KR101669857B1 - Method for channel estimation and feedback in massive MIMO systems - Google Patents

Method for channel estimation and feedback in massive MIMO systems Download PDF

Info

Publication number
KR101669857B1
KR101669857B1 KR1020150079919A KR20150079919A KR101669857B1 KR 101669857 B1 KR101669857 B1 KR 101669857B1 KR 1020150079919 A KR1020150079919 A KR 1020150079919A KR 20150079919 A KR20150079919 A KR 20150079919A KR 101669857 B1 KR101669857 B1 KR 101669857B1
Authority
KR
South Korea
Prior art keywords
channel
downlink
vector
base station
terminal
Prior art date
Application number
KR1020150079919A
Other languages
Korean (ko)
Inventor
이용훈
김민현
이준호
길계태
Original Assignee
한국과학기술원
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 한국과학기술원 filed Critical 한국과학기술원
Priority to KR1020150079919A priority Critical patent/KR101669857B1/en
Application granted granted Critical
Publication of KR101669857B1 publication Critical patent/KR101669857B1/en

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0417—Feedback systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0658—Feedback reduction
    • H04B7/0663—Feedback reduction using vector or matrix manipulations

Abstract

The present invention relates to a method for channel estimation and feedback in a massive multi-input multi-output communication system and, more particularly, to a method for channel estimation and feedback in a massive multi-input multi-output communication system, which can perform more accurate channel estimation than existing channel estimation methods with less pilot overhead and feed back channel information with less feedback resources by using channel correlation information in a terminal. The method for channel estimation and feedback in a massive multi-input multi-output communication system according to a preferable embodiment of the present invention comprises the steps of: a) determining a pilot vector and an array response vector; b) acquiring the pilot vector and the array response vector and designing a downward oblique operator by using the channel correlation information, by the terminal; c) receiving the pilot vector, which is transmitted from a base station, as a downward received signal through a downlink channel, by the terminal; and d) estimating the downlink channel from the downward received signal by using the downward oblique operator, by the terminal. A pilot signal is determined to minimize the mean square error of the channel when estimating the channel by compressed sensing.

Description

[0001] The present invention relates to channel estimation and feedback in massive MIMO systems in a large-scale multi-input multi-output communication system,

The present invention relates to a channel estimation and feedback method in a large-scale multi-input multiple-output communication system, and more particularly, to a pilot estimation method and a feedback method using a channel estimation method, To a channel estimation and feedback method in a large-scale multi-input multi-output communication system.

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method and apparatus for estimating a channel in a massive multi-input multi-output (MIMO) system using compressed sensing (CS) Quot;). ≪ / RTI >

Recently, the massive MIMO system has attracted great attention as a core technology of the next generation mobile communication system. Generally, a massive MIMO cellular system considers a base station having a very large number of antennas and a smaller number of single antenna terminals, and a conventional MIMO system considering a number of base station antennas similar to the number of terminals through simple transmission / By contrast, a significant improvement in transmission capacity and energy efficiency can be expected. However, in order to obtain such an advantage, it is difficult to estimate channel information of a large dimension within a limited time. To solve this problem, most massive MIMO systems consider Time Division Duplex (TDD) operation. That is, the base station estimates the downlink channel using the point that the channel has reciprocity, and requires a pilot overhead proportional to the number of antennas of the terminal much smaller than the number of antennas of the base station.

However, most mobile communication systems currently support Frequency Division Duplex (FDD) operation. In the FDD environment, a downlink channel is estimated by a terminal and an overhead required for channel estimation Increases exponentially with the number of antennas. Therefore, in FDD massive MIMO system, there is a need for a method that can efficiently estimate a large dimension channel with limited pilot resources.

For example, SLG Nguyen and A. Ghrayeb, "Compressive sensing-based channel estimation for massive multi-user MIMO systems," in Proc. Proc. IEEE Wireless Commun. Netw. Conf., Shanghai, China, Apr. 2013.

However, the conventional technique is limited in its application range because it considers the TDD massive MIMO system.

S. L. G. Nguyen and A. Ghrayeb, " Compressive sensing-based channel estimation for massive multiuser MIMO systems, " in Proc. IEEE Wireless Commun. Netw. Conf., Shanghai, China, Apr. 2013.

