CN108282423B - Noise reduction method and system for MIMO channel estimation - Google Patents

Noise reduction method and system for MIMO channel estimation Download PDF

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CN108282423B
CN108282423B CN201810005554.9A CN201810005554A CN108282423B CN 108282423 B CN108282423 B CN 108282423B CN 201810005554 A CN201810005554 A CN 201810005554A CN 108282423 B CN108282423 B CN 108282423B
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CN108282423A (en
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吴帅
蒋芜
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Shenzhen Itest Technology Co ltd
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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
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference

Abstract

The invention provides a noise reduction method and a noise reduction system for MIMO channel estimation, wherein the noise reduction method for the MIMO channel estimation comprises the following steps: step S1, channel estimation of long training sequence is carried out through receiving signal and local signal to obtain channel estimation matrix of long training sequence; step S2, processing each subcarrier respectively through weight calculation; and step S3, correcting each element in the channel estimation matrix according to the weight value. The invention can provide an effective noise reduction method for correcting the channel estimation matrix aiming at the MIMO channel estimation by balancing the influence of noise suppression and inter-stream leakage, thereby achieving the purpose of improving the receiving performance of the MIMO system.

Description

Noise reduction method and system for MIMO channel estimation
Technical Field
The invention relates to MIMO channel estimation based on an OFDM wireless system, in particular to a noise reduction method for MIMO channel estimation by using HT-LTF/VHT-LTF under an MIMO scene under an 802.11n/ac standard, and a noise reduction system adopting the noise reduction method for the MIMO channel estimation.
Background
In the MIMO transmission process, noise interference and inter-stream interference exist, and if the streams are approximately orthogonal, for example, the MIMO multi-stream isolation is good when the device to be tested is connected to a tester by a cable line, the channel estimation matrix H is similar to a unit matrix, only a small amount of inter-stream leakage and noise exist, and a very small value is represented on a non-main diagonal line of the channel estimation matrix H. In the 802.11n/ac standard, the demodulation performance of the MIMO channel estimation using HT-LTF/VHT-LTF in the MIMO scenario deteriorates due to the presence of inter-stream leakage and noise, and the inter-stream leakage needs to be correctly collected and the noise needs to be suppressed to improve the reception performance.
Disclosure of Invention
The technical problem to be solved by the present invention is to balance the influence of noise and inter-stream leakage, and further provide a noise reduction method for MIMO channel estimation based on a modified channel estimation matrix, aiming at improving the receiving performance of the MIMO channel, and relating to a noise reduction system adopting the noise reduction method for MIMO channel estimation.
In view of the above, the present invention provides a method for denoising MIMO channel estimation, comprising the following steps:
step S1, channel estimation of long training sequence is carried out through receiving signal and local signal to obtain channel estimation matrix of long training sequence;
step S2, processing each subcarrier respectively through weight calculation;
and step S3, correcting each element in the channel estimation matrix according to the weight value.
The invention is further improved in that in the step S1, the step S is carried out by
Figure BDA0001538507180000011
Representing data for each receive antenna on subcarrier k,
Figure BDA0001538507180000012
data on subcarrier k representing a long training sequence of a local signal, NtThe data on each transmitting antenna is the same, then the channel estimation matrix of the long training sequence on the subcarrier k is
Figure BDA0001538507180000013
Wherein, Xk HIs XkConjugation of (A) to (B), NtNumber of transmit antennas, N, for MIMO systemsrN is the number of Fourier transform points, and k is the number of subcarriers.
A further refinement of the invention is that said step S2 comprises the following sub-steps:
step S201, aiming at the channel estimation coefficient of the long training sequence from the transmitting antenna i to the receiving antenna j on the subcarrier k, calculating the corresponding power;
step S202, calculating the power normalization weight of the long training sequence channel estimation main diagonal coefficient;
step S203, calculating normalized power according to the power normalized weight;
and step S204, calculating the weight of each element in the normalized power.
The invention is further improved in that in the step S201, the formula is used
Figure BDA0001538507180000021
Calculating the corresponding power PkWherein, in the step (A),
Figure BDA0001538507180000022
the coefficients are estimated for the long training sequence channel from transmit antenna i to receive antenna j on subcarrier k,
Figure BDA0001538507180000023
to seek
Figure BDA0001538507180000024
The modulus value of (a).
The invention is further improved in that in the step S202, the formula is used
Figure BDA0001538507180000025
i＝1,2,…,min(Nt,Nr) Calculating the power normalization weight of the main diagonal coefficient
Figure BDA0001538507180000026
To seek
Figure BDA0001538507180000027
Is measured.
The invention is further improved in that in the step S203, the formula is used
Figure BDA0001538507180000028
Calculating the normalized power PSk。
The invention is further improved in that in the step S204, the formula is used
Figure BDA0001538507180000029
Calculating normalized power PSkEach element of
Figure BDA00015385071800000210
Weight of (2)
Figure BDA00015385071800000211
Hair brushIn a further improvement, in the step S3, the weight is calculated according to the weight
