CN112491752A - Multi-user large-scale MIMO relay network joint channel estimation technology - Google Patents

Multi-user large-scale MIMO relay network joint channel estimation technology Download PDF

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CN112491752A
CN112491752A CN202011106410.6A CN202011106410A CN112491752A CN 112491752 A CN112491752 A CN 112491752A CN 202011106410 A CN202011106410 A CN 202011106410A CN 112491752 A CN112491752 A CN 112491752A
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user
relay
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channel estimation
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CN112491752B (en
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杜建和
叶思雨
陈远知
张鹏
刘昌银
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Communication University of China
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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
    • 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
    • H04B7/0452—Multi-user MIMO systems
    • 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
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Abstract

The invention relates to a channel joint estimation technology of a multi-user large-scale MIMO relay system. The method has high channel estimation precision, can estimate all channel matrixes of a communication system, and simultaneously deduces the CRB of the method. The method comprises the following implementation steps: 1) establishing a multi-user large-scale MIMO relay system model; 2) each user side sends an orthogonal channel training sequence to the relay; 3) the relay amplifies the received signals and forwards the signals to all users; 4) constructing a Tucker-2 tensor model at a user side; 5) designing a simple and feasible iterative algorithm fitting tensor model to realize the joint estimation of the channel matrix; 6) the CRB of the proposed method is derived. The channel estimation method has the advantage of high accuracy, and in addition, the channel estimation method still has high channel estimation accuracy even if the number of relay antennas is increased, namely channel parameters needing to be estimated are increased.

Description

Multi-user large-scale MIMO relay network joint channel estimation technology
Technical Field
The invention relates to the technical field of wireless communication, in particular to a joint channel estimation method for a multi-user large-scale multi-input multi-output relay network
Background
A large-scale multiple-input multiple-output (MIMO) relay network has been favored by the academic world and the industry because of its excellent communication performance. The next generation communication system will support multiple users and guarantee strict quality of service (QoS) requirements, and therefore, designing a communication system with high spectrum utilization rate is of great significance for overcoming the scarcity of spectrum. In particular, massive MIMO provides a large number of spatial degrees of freedom, contributes to large multiplexing and diversity gains, and effectively improves link reliability and data transmission rate.
Among the blind signal processing techniques, the blind signal processing technique based on tensor can obtain an optimal solution through tensor fitting on the basis of decomposition uniqueness by utilizing the inherent multi-domain characteristics of signals in frequency domain, time domain, code domain and space domain. Tensor-based modeling is more flexible than traditional multidimensional matrix modeling in wireless communication systems. Meanwhile, the tensor decomposition does not destroy the internal relation among all elements, the spatial structure information of the signals is fully utilized, and the estimation precision is improved. Furthermore, tensor modeling benefits from multiple diversity, a feature that helps achieve multi-user signal separation/equalization and channel estimation under more loosely discernable conditions than traditional matrix methods.
Tensor and tensor decomposition theories are widely applied to the fields of radar, data compression, communication, pattern recognition, image processing and the like. The tensor approach is used for solving the joint estimation problem in the MIMO cooperative communication system, which is quite mature. The parallel factor (PARAFAC) tensor model is utilized to model a multi-user point-to-point direct sequence code division multiple access (ZDS-CDMA) system, and a symbol matrix and a channel matrix can be accurately estimated at a receiving end. The scholars propose a joint channel and symbol estimation method based on determined tensor aiming at a multi-user single-input multiple-output (SIMO) Code Division Multiple Access (CDMA) communication system, and obtain a third-order block Tucker-2 model of a plurality of receiving antennas for receiving signals.
Recently, the PARAFAC model is applied to millimeter wave (mmWave) massive MIMO systems to achieve joint channel estimation for multiple users. The extension of the PARAFAC decomposition to multi-parameter estimation of millimeter wave MIMO orthogonal frequency division multiplexing (MIMO-OFDM) systems has been studied and the associated Cramer-Rao bound (CRB) derived to show the estimated performance. The scholars propose a robust semi-blind receiver based on the Tucker-2 model for joint estimation of symbols and channels. And such a semi-blind receiver can be applied to a multi-user massive MIMO system.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the defects of the prior art, the invention provides a channel joint estimation method in a multi-user large-scale MIMO relay system, which can estimate all channel matrixes of a communication system and simultaneously deduce the CRB of the