728-appb-100024
    其中，
    Figure PCTCN2018079728-appb-100025
    为
    Figure PCTCN2018079728-appb-100026
    的共轭转置运算，
    Figure PCTCN2018079728-appb-100027
    为
    Figure PCTCN2018079728-appb-100028
    的逆运算，
    Figure PCTCN2018079728-appb-100029
    为第k次的估计的训练数据。
  10. 根据权利要求4所述的半盲信道估计装置，其特征在于，所述循环模块包括：
    第三确定模块，用于通过第二关系式确定上次得到的所述信道参数矩阵和本次得到的信道参数矩阵相同，其中，所述第二关系式为：
    Figure PCTCN2018079728-appb-100030
    其中，
    Figure PCTCN2018079728-appb-100031
    为所述第k-1次得到的所述信道参数矩阵，
    Figure PCTCN2018079728-appb-100032
    为所述第k-1次得到的估计数据，
    Figure PCTCN2018079728-appb-100033
    为所述第k次得到的所述信道参数矩阵，
    Figure PCTCN2018079728-appb-100034
    为所述第k次得到的估计数据。
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