Figure BDA00015385071800000212
Modifying channel estimation matrices
Figure BDA00015385071800000213
Each element of
Figure BDA00015385071800000214
In a further improvement of the present invention, in the step S3, the channel estimation matrix is corrected
Figure BDA00015385071800000215
When, if
Figure BDA00015385071800000216
Then
Figure BDA00015385071800000217
Otherwise
Figure BDA00015385071800000218
The change is not changed; wherein, the threshold is a preset threshold.
The invention also provides a noise reduction system for MIMO channel estimation, which adopts the noise reduction method for MIMO channel estimation and comprises the following steps:
the long training sequence channel estimation module is used for performing channel estimation on a long training sequence through a received signal and a local signal to obtain a channel estimation matrix of the long training sequence;
the weight calculation module is connected with the long training sequence channel estimation module and is used for respectively processing each subcarrier through weight calculation;
and the channel estimation correction module is connected with the weight calculation module and corrects each element in the channel estimation matrix according to the weight.
Compared with the prior art, the invention has the beneficial effects that: the method comprises the steps of firstly carrying out long training sequence channel estimation by using a received signal and a local signal to obtain a channel estimation matrix, then calculating the weight of each element in the channel estimation matrix, and finally correcting each element in the channel estimation matrix according to the calculated weight, so that the invention can provide an effective noise reduction method for correcting the channel estimation matrix aiming at MIMO channel estimation by balancing the influence of noise suppression and inter-stream leakage, thereby achieving the purpose of improving the receiving performance of the MIMO system.
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FIG. 1 is a schematic workflow diagram of one embodiment of the present invention;
fig. 2 is a schematic system structure according to an embodiment of the present invention.
Detailed Description
Preferred embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.
WLANs of the IEEE802.11 standard offer high-rate, high-quality broadband service applications, where the core technologies are OFDM technology and MIMO technology. 802.11n supports 4 antenna stream OFDM-MIMO transmission and reception, while 802.11ac supports to 8 antenna stream MIMO-OFDM transmission and reception. The MIMO technology can generate a plurality of independent parallel channels in space and simultaneously transmit a plurality of data streams, thereby effectively increasing the transmission efficiency of the system.
In practical situations, the state information (CSI) of the channel is usually unknown, and must be estimated from the received signal, and if the channel estimation is not ideal, the performance of the system will be drastically degraded, so accurate channel estimation is the key to ensure the transmission quality of the MIMO-OFDM system and exert its superiority.
The channel estimation algorithm of the MIMO-OFDM system has a greater complexity than that of the single antenna system because the received signal is a superimposed signal of a plurality of transmitted signals, which are transmitted from a plurality of transmitting antennas simultaneously and arrive at any receiving antenna almost synchronously. Therefore, to correctly identify multiple transmission signals from a single superimposed signal, a channel estimation algorithm is required to estimate the channel characteristics of multiple parallel channels from each transmission antenna to the same reception antenna.
Assuming MIMO system transmissionThe number of antennas is NtThe number of receiving antennas is Nr. Under flat fading channel, the channel fading coefficient from transmitting antenna i to receiving antenna j is hj,i(i＝1,2,…,Nt；j＝1,2,…,Nr) Representing a vector of input signals of
Figure BDA0001538507180000031
The noise vector is
Figure BDA0001538507180000032
Vector of output signals
Figure BDA0001538507180000041
Then y (t) ═ hx (t) + η (t), where
Figure BDA0001538507180000042
Figure BDA0001538507180000043
The basic of the channel estimation algorithm based on the training symbols is the LS algorithm, the WLAN of IEEE802.11 standard uses the long training sequence to estimate the transmission channel H, and since the subcarriers used by the long training sequence in the preamble to transmit data are all pilot frequencies and the channel can be assumed to be unchanged in one data frame, the channel frequency response value on each subcarrier can be quickly obtained by only using the LS algorithm.
The receive model in the frequency domain may be denoted as Yn＝HnXn+VnN is 1,2, …, L, wherein YnFor received frequency domain signal values, HnFor frequency domain impulse response, VnIs the corresponding frequency domain white gaussian noise, and L is the number of frequency domain subcarriers. In case of a long training sequence, LS estimation of the channel can be derived,
Figure BDA0001538507180000044
h can be used under the premise that the channel response in a data frame is basically unchangedLSEqualization is performed, and finally correct reception of data is completed.
Based on the MIMO system is applied to the test instrument, under the premise that the equipment to be tested is connected with the test instrument by a cable, the isolation between MIMO multi-streams is good. Under the premise, the embodiment aims to provide a noise reduction method for MIMO channel estimation, and the receiving performance of the channel is improved by balancing the influence of noise and inter-stream leakage.
In contrast, as shown in fig. 1, this example provides a method for denoising MIMO channel estimation, including the following steps:
step S1, channel estimation of long training sequence is carried out through receiving signal and local signal to obtain channel estimation matrix of long training sequence;
step S2, processing each subcarrier respectively through weight calculation;