The present invention provides a method for performing accurate channel estimation with a small pilot overhead using channel correlation information in a terminal in a large-scale multi-input multiple-output communication system.

 It is another object of the present invention to provide a method of feeding back channel information using a small amount of radio resources in a large-scale multi-input multi-output communication system.

Other objects of the present invention will become readily apparent from the following description of the embodiments.

According to an aspect of the present invention, there is provided a channel estimation method in a large-scale multi-input multi-output communication system, including a base station having a plurality of antennas and a terminal having a single antenna, In a multi-input multiple-output communication system, a) determining a pilot vector and an array response vector; b) obtaining a pilot vector and an array response vector from the terminal and designing a downward slanting operation matrix using channel correlation information; c) the pilot vector transmitted from the base station passes through a downlink channel and arrives at a terminal as a downlink received signal; d) estimating the downlink channel from the downlink received signal using the downlink diagonal computation matrix at the terminal, wherein when estimating a channel by compressing and sensing the channel, Can be minimized.

In the step a), the pilot vector may be determined based on a matrix obtained by QR decomposition of the Gaussian probability matrix and the array reaction vector.

In addition, in the step a), the number of array reaction vectors may be larger than the number of multiple paths of the downlink channel.

In the step d), the downlink diagonal calculation matrix may be used to estimate arrival angles of the downlink channel from the downlink received signal.

Also, in step d), all the arrival angles of the downlink channel may be estimated, and then the gain of the downlink channel for the estimated arrival angle may be estimated from the downlink received signal.

E) after the step d), the channel correlation information is fed back from the terminal to the base station; f) acquiring the pilot vector and the array response vector at a base station, and designing an upward slant operation matrix using the channel correlation information; g) the pilot vector transmitted from the UE arrives as an uplink received signal through the uplink channel to the base station; h) estimating the uplink channel from the uplink received signal using the uplink diagonal operation matrix at the base station, and the large-scale multi-input multi-output communication system may be a time division duplex system.

According to an aspect of the present invention, there is provided a channel feedback method in a large-scale multi-input multi-output communication system including a base station having a plurality of antennas and a terminal having a single antenna, In a multiple-input multiple-output communication system, a) determining an array response vector and a pilot vector; b) obtaining the pilot vector and the array response vector at the terminal and designing a downward slanting operation matrix using channel correlation information; c) the pilot vector transmitted from the base station passes through the downlink channel and reaches the terminal as a downlink received signal; d) estimating the downlink channel from the downlink received signal using the downward slanting matrix; and e) the channel correlation information and the downlink received signal are fed back from the terminal to the base station, wherein the large-scale multi-input multi-output communication system is a frequency division duplex system, , It is possible to minimize the mean square error of the channel.

In the step a), the pilot vector may be determined based on a matrix obtained by QR decomposition of the Gaussian probability matrix and the array reaction vector.

Also, in the step a), the number of array reaction vectors may be larger than the number of multiple paths of the downlink channel.

In the step d), the downlink diagonal calculation matrix may be used to estimate arrival angles of the downlink channel from the downlink received signal.

In addition, in step d), all the arrival angles of the downlink channel may be estimated, and then the downlink channel gain for the estimated arrival angle may be estimated from the downlink received signal.

As described above, the present invention provides a large-scale multi-input method for performing channel estimation more precisely when compared with existing channel estimation methods with less pilot overhead using channel correlation information at a terminal and feedbacking channel information with less propagation resources To provide a channel estimation and feedback method in a multi-output communication system.

In particular, in a large-scale multi-input multiple-output communication system using a frequency division duplex scheme, it is possible to feed back downlink channel information and uplink channel information using radio resources having less information.

1 is a diagram illustrating a large-scale multi-input multiple-output communication system environment including one base station having M antennas and K terminals having a single antenna.
2 is a diagram illustrating a received signal model according to an embodiment of the present invention.
3 is a diagram illustrating a channel estimation algorithm according to an embodiment of the present invention.
4 is a flowchart illustrating a method of estimating downlink and uplink channels in a time division duplex scheme using a channel estimation algorithm according to an embodiment of the present invention.
5 is a flowchart illustrating a method of feeding back a downlink channel in a frequency division duplex scheme using a channel estimation algorithm according to an embodiment of the present invention.
6 is a diagram illustrating a result of channel estimation in an environment using 40 pilots according to an embodiment of the present invention.
7 is a diagram illustrating a result of channel estimation in an environment with an SNR of 30 dB according to an embodiment of the present invention.

While the invention is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and will herein be described in detail. It should be understood, however, that the invention is not intended to be limited to the particular embodiments, but includes all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.

The singular expressions include plural expressions unless the context clearly dictates otherwise. In the present application, the terms "comprises" or "having" and the like are used to specify that there is a feature, a number, a step, an operation, an element, a component or a combination thereof described in the specification, But do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

As a result of considering the method of using the feedback resource with high accuracy of channel estimation in the massive MIMO system, the present applicant contemplated a method based on CS instead of the existing least squares method or minimum mean square error (MMSE) And the process of using channel correlation information is introduced to complete the present invention.

In general, existing methods for channel estimation have a least squares method or minimum mean square error method. The least squares method is an optimal estimation that can be performed when the terminal only has information on the pilot. The minimum mean square error method is an optimal estimation when statistical information on the channel is known. When applying the existing estimation methods to the massive MIMO system, the pilot overhead required to obtain sufficient channel estimation performance is very large because it is proportional to the number of antennas of the base station. That is, if the channel of the massive MIMO system is estimated using the limited pilot resource in the conventional method, deterioration of performance necessarily occurs. Therefore, a CS-based efficient channel estimation method should be applied to solve the problem of the conventional channel estimation method.

Hereinafter, a channel estimation and feedback method in a large-scale multi-input multiple-output communication system according to the present invention will be described in detail with reference to the accompanying drawings.

1 is a diagram illustrating a large-scale multi-input multiple-output communication system environment including one base station having M antennas and K terminals having a single antenna.

The present invention considers a massive MIMO cellular system in which a base station having M antennas communicates with K terminals having a single antenna in a cell.

However, since the system environment can be extended to a MIMO structure in which a terminal has multiple antennas with a multi input single output (MISO) structure according to each terminal, the present invention is not limited to MISO , The MISO structure is considered for convenience in this description. The base station transmits T pilot vectors

Figure 112015054477962-pat00001
And the transmission power of each pilot vector is constant P.

2 is a diagram illustrating a received signal model according to an embodiment of the present invention.

The received signal for the downlink channel estimation in each terminal is expressed by Equation (1). Here, since the same procedure is performed for each terminal, the index for the terminal is omitted. The received signal for downlink channel estimation may be referred to as a downlink received signal to distinguish it from other received signals.

Figure 112015054477962-pat00002

Each signal

Figure 112015054477962-pat00003
to be. The channel is modeled using a parametric channel model. Assuming that the base station is a uniform linear array (ULA), the channel is expressed by Equation (2).

Figure 112015054477962-pat00004

Where L is the number of channel paths,

Figure 112015054477962-pat00005
silver
Figure 112015054477962-pat00006
The gain value of the ith path,
Figure 112015054477962-pat00007
silver
Figure 112015054477962-pat00008
Angle-of-departure (AoD) (hereinafter referred to as " AoD "),
Figure 112015054477962-pat00009
Is an array response vector.

In order to establish the channel estimation problem, approximation of a channel to a grid (quantized angle) can be expressed as Equation (3).

Figure 112015054477962-pat00010

here

Figure 112015054477962-pat00011
,
Figure 112015054477962-pat00012
The
Figure 112015054477962-pat00013
To
Figure 112015054477962-pat00014
Angularly quantized angles,
Figure 112015054477962-pat00015
Is a vector representing the path gain for each AoD. Assuming that there is no quantization error, equation (1) is written as equation (4).

Figure 112015054477962-pat00016

From Equation (4)

Figure 112015054477962-pat00017
The problem of estimating is as follows.

Figure 112015054477962-pat00018

Obtained

Figure 112015054477962-pat00019
from
Figure 112015054477962-pat00020
Can be obtained.

3 is a diagram illustrating a channel estimation algorithm according to an embodiment of the present invention. Referring to Figure 3, the algorithm is repeated for each iteration step

Figure 112015054477962-pat00021
of
Figure 112015054477962-pat00022
And the selected index is the AoD of the estimated channel. Therefore, a channel gain corresponding to the index is estimated. This process
Figure 112015054477962-pat00023
If the value is set at a preset threshold value
Figure 112015054477962-pat00024
Repeat until it becomes smaller. here
Figure 112015054477962-pat00025
Is an oblique operator and is generally an element designed to improve estimation performance. In particular, for estimating the downlink channel
Figure 112015054477962-pat00026
, It can be called a downward slanting operation matrix for convenience.
Figure 112015054477962-pat00027
The problem of designing is described as Equation (6).

