CN112702093A - Channel estimation method in FDD downlink multi-user large-scale MIMO system - Google Patents

Channel estimation method in FDD downlink multi-user large-scale MIMO system Download PDF

Info

Publication number
CN112702093A
CN112702093A CN202011526747.2A CN202011526747A CN112702093A CN 112702093 A CN112702093 A CN 112702093A CN 202011526747 A CN202011526747 A CN 202011526747A CN 112702093 A CN112702093 A CN 112702093A
Authority
CN
China
Prior art keywords
channel
user
vector
theta
estimation method
Prior art date
Legal status (The legal status 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 status listed.)
Granted
Application number
CN202011526747.2A
Other languages
Chinese (zh)
Other versions
CN112702093B (en
Inventor
秦树珍
王海泉
王雨佳
赵吕珂
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hangzhou Dianzi University
Original Assignee
Hangzhou Dianzi University
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 Hangzhou Dianzi University filed Critical Hangzhou Dianzi University
Priority to CN202011526747.2A priority Critical patent/CN112702093B/en
Publication of CN112702093A publication Critical patent/CN112702093A/en
Application granted granted Critical
Publication of CN112702093B publication Critical patent/CN112702093B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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
    • 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
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Power Engineering (AREA)
  • Radio Transmission System (AREA)

Abstract

The invention provides a channel estimation method in an FDD downlink multi-user large-scale MIMO system, which comprises the following steps: s1, designing training sequences and processing received signals aiming at a plurality of users; s2, carrying out corresponding angle estimation according to the received signal; s3, constructing a channel guide vector through an angle estimation value; and S4, reconstructing the state information of the downlink channel according to the constructed channel guide vector. The channel estimation method based on a small amount of uniform training sequences can simultaneously estimate the channel state information by a plurality of users, effectively reduce the pilot frequency overhead and lay a foundation for realizing higher system throughput.