and step S3, correcting each element in the channel estimation matrix according to the weight value.
Suppose the number of transmitting antennas of the MIMO system is NtThe number of receiving antennas is Nr. Under flat fading channel, the channel fading coefficient from transmitting antenna i to receiving antenna j is hj,i(i＝1,2,…,Nt；j＝1,2,…,Nr) Representing a vector of input signals of
Figure BDA0001538507180000045
The noise vector is
Figure BDA0001538507180000046
Vector of output signals
Figure BDA0001538507180000047
Then y (t) ═ hx (t) + η (t), where
Figure BDA0001538507180000048
Figure BDA0001538507180000049
This example selects the output signal yj(t),j＝1,2,…,NrFor the received sequence corresponding to the long training sequence on the receiving antenna j, the Fourier transform is performed to the frequency domain Yj,n＝DFT(yj(t)), where n is the number of Fourier transform points, n being determined by the bandwidth.
In step S1 of the present example, by
Figure BDA0001538507180000051
Representing data for each receive antenna on subcarrier k,
Figure BDA0001538507180000052
data on subcarrier k representing a long training sequence of a local signal, NtThe data on each transmitting antenna is the same, then the channel estimation matrix of the long training sequence on the subcarrier k is
Figure BDA0001538507180000053
Wherein, Xk HIs XkConjugation of (A) to (B), NtNumber of transmit antennas, N, for MIMO systemsrN is the number of Fourier transform points, and k is the number of subcarriers.
In this example, step S2 processes each subcarrier separately through weight calculation, wherein,
Figure BDA0001538507180000054
is the LS channel estimation coefficients for transmit antenna i to receive antenna j on subcarrier k,
Figure BDA0001538507180000055
is that
Figure BDA0001538507180000056
One component of the matrix, LS, the long training sequence, for an ideal MIMO test scenario, the channel response when j ≠ i also includes noise and inter-stream leakage, while only the current stream channel response and noise when j ≠ i.
Step S2 in this example includes the following substeps:
step S201, aiming at the channel estimation coefficient of the long training sequence from the transmitting antenna i to the receiving antenna j on the subcarrier k, calculating the corresponding power;
step S202, calculating the power normalization weight of the long training sequence channel estimation main diagonal coefficient;
step S203, calculating the normalized power of the main diagonal line according to the power normalized weight;
and step S204, calculating the weight of each element in the normalized power.
In step S201 in this example, the formula is used
Figure BDA0001538507180000057
Calculating the corresponding power PkWherein, in the step (A),
Figure BDA0001538507180000058
the coefficients are estimated for the long training sequence channel from transmit antenna i to receive antenna j on subcarrier k,
Figure BDA0001538507180000059
to seek
Figure BDA00015385071800000510
The modulus value of (a).
In step S202 in this example, the formula is shown
Figure BDA00015385071800000511
i＝1,2,…,min(Nt,Nr) Calculating the power normalization weight
Figure BDA00015385071800000512
To seek
Figure BDA00015385071800000513
Normalized weights are only the values on the main diagonal.
In step S203 described in this example, the formula is used
Figure BDA00015385071800000514
Calculating the normalized power PSk。
In step S204 described in this example, the formula is used
Figure BDA00015385071800000515
Calculating normalized power PSkEach element of
Figure BDA00015385071800000516
Weight of (2)
Figure BDA00015385071800000517
In step S3 in this example, according to the weight
Figure BDA0001538507180000061
Modifying channel estimation matrices
Figure BDA0001538507180000062
Each element of
Figure BDA0001538507180000063
Specifically, in the step S3, the channel estimation matrix is corrected
Figure BDA0001538507180000064
When, if
Figure BDA0001538507180000065
Figure BDA0001538507180000066
Then
Figure BDA0001538507180000067
Otherwise
Figure BDA0001538507180000068
The change is not changed; the threshold is a preset threshold, and generally, the preset threshold is set to be-30 dB by default, and may be set to be other values according to actual situations.
And the subsequent analysis data after noise reduction can utilize the corrected channel estimation matrix for each subcarrier
Figure BDA0001538507180000069
Equalization is performed, and finally correct reception of data is completed.
As shown in fig. 2, this example further provides a noise reduction system for MIMO channel estimation, which employs the above-mentioned noise reduction method for MIMO channel estimation, and includes:
the long training sequence channel estimation module is used for performing channel estimation on a long training sequence through a received signal and a local signal to obtain a channel estimation matrix of the long training sequence;
the weight calculation module is connected with the long training sequence channel estimation module and is used for respectively processing each subcarrier through weight calculation;
and the channel estimation correction module is connected with the weight calculation module and corrects each element in the channel estimation matrix according to the weight.
In this embodiment, first, the long training sequence channel estimation module performs long training sequence channel estimation on a received signal and a local signal to obtain a channel estimation matrix H, then, the weight calculation module calculates a weight of each element in the channel estimation matrix H, and finally, according to the calculated weight, each element in the channel estimation matrix H is modified to obtain a modified channel estimation matrix for use in subsequent data symbol equalization.
In summary, the embodiment can provide an effective noise reduction method for correcting a channel estimation matrix for MIMO channel estimation by balancing the influence of noise suppression and inter-stream leakage, thereby achieving the purpose of improving the receiving performance of the MIMO system.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.