Figure 112015054477962-pat00028

here

Figure 112015054477962-pat00029
Is defined as an incidence probability,
Figure 112015054477962-pat00030
Is a unit-impulse function.
Figure 112015054477962-pat00031
The value is 1, and in other cases it is zero. The solution of Equation (6) can be obtained for each column as shown in Equation (7).

Figure 112015054477962-pat00032

here

Figure 112015054477962-pat00033
to be. In the present invention, an oblique operator is calculated from channel covariance information,
Figure 112015054477962-pat00034
. First,
Figure 112015054477962-pat00035
Is expressed by Equation (8).

Figure 112015054477962-pat00036

here,

Figure 112015054477962-pat00037
From this relationship, the oblique operator of the channel estimation algorithm proposed by the present invention can be obtained as shown in Equation (9).

Figure 112015054477962-pat00038

Figure 112015054477962-pat00039
Can be obtained as shown in Equation (10) using Equation (8).

Figure 112015054477962-pat00040

here

Figure 112015054477962-pat00041
Is an eigenvalue decomposition,
Figure 112015054477962-pat00042
Respectively
Figure 112015054477962-pat00043
Eigenvector and eigenvalue, respectively.

When using the channel estimation method derived above, the channel estimation performance is

Figure 112015054477962-pat00044
≪ / RTI > In other words,
Figure 112015054477962-pat00045
And
Figure 112015054477962-pat00046
The pilot signal < RTI ID = 0.0 >
Figure 112015054477962-pat00047
It is important to design well. In general, CS (compressed sensing)
Figure 112015054477962-pat00048
Is designed as a random matrix. However, recent studies have been made to suggest methods that can improve the estimation performance more than the probability matrix. Particularly, in the problem such as channel estimation, a design method that minimizes a mean square error (MSE) of a channel while considering transmission power should be considered. The problem of designing a pilot signal is as follows.

Figure 112015054477962-pat00049

here

Figure 112015054477962-pat00050
Can be obtained by QR decomposition of a Gaussian random matrix as a design target, and the total transmit power of the pilot signal is
Figure 112015054477962-pat00051
to be. In solving the problem, since the problem and the transmission power condition can not be considered together, the solution to Equation (11) is first obtained and the pilot signal is normalized by designing the pilot signal so as to satisfy the power condition.

First, the solution to Equation 11 is obtained

Figure 112015054477962-pat00052
ego,
Figure 112015054477962-pat00053
Each column of
Figure 112015054477962-pat00054
for all
Figure 112015054477962-pat00055
. ≪ / RTI >

4 is a flowchart illustrating a method of estimating downlink and uplink channels in a time division duplex scheme using a channel estimation algorithm according to an embodiment of the present invention. Referring to FIG. 4, a method for estimating downlink and uplink channels when a large-scale multi-input multiple-output communication system is a time division duplex system includes a) determining a pilot vector and an array response vector (S11) (b) acquiring the pilot vector and the array response vector at the mobile station, and designing a downward slanting operation matrix using the channel correlation information at step (S12); c) if the pilot vector transmitted from the base station passes through the downlink channel (D) estimating the downlink channel from the downlink received signal using the downlink diagonal matrix at step (S14), (e) transmitting the downlink signal from the terminal to the base station (S15) the channel correlation information is fed back; f) the base station acquires the pilot vector and the array response vector; A step S16 of designing a matrix S, g) the pilot vector transmitted from the UE arrives at the base station as an uplink received signal through an uplink channel, h) a base station And estimating the uplink channel from the uplink received signal (S18).

In the method of estimating the downlink and uplink channels, steps a) through d) are steps for estimating a downlink channel, and steps e) through h) are steps for estimating an uplink channel. The reason why the present invention can be applied to the time division duplex scheme as described above is that the proposed channel estimation algorithm is not limited to the FDD system. In the TDD mode, the proposed channel estimation scheme can be applied with some modifications or conditions. The signal model for the uplink channel estimation is as follows. At the terminal

Figure 112015054477962-pat00056
And transmits the pilot signal to the base station,
Figure 112015054477962-pat00057
Is expressed by Equation (12). The received signal for uplink channel estimation may be referred to as an uplink received signal to distinguish it from other received signals.

Figure 112015054477962-pat00058

Assuming that there is no quantization error as in Equation (4) when transmitting T pilot signals as in the downlink channel estimation, the signal received from the base station can be expressed as follows.

Figure 112015054477962-pat00059

Here,

Figure 112015054477962-pat00060
to be. From equation (13)
Figure 112015054477962-pat00061
The equation is expanded as shown in Equation (14) by using a vectorize operation in order to establish a problem of estimating the following equation.

Figure 112015054477962-pat00062

here

Figure 112015054477962-pat00063
Relationship,
Figure 112015054477962-pat00064
Means a Kronecker product.

Equation (14)

Figure 112015054477962-pat00065
, It can be expressed by Equation (4) and can be solved in the same way as the downlink channel estimation problem. However, the channel covariance for deriving the oblique operator must be known by the base station. This can be achieved by feedback of the entire correlation information or the incidence probability information at the terminal. In particular,
Figure 112015054477962-pat00066
, It can be called an upward diagonal arithmetic matrix for convenience.

5 is a flowchart illustrating a method of feeding back a downlink channel in a frequency division duplex scheme using a channel estimation algorithm according to an embodiment of the present invention. Referring to FIG. 5, when a large-scale multi-input multiple-output communication system is a frequency division duplex system, a) determining an array response vector and a pilot vector (S21), b) A step S22 of designing a downward slanting operation matrix using the channel correlation information, c) a step S23 in which the pilot vector transmitted from the base station passes through the downlink channel and arrives as a downlink received signal to the terminal d) estimating the downlink channel from the downlink received signal using the downward slanting matrix at step S24, e) transmitting the channel correlation information and the downlink received signal to the base station, (S25). In step e), the downlink received signal may be fed back to the base station. The reason why the present invention can be applied not only to channel estimation but also to channel feedback is as follows. If the terminal feeds back the received signal by the pilot transmitted from the base station to the base station together with channel correlation information or incidence probability information, the base station can restore the downlink channel information when performing the channel estimation process have. This is the same result that the estimated channel is fed back to the base station after estimating the channel in the terminal, so that it is possible to utilize the present invention also in the channel feedback.

FIG. 6 is a diagram illustrating a channel estimation result obtained by applying 40 pilots to an algorithm according to an embodiment of the present invention. FIG. 7 is a graph illustrating a result of channel estimation using an SNR of 30 dB for an algorithm according to an embodiment of the present invention Fig.

Simulations were performed to verify the performance of the embodiments of the present invention. The experimental environment is as follows. The number of antennas in the base station is 64 (M = 64) and the number of grid is 180 (G = 180). The channel has 15 paths (L = 15),

Figure 112015054477962-pat00067
, And the angular standard deviation of AoD was set at 10 degrees. To evaluate channel estimation performance
Figure 112015054477962-pat00068
(NMSE), which is defined as a normalized mean square error (NMSE). FIG. 6 shows the NMSE performance according to the SNR when the pilot overhead T is set to 40, and FIG. 7 shows the NMSE performance according to the pilot overhead when the SNR is set to 30 dB. From the results, it can be seen that the performance of the proposed method (denoted by G-ObMP) is the best and the pilot overhead can be reduced when using the proposed invention through the performance results.

The methods described above may be implemented with hardware components, software components, and / or a combination of hardware components and software components. For example, the apparatus and components described in the embodiments may be implemented within a computer system, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA) , A programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For ease of understanding, the processing apparatus may be described as being used singly, but those skilled in the art will recognize that the processing apparatus may have a plurality of processing elements and / As shown in FIG. For example, the processing unit may comprise a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as a parallel processor.

The software may comprise a computer program, code, instructions, or combinations of one or more of the foregoing, configured to configure the processing device to operate as desired, or independently or in combination (e.g., collectively) processing devices. The software and / or data may be in the form of any type of machine, component, physical device, virtual equipment, computer storage media, or device , Or may be permanently or temporarily embodied in a transmitted signal wave. The software may be distributed over a networked computer system and stored or executed in a distributed manner. The software and data may be stored on one or more computer readable recording media.