Description

Channel estimation method in FDD downlink multi-user large-scale MIMO system
Technical Field
The invention relates to the technical field of wireless communication, in particular to a channel estimation method in an FDD downlink multi-user large-scale MIMO system.
Background
In a large-scale multiple-input multiple-output (MIMO) system, by utilizing a large-scale antenna array of a Base Station (BS), the system can obtain extremely high spatial resolution and spatial division multiplexing gain, can provide services for a plurality of users simultaneously under the condition of not being seriously interfered, and greatly improves the spectral efficiency and the transmission rate of the system. But these good performances all depend on the availability of Channel State Information (CSI), so how to acquire the complete CSI in a real system is crucial to the system performance.
For a Frequency Division Duplex (FDD) massive MIMO system, a large amount of pilot overhead is required to acquire complete downlink CSI. This is because in an actual large-scale MIMO system, the number of training times and the feedback overhead are in direct proportion to the number of BS antennas, and the resources for acquiring complete CSI by using a conventional linear channel estimation method (such as least square algorithm (LS) and Linear Minimum Mean Square Error (LMMSE) algorithm) need to transmit training sequences with the same number as that of base station antennas, so that in an FDD downlink channel estimation method developed in recent years, different training sequences are almost designed for channel information of different users to acquire CSI, and thus, for a multi-user system, more time and resources are consumed, which is impractical.
In view of the above technical problems, it is desirable to improve.
Disclosure of Invention
The invention aims to provide a channel estimation method in an FDD downlink multi-user large-scale MIMO system aiming at the defects of the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
the channel estimation method in the FDD downlink multi-user large-scale MIMO system comprises the following steps:
s1, designing training sequences and processing received signals aiming at a plurality of users;
s2, carrying out corresponding angle estimation according to the received signal;
s3, constructing a channel guide vector through an angle estimation value;
and S4, reconstructing the state information of the downlink channel according to the constructed channel guide vector.
Further, in step S1, the training sequence is designed by multiple users and the received signal is processed by K independent single-antenna users, a base station, and M antennas are equipped at the base station.
Furthermore, M antennas provided at the base station are arranged in a planar array, and M is equal to MhMvWherein each row has MhA root antenna, each column having MvFor a root antenna, the channel from the base station to the receiving end for the K (K ═ 1,2,3, …, K) th user is specifically represented as:
Figure BDA0002850812930000021
wherein: pkRepresenting the number of propagation paths between the base station and the receiving end of the kth user; gk,lThe channel complex gain of the l path representing the k user; α (θ, φ) represents a steering vector of the channel; alpha (theta)k,l,φk,l) A steering vector representing a channel of the ith path of the kth user; thetak,lAnd phik,lRespectively representing the elevation and azimuth of the ith path of the kth user.
Further, the steering vector α (θ, φ) of the channel is expressed as:
Figure BDA0002850812930000022
Figure BDA0002850812930000023
Figure BDA0002850812930000024
wherein: d represents the distance between antenna elements, and if d is lambda/2, lambda is the carrier wavelength; alpha is alphav(theta) includes information on the longitudinal angle of the planar array, alphah(θ, φ) contains lateral angle information of the planar array.
Further, the designing of the training sequence in step S1 specifically includes:
suppose that the base station transmits S signals to all K usersNThe matrix is an M × N matrix, and is specifically expressed as:
Figure BDA0002850812930000031
wherein: fnA discrete Fourier DFT matrix representing n dimensions; 0nA zero matrix of n dimensions; 0(M-7n)×nThe matrix is a zero matrix with (M-7N) multiplied by N dimensions, wherein N is equal to N when N is 4N, and N is the total number of samples.
Further, the processing of the received signal in step S1 specifically includes:
after the noise is mixed, the received signal of the kth (K ═ 1,2, 3., K) ue is represented as:
Figure BDA0002850812930000032
wherein: ρ is the signal-to-noise ratio, zkThe noise mixed in the k user is assumed to be Gaussian noise which obeys zero mean and unit variance;
for transmitting signals
Figure BDA0002850812930000035
Performing an inverse DFT transform, the actual received signal at the kth user is represented as:
Figure BDA0002850812930000033
wherein, ykRepresenting the received signal.
Further, the step S2 specifically includes:
s21, setting a codebook, and enabling a transverse vector alpha in a channel guide vector alpha (theta, phi)hCos θ sin φ and longitudinal vector α in (θ, φ)vSin theta in (theta) is respectively regarded as two integers in the interval (-1, 1); two M codebooks are respectively created in the (-1,1) interval, and M is equal to M/2;
Figure BDA0002850812930000034
Figure BDA0002850812930000041
s22, the received signal of i (i ═ 1,2, …, P) path at k-th user is yk(i) The noise of the ith path of the kth user is derived and is expressed as:
Figure BDA0002850812930000042
wherein,
Figure BDA0002850812930000043
and
Figure BDA0002850812930000044
representing the value of the set angle codebook in each traversal;
Figure BDA0002850812930000045
representing the value after traversing the set angle codebook each time;
s23, combining a preset codebook, setting the code words as follows:
Figure BDA0002850812930000046
combining set code words
Figure BDA0002850812930000047
Traverse a predetermined codebook from yk,r(i) Selecting the code word with the maximum projection power, and expressing as:
Figure BDA0002850812930000048
in a code word
Figure BDA0002850812930000049
And
Figure BDA00028508129300000410
reconstructing the steering vector of the channel by using the estimated value of the angle information for the estimated value of the angle information in the steering vector of the channel in the ith path of the kth user
Figure BDA00028508129300000411
S24, traversing the angle codebook by combining a preset codebook, and selecting and obtaining an estimation value of angle information, thereby estimating the ith path gain of the kth user, wherein the estimation is represented as:
Figure BDA00028508129300000412
then the estimated value of the channel complex gain is:
Figure BDA00028508129300000413
further, the step S3 is specifically:
incorporating angle information estimates
Figure BDA0002850812930000051
By α in the channel steering vector α (θ, φ)v(theta) and alphah(theta, phi) form to construct steering matrix transverse vector
Figure BDA0002850812930000052
And a longitudinal vector
Figure BDA0002850812930000053
The product of the two tensors
Figure BDA0002850812930000054
Constructing channel steering vectors
Figure BDA0002850812930000055
Further, the step S4 is specifically: steering vector based on reconstructed channel
Figure BDA0002850812930000056
And channel complex gain estimates
Figure BDA0002850812930000057
And reconstructing the channel matrix to recover the downlink channel information.
Compared with the prior art, the channel estimation method based on a small amount of uniform training sequences can simultaneously estimate the channel state information by a plurality of users, effectively reduce the pilot frequency overhead and lay a foundation for realizing higher system throughput.
Drawings
Fig. 1 is a flowchart of a channel estimation method in an FDD downlink multi-user massive MIMO system according to a first embodiment;
fig. 2 is a simulation diagram of a channel estimation method in an FDD downlink multi-user massive MIMO system according to a first embodiment.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
Example one
The present embodiment provides a channel estimation method in an FDD downlink multi-user massive MIMO system, as shown in fig. 1, including:
s1, designing training sequences and processing received signals aiming at a plurality of users;
s2, carrying out corresponding angle estimation according to the received signal;
s3, constructing a channel guide vector through an angle estimation value;
and S4, reconstructing the state information of the downlink channel according to the constructed channel guide vector.
In the channel estimation method in the FDD downlink multi-user large-scale MIMO system of the present embodiment, a channel estimation method based on a small number of uniform training sequences is used for a downlink multi-user large-scale MIMO system, and is improved in view of the shortcomings of the existing channel estimation method.
The specific application case is as follows:
suppose that there are 1 cell, 1 base station, and the number of antennas of the base station is 128, the sampling number N is 32, N is 8, and m is 64. Number of paths P for radio signals from base station to each userkEach user receive antenna is 1 ═ 6. Number of antennas M per row of planar array antenna h16, number of antennas per column M v8, carrier wavelength λ 3 × 108/1.5×109The array element spacing d is lambda/2, and the elevation angle theta and the azimuth angle phi contained in the steering vector in the channel matrix are obedient intervals (-900,900)]Uniformly distributed therein.
In step S1, training sequences are designed for a plurality of users and received signals are processed.
The 128 antennas provided at the base station are arranged in a Planar Array (UPA Uniform-Planar-Array) and M is 16 × 8, where each row has 16 antennas and each column has 8 antennas. The channel from the base station to the receiving end of the kth user is specifically represented as:
Figure BDA0002850812930000061
wherein: pkThe number of propagation paths, g, between the base station and the receiving end of the kth userk,lThe channel complex gain of the i-th path representing the k-th user is assumed to follow a complex gaussian distribution of zero mean, unity variance. Alpha (theta)k,l,φk,l) Steering vector, theta, of channel representing the ith path of the kth userk,lAnd phik,lThe l-th bars representing the k-th users respectivelyElevation and azimuth of the path.
The training sequence is specifically designed as follows:
for all K users, the basis for selecting training sequences at the base station is as follows: it should be satisfied that the number of sampling times of the horizontal angle information and the vertical angle information in the channel steering vector of the ith path of the kth user satisfies the ratio of the number of rows and the number of columns of the planar array antenna.
Suppose that the base station transmits S signals to all K usersNA matrix of 128 × 32 dimensions, which is specifically expressed as:
Figure BDA0002850812930000071
wherein: fnIs an 8-dimensional Discrete Fourier Transform (DFT) matrix, 0nZero matrix of 8 dimensions, 0(M-7n)×nThe matrix is a zero matrix with 72 × 8 dimensions, N is equal to N, and N is the total number of samples.
The processing of the received signal is specifically:
after mixing noise, the received signal of the kth ue is represented as:
Figure BDA0002850812930000072
wherein: ρ is the signal-to-noise ratio, zkThe noise mixed in at the kth user is assumed to be gaussian noise subject to zero mean, unit variance.
For transmitting signals
Figure BDA0002850812930000073
Performing an inverse DFT transform, the actual received signal at the kth user is represented as:
Figure BDA0002850812930000074
wherein, ykRepresenting the received signal.
In step S2, a corresponding angle estimation is performed based on the received signal.
A codebook is created in advance for an angle and all codebooks are traversed next.
S21, setting a codebook, and enabling a transverse vector alpha in a channel guide vector alpha (theta, phi)hCos θ sin φ and longitudinal vector α in (θ, φ)vSin θ in (θ) is considered as two integers in the interval (-1,1), respectively. Two codebooks of 64 copies are created in the (-1,1) interval, respectively.
Figure BDA0002850812930000075
Figure BDA0002850812930000081
S22, the received signal of i (i ═ 1,2,3,4,5,6) paths at the k-th user is yk(i) The noise of the l path derived from the k user is expressed as follows:
Figure BDA0002850812930000082
wherein:
Figure BDA0002850812930000083
and
Figure BDA0002850812930000084
representing the value of the set angle codebook for each traversal,
Figure BDA0002850812930000085
and representing the value after traversing the set angle codebook each time.
S23, combining a preset codebook, setting the code words as follows:
Figure BDA0002850812930000086
binding settingsCode word of
Figure BDA0002850812930000087
Traverse a predetermined codebook from yk,r(i) Selecting the code word with the maximum projection power, wherein the specific calculation formula is as follows:
Figure BDA0002850812930000088
then in the code word
Figure BDA0002850812930000089
And
Figure BDA00028508129300000810
namely the estimated value of the angle information in the channel guide vector in the ith path of the kth user, and the guide vector of the channel can be reconstructed by using the obtained estimated value of the angle information
Figure BDA00028508129300000811
S24, traversing the angle codebook by combining a preset codebook, and selecting and obtaining an estimation value of angle information, thereby estimating the ith path gain of the kth user, wherein the specific calculation formula is as follows:
Figure BDA00028508129300000812
then the estimated value of the channel complex gain is:
Figure BDA00028508129300000813
in step S3, a channel steering vector is constructed from the angle estimates.
Incorporating angle information estimates
Figure BDA0002850812930000091
By α in the channel steering vector α (θ, φ)v(θ) And alphah(theta, phi) form to construct steering matrix transverse vector
Figure BDA0002850812930000092
And a longitudinal vector
Figure BDA0002850812930000093
The product of the two tensors
Figure BDA0002850812930000094
Channel steering vectors can be constructed
Figure BDA0002850812930000095
In step S4, the downlink channel state information is reconstructed from the constructed channel steering vector.
Steering vector based on reconstructed channel
Figure BDA0002850812930000096
And channel complex gain estimates
Figure BDA0002850812930000097
And reconstructing the channel matrix to recover the downlink channel information.
In this embodiment, in order to analyze the performance of the proposed channel estimation method based on angle estimation in the FDD large-scale antenna system, the system throughput is defined as: c ═ log (1+ ρ | | | h)Hf||2) And f is precoding.
As shown in fig. 2, it is a simulation diagram of system throughput under the above exemplary conditions, where "ideal transition state" is a system throughput curve using real channel for precoding, and the method proposed in this embodiment uses reconstructed channel for precoding. As can be seen from fig. 2, the precoding performed by the channel estimation value obtained by the method provided by the present embodiment is smaller than about 1 bit in throughput difference of the system compared with the precoding performed by the real channel under the same condition, and the signal-to-noise ratio has little influence on the method provided by the present invention, so that the method has good system performance.
Compared with the prior art, in order to better save training overhead, the embodiment can be deduced by combining with the PRONY equation after theoretical derivation, and for a large-scale antenna system, for an array antenna, channels of the array antenna are in a form meeting the PRONY equation. Based on this, it is considered that in a multi-user scenario, different users can transmit the same training sequence to perform training simultaneously, and complete channel state information is acquired by combining an angle estimation method, so that pilot frequency overhead and time consumption are reduced. The channel estimation method based on a small amount of uniform training sequences can simultaneously estimate the channel state information by a plurality of users, effectively reduce the pilot frequency overhead and lay the foundation for realizing higher system throughput.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.