Claims (3)

1. A method for denoising a MIMO channel estimate, comprising the steps of:
step S1, channel estimation of long training sequence is carried out through receiving signal and local signal to obtain channel estimation matrix of long training sequence;
step S2, processing each subcarrier respectively through weight calculation;
step S3, correcting each element in the channel estimation matrix according to the weight value, and correcting each element according to the weight value
Figure FDA0002797225910000011
Modifying channel estimation matrices
Figure FDA0002797225910000012
Each element of
Figure FDA0002797225910000013
In modifying the channel estimation matrix
Figure FDA0002797225910000014
When, if
Figure FDA0002797225910000015
Figure FDA0002797225910000016
Then
Figure FDA0002797225910000017
Otherwise
Figure FDA0002797225910000018
The change is not changed; wherein, threshold is a preset threshold;
the step S2 includes the following sub-steps:
step S201, aiming at the channel estimation coefficient of the long training sequence from the transmitting antenna i to the receiving antenna j on the subcarrier k, calculating the corresponding power, and calculating the corresponding power through a formula
Figure FDA0002797225910000019
ComputingCorresponding power PkWherein, in the step (A),
Figure FDA00027972259100000110
the coefficients are estimated for the long training sequence channel from transmit antenna i to receive antenna j on subcarrier k,
Figure FDA00027972259100000111
to seek
Figure FDA00027972259100000112
A modulus value of (d);
step S202, calculating the power normalization weight of the channel estimation main diagonal coefficient of the long training sequence, and passing through a formula
Figure FDA00027972259100000113
Calculating the power normalization weight of the main diagonal coefficient
Figure FDA00027972259100000114
Figure FDA00027972259100000115
To seek
Figure FDA00027972259100000116
The mean value of (a);
step S203, calculating the normalized power according to the power normalized weight, and calculating the normalized power according to a formula
Figure FDA00027972259100000117
Figure FDA00027972259100000118
Calculating the normalized power PS of the main diagonalk；
Step S204, calculating the weight of each element in the normalized power through a formula
Figure FDA00027972259100000119
Figure FDA00027972259100000120
Calculating normalized power PSkEach element of
Figure FDA00027972259100000121
Weight of (2)
Figure FDA00027972259100000122
2. The method for denoising of MIMO channel estimation according to claim 1, wherein the step S1 is performed by
Figure FDA00027972259100000123
Representing data for each receive antenna on subcarrier k,
Figure FDA00027972259100000124
Figure FDA00027972259100000125
data on subcarrier k representing a long training sequence of a local signal, NtThe data on each transmitting antenna is the same, then the channel estimation matrix of the long training sequence on the subcarrier k is
Figure FDA00027972259100000126
Wherein, Xk HIs XkConjugation of (A) to (B), NtNumber of transmit antennas, N, for MIMO systemsrN is the number of Fourier transform points, and k is the number of subcarriers.
3. A MIMO channel estimation noise reduction system, wherein the MIMO channel estimation noise reduction method according to claim 1 or 2 is adopted, and comprising:
the long training sequence channel estimation module is used for performing channel estimation on a long training sequence through a received signal and a local signal to obtain a channel estimation matrix of the long training sequence;
the weight calculation module is connected with the long training sequence channel estimation module and is used for respectively processing each subcarrier through weight calculation;
and the channel estimation correction module is connected with the weight calculation module and corrects each element in the channel estimation matrix according to the weight.
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