The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI > or equivalents, even if it is replaced or replaced.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (11)

In a large-scale multi-input multiple-output communication system including a base station with multiple antennas and a terminal with a single antenna,
a) determining a pilot vector and an array response vector;
b) obtaining a pilot vector and an array response vector from the terminal and designing a downward slanting operation matrix using channel correlation information;
c) the pilot vector transmitted from the base station passes through a downlink channel and arrives at a terminal as a downlink received signal; And
d) estimating the downlink channel from the downlink received signal using the downlink diagonal matrix;
Lt; / RTI >
Wherein the pilot vector minimizes the mean square error of the channel when estimating the channel by compressing and sensing the channel. The method according to claim 1,
In the step a)
Wherein the pilot vector is determined based on a matrix obtained by QR decomposition of the Gaussian probability matrix and the array response vector. The method according to claim 1,
In the step a)
Wherein the number of array response vectors is greater than the number of multiple paths of the downlink channel. The method according to claim 1,
In step d)
Using the forward slanting operation matrix,
Wherein the arrival angles of the downlink channel are estimated from the downlink received signals. The method according to claim 1,
In step d)
Estimating all the arrival angles of the downlink channel and then estimating the gain of the downlink channel with respect to the estimated arrival angle from the downlink received signal. The method according to claim 1,
After the step d)
e) feeding back the channel correlation information from the terminal to the base station;
f) acquiring the pilot vector and the array response vector at a base station, and designing an upward slant operation matrix using the channel correlation information;
g) the pilot vector transmitted from the UE arrives as an uplink received signal through the uplink channel to the base station; And
h) estimating the uplink channel from the uplink received signal using the uplink diagonal computation matrix at the base station;
Further comprising:
Wherein the large-scale multi-input multi-output communication system is a time division duplex system. In a large-scale multi-input multiple-output communication system including a base station with multiple antennas and a terminal with a single antenna,
a) determining an array response vector and a pilot vector;
b) obtaining the pilot vector and the array response vector at the terminal and designing a downward slanting operation matrix using channel correlation information;
c) the pilot vector transmitted from the base station passes through the downlink channel and reaches the terminal as a downlink received signal;
d) estimating the downlink channel from the downlink received signal using the downward slanting matrix; And
e) the channel correlation information and the downlink received signal are fed back from the terminal to the base station;
Lt; / RTI >
The large scale multi-input multiple-output communication system is a frequency division duplex system
Wherein the pilot vector minimizes a mean square error of a channel when the downlink channel is estimated by compressing and sensing the channel. 8. The method of claim 7,
In the step a)
Wherein the pilot vector is determined based on a matrix obtained by QR decomposition of a Gaussian probability matrix and the array response vector. 8. The method of claim 7,
In the step a)
Wherein the number of array response vectors is greater than the number of multiple paths of a downlink channel. 8. The method of claim 7,
In step d)
Using the forward slanting operation matrix,
And estimating arrival angles of the downlink channel from the downlink received signals. 8. The method of claim 7,
In step d)
Estimating all the arrival angles of the downlink channel and then estimating the downlink channel gain for the estimated arrival angle from the downlink received signal.

KR1020150079919A 2015-06-05 2015-06-05 Method for channel estimation and feedback in massive MIMO systems KR101669857B1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
KR1020150079919A KR101669857B1 (en) 2015-06-05 2015-06-05 Method for channel estimation and feedback in massive MIMO systems

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
KR1020150079919A KR101669857B1 (en) 2015-06-05 2015-06-05 Method for channel estimation and feedback in massive MIMO systems

Publications (1)

Publication Number Publication Date
KR101669857B1 true KR101669857B1 (en) 2016-10-27

Family

ID=57247124

Family Applications (1)

Application Number Title Priority Date Filing Date
KR1020150079919A KR101669857B1 (en) 2015-06-05 2015-06-05 Method for channel estimation and feedback in massive MIMO systems

Country Status (1)

Country Link
KR (1) KR101669857B1 (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106452534A (en) * 2016-11-23 2017-02-22 南京邮电大学 Pilot optimization method for large-scale MIMO channel estimation based on structural compressed sensing
CN108242943A (en) * 2016-12-23 2018-07-03 上海诺基亚贝尔股份有限公司 The method and apparatus of precoding is used in communication
KR20210067762A (en) 2019-11-29 2021-06-08 국방과학연구소 Method and apparatus for precoding downlink data in multi input multi output system
CN114726412A (en) * 2021-01-04 2022-07-08 中国移动通信有限公司研究院 Channel information acquisition method, device and related equipment

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20160061848A (en) * 2014-11-24 2016-06-01 한국과학기술원 Ofdm symbol transmiting using espar antenna in beamspace mimo system

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20160061848A (en) * 2014-11-24 2016-06-01 한국과학기술원 Ofdm symbol transmiting using espar antenna in beamspace mimo system

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
Junho Lee et al., "Exploiting spatial sparsity for estimating channels of hybrid MIMO systems in millimeter wave communications", 2014 IEEE Global Communications Conference(2014.12.14.)* *
Peter W. C. Chan et al., "The evolution path of 4G networks: FDD or TDD?", IEEE Communications Magazine(2007.01.08.)* *
S. L. G. Nguyen and A. Ghrayeb, "Compressive sensing-based channel estimation for massive multiuser MIMO systems," in Proc. IEEE Wireless Commun. Netw. Conf., Shanghai, China, Apr. 2013.
Wei Chen et al., "Projection Design for Statistical Compressive Sensing: A Tight Frame Based Approach", IEEE Transactions on Signal Processing(2013.03.22.)* *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106452534A (en) * 2016-11-23 2017-02-22 南京邮电大学 Pilot optimization method for large-scale MIMO channel estimation based on structural compressed sensing
CN108242943A (en) * 2016-12-23 2018-07-03 上海诺基亚贝尔股份有限公司 The method and apparatus of precoding is used in communication
KR20210067762A (en) 2019-11-29 2021-06-08 국방과학연구소 Method and apparatus for precoding downlink data in multi input multi output system
CN114726412A (en) * 2021-01-04 2022-07-08 中国移动通信有限公司研究院 Channel information acquisition method, device and related equipment

Similar Documents

Publication Publication Date Title
JP6685623B2 (en) Efficient sparse channel estimation based on compressed sensing
KR101750656B1 (en) Method for pattern/polarization beam division multiple access based on massive antennas, and an apparatus performing the same
US20230040739A1 (en) Method, device and computer readable medium of communication
US8259599B2 (en) Systems and methods for distributed beamforming based on carrier-to-caused interference
KR101669857B1 (en) Method for channel estimation and feedback in massive MIMO systems
KR101576915B1 (en) Communication system of using joint leakage suppression scheme with low complexity
CN110380997B (en) Millimeter wave channel estimation method based on adaptive compressed sensing
JP2012503934A (en) Method for precoding a signal for transmission in a wireless MIMO system
CN115053465B (en) Information transmission method and device
CN108242943B (en) Method and device for precoding in communication
KR20160000222A (en) Method and apparatus for iterative interference alignment for downlink multi-user mulit-input multi-output
KR102376810B1 (en) Method and apparatus for improving security performance using artificial noise
KR101022120B1 (en) Method and Apparatus for Determining Weight Matrix for Beamforming
CN113746509B (en) Channel information processing method, device, terminal, network side equipment and medium
CN114124173A (en) Method, apparatus and storage medium for channel information acquisition
KR102118389B1 (en) Basement apparatus, and control method thereof
Ullah et al. Beyond traditional beamforming: Singular vector projection techniques for mu-mimo interference management
US20150181611A1 (en) Opportunistic downlink interference alignment
Han et al. FDD massive MIMO without CSI feedback
KR101819318B1 (en) Effective interference cancellation method with limited channel feedback for time varying multi-user mimo system
KR20230024713A (en) Method and Apparatus for Adjusting Low-Complexity Optimal Phase Shift for Intelligent Reflecting Surface
CN110999114B (en) Codebook subset restriction based on wideband amplitude
KR101813781B1 (en) Method of generating a pilot beam, and an apparatus performing the same
WO2018188584A1 (en) Space division multiplexing method and apparatus
KR102103667B1 (en) Method for Multi-mode Transmission of Multi-user Beamforming System

Legal Events

Date Code Title Description
E701 Decision to grant or registration of patent right
GRNT Written decision to grant
FPAY Annual fee payment

Payment date: 20191001

Year of fee payment: 4