Claims (9)

  1. A channel estimation method in an FDD downlink multi-user large-scale MIMO system is characterized by comprising the following steps:
    s1, designing training sequences and processing received signals aiming at a plurality of users;
    s2, carrying out corresponding angle estimation according to the received signal;
    s3, constructing a channel guide vector through an angle estimation value;
    and S4, reconstructing the state information of the downlink channel according to the constructed channel guide vector.
  2. 2. The method of claim 1, wherein in step S1, the training sequences are designed for multiple users and the received signals include K independent single-antenna users, a base station, and M antennas equipped at the base station.
  3. 3. FDD downlink multiuser macro according to claim 2The channel estimation method in the scale MIMO system is characterized in that M antennas equipped at the base station adopt a planar array arrangement mode, and M is MhMvWherein each row has MhA root antenna, each column having MvFor a root antenna, the channel from the base station to the receiving end for the K (K ═ 1,2,3, …, K) th user is specifically represented as:
    Figure FDA0002850812920000011
    wherein: pkRepresenting the number of propagation paths between the base station and the receiving end of the kth user; gk,lThe channel complex gain of the l path representing the k user; α (θ, φ) represents a steering vector of the channel; alpha (theta)k,l,φk,l) A steering vector representing a channel of the ith path of the kth user; thetak,lAnd phik,lRespectively representing the elevation and azimuth of the ith path of the kth user.
  4. 4. The method of claim 3, wherein the steering vector α (θ, φ) of the channel is expressed as:
    Figure FDA0002850812920000012
    Figure FDA0002850812920000013
    Figure FDA0002850812920000021
    wherein: d represents the distance between antenna elements, and if d is lambda/2, lambda is the carrier wavelength; alpha is alphav(theta) includes information on the longitudinal angle of the planar array, alphah(theta, phi) transverse to an array comprising planesAnd (4) direction angle information.
  5. 5. The channel estimation method in an FDD downlink multiuser massive MIMO system according to claim 4, wherein the designing of the training sequence in the step S1 is specifically:
    suppose that the base station transmits S signals to all K usersNThe matrix is an M × N matrix, and is specifically expressed as:
    Figure FDA0002850812920000022
    wherein: fnA discrete Fourier DFT matrix representing n dimensions; 0nA zero matrix of n dimensions; 0(M-7n)×nThe matrix is a zero matrix with (M-7N) multiplied by N dimensions, wherein N is equal to N when N is 4N, and N is the total number of samples.
  6. 6. The channel estimation method in an FDD downlink multiuser massive MIMO system according to claim 5, wherein the processing of the received signal in the step S1 is specifically:
    after the noise is mixed, the received signal of the kth (K ═ 1,2, 3., K) ue is represented as:
    Figure FDA0002850812920000023
    wherein: ρ is the signal-to-noise ratio, zkThe noise mixed in the k user is assumed to be Gaussian noise which obeys zero mean and unit variance;
    for transmitting signals
    Figure FDA0002850812920000024
    Performing an inverse DFT transform, the actual received signal at the kth user is represented as:
    Figure FDA0002850812920000031
    wherein, ykRepresenting the received signal.
  7. 7. The channel estimation method in an FDD downlink multiuser massive MIMO system according to claim 6, wherein the step S2 specifically comprises:
    s21, setting a codebook, and enabling a transverse vector alpha in a channel guide vector alpha (theta, phi)hCos θ sin φ and longitudinal vector α in (θ, φ)vSin theta in (theta) is respectively regarded as two integers in the interval (-1, 1); two M codebooks are respectively created in the (-1,1) interval, and M is equal to M/2;
    Figure FDA0002850812920000032
    Figure FDA0002850812920000033
    s22, the received signal of i (i ═ 1,2, …, P) path at k-th user is yk(i) The noise of the ith path of the kth user is derived and is expressed as:
    Figure FDA0002850812920000034
    wherein,
    Figure FDA0002850812920000035
    and
    Figure FDA0002850812920000036
    representing the value of the set angle codebook in each traversal;
    Figure FDA0002850812920000037
    representing the value after traversing the set angle codebook each time;
    s23, combining a preset codebook, setting the code words as follows:
    Figure FDA0002850812920000038
    combining set code words
    Figure FDA0002850812920000039
    Traverse a predetermined codebook from yk,r(i) Selecting the code word with the maximum projection power, and expressing as:
    Figure FDA00028508129200000310
    in a code word
    Figure FDA00028508129200000311
    And
    Figure FDA00028508129200000312
    reconstructing the steering vector of the channel by using the estimated value of the angle information for the estimated value of the angle information in the steering vector of the channel in the ith path of the kth user
    Figure FDA0002850812920000041
    S24, traversing the angle codebook by combining a preset codebook, and selecting and obtaining an estimation value of angle information, thereby estimating the ith path gain of the kth user, wherein the estimation is represented as:
    Figure FDA0002850812920000042
    then the estimated value of the channel complex gain is:
    Figure FDA0002850812920000043
  8. 8. the channel estimation method in an FDD downlink multiuser massive MIMO system according to claim 7, wherein the step S3 specifically comprises:
    incorporating angle information estimates
    Figure FDA0002850812920000044
    By α in the channel steering vector α (θ, φ)v(theta) and alphah(theta, phi) form to construct steering matrix transverse vector
    Figure FDA0002850812920000045
    And a longitudinal vector
    Figure FDA0002850812920000046
    The product of the two tensors
    Figure FDA0002850812920000047
    Constructing channel steering vectors
    Figure FDA0002850812920000048
  9. 9. The channel estimation method in an FDD downlink multiuser massive MIMO system according to claim 8, wherein the step S4 specifically comprises: steering vector based on reconstructed channel
    Figure FDA0002850812920000049
    And channel complex gain estimates
    Figure FDA00028508129200000410
    And reconstructing the channel matrix to recover the downlink channel information.
CN202011526747.2A 2020-12-22 2020-12-22 Channel estimation method in FDD downlink multi-user large-scale MIMO system Active CN112702093B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011526747.2A CN112702093B (en) 2020-12-22 2020-12-22 Channel estimation method in FDD downlink multi-user large-scale MIMO system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011526747.2A CN112702093B (en) 2020-12-22 2020-12-22 Channel estimation method in FDD downlink multi-user large-scale MIMO system

Publications (2)

Publication Number Publication Date
CN112702093A true CN112702093A (en) 2021-04-23
CN112702093B CN112702093B (en) 2021-11-30

Family

ID=75510202

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011526747.2A Active CN112702093B (en) 2020-12-22 2020-12-22 Channel estimation method in FDD downlink multi-user large-scale MIMO system

Country Status (1)

Country Link
CN (1) CN112702093B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114244658A (en) * 2021-12-24 2022-03-25 杭州电子科技大学 Channel estimation method based on multiple angle estimation in large-scale MIMO system

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160087699A1 (en) * 2004-08-11 2016-03-24 Interdigital Technology Corporation Channel sounding for improved system performance
CN106302274A (en) * 2016-08-26 2017-01-04 清华大学 A kind of extensive mimo system multiuser channel is estimated and tracking
CN106453163A (en) * 2016-10-11 2017-02-22 电子科技大学 Massive MIMO (Multiple Input Multiple Output) channel estimation method
CN108259072A (en) * 2018-01-10 2018-07-06 杭州电子科技大学 The method for reducing training sequence expense for the extensive MIMO downlink systems of FDD
CN109327918A (en) * 2018-11-19 2019-02-12 东南大学 A kind of extensive MIMO down channel method for reconstructing of the FDD of low overhead
CN109802901A (en) * 2017-11-16 2019-05-24 上海交通大学 Based on the 3D mimo channel estimation method and system for reaching angular measurement
CN110086734A (en) * 2019-03-14 2019-08-02 西安电子科技大学 Channel tracking and restoration methods in a kind of extensive mimo system of time-varying

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160087699A1 (en) * 2004-08-11 2016-03-24 Interdigital Technology Corporation Channel sounding for improved system performance
CN106302274A (en) * 2016-08-26 2017-01-04 清华大学 A kind of extensive mimo system multiuser channel is estimated and tracking
CN106453163A (en) * 2016-10-11 2017-02-22 电子科技大学 Massive MIMO (Multiple Input Multiple Output) channel estimation method
CN109802901A (en) * 2017-11-16 2019-05-24 上海交通大学 Based on the 3D mimo channel estimation method and system for reaching angular measurement
CN108259072A (en) * 2018-01-10 2018-07-06 杭州电子科技大学 The method for reducing training sequence expense for the extensive MIMO downlink systems of FDD
CN109327918A (en) * 2018-11-19 2019-02-12 东南大学 A kind of extensive MIMO down channel method for reconstructing of the FDD of low overhead
CN110086734A (en) * 2019-03-14 2019-08-02 西安电子科技大学 Channel tracking and restoration methods in a kind of extensive mimo system of time-varying

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
HAYDER ALMOSA; SUSANNA MOSLEH: "Downlink Channel Estimation with Limited Feedback for FDD Multi-User Massive MIMO with Spatial Channel Correlation", 《2018 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC)》 *
周蕾: "FDD大规模MIMO系统基于角度域稀疏性和互易性的信道估计与传输方法研究", 《中国优秀硕士学位论文全文数据库 (基础科学辑)》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114244658A (en) * 2021-12-24 2022-03-25 杭州电子科技大学 Channel estimation method based on multiple angle estimation in large-scale MIMO system
CN114244658B (en) * 2021-12-24 2024-03-26 杭州电子科技大学 Channel estimation method based on multiple angle estimation in large-scale MIMO system

Also Published As

Publication number Publication date
CN112702093B (en) 2021-11-30

Similar Documents

Publication Publication Date Title
Upadhya et al. Covariance matrix estimation for massive MIMO
EP2737640B1 (en) Apparatus and method for combining baseband processing and radio frequency beam steering in a wireless communication system
Hoydis et al. Comparison of linear precoding schemes for downlink massive MIMO
Ravindran et al. MIMO broadcast channels with block diagonalization and finite rate feedback
KR102273118B1 (en) Apparatus and method for feeding back channel information in wireless communication system
JP5133007B2 (en) Transmitting apparatus and beamforming matrix generation method
CN101394254A (en) Linear pre-coding method in multi-user MIMO system
CN106487725A (en) A kind of multi-user MIMO system millimeter wave channel estimation methods
CN112737649B (en) Millimeter wave channel estimation method based on angle grid optimization and norm constraint
KR101547421B1 (en) Method for hybrid beamforming on statistical channel informaion, and apparatuses performing the same
CN102546088A (en) BD (block diagonalization) pre-coding method and device
JP2014519227A (en) Spatial CSI feedback method for MIMO (Multiple-input Multiple-output) and spatial CSI feedback system
CN102696180B (en) Space channel State Feedback Approach and device
Schwarz et al. Grassmannian product codebooks for limited feedback massive MIMO with two-tier precoding
CN103493393B (en) For multiple-input and multiple-output（MIMO）Spatial channel state information feedback method and system
CN112702092B (en) Channel estimation method in FDD downlink multi-user large-scale MIMO system
CN110943767B (en) Precoding design method based on channel part reciprocity in FDD large-scale MIMO system
CN102171946B (en) Method and device for data transmission
Yin et al. A partial channel reciprocity-based codebook for wideband FDD massive MIMO
CN113839695B (en) FDD large-scale MIMO and rate optimal statistical precoding method and device
CN105743559A (en) Hybrid beam-forming and space-time coding multi-user downlink transmission method in Massive MIMO (Multiple Input Multiple Output) system
CN112702093B (en) Channel estimation method in FDD downlink multi-user large-scale MIMO system
CN114221838A (en) Channel estimation method and system using channel conjugate data in large-scale MIMO system
Dreifuerst et al. Machine learning codebook design for initial access and CSI type-II feedback in sub-6GHz 5G NR
JP2009268106A (en) Method for determining signal to interference plus noise ratio (sinr) and apparatus therefor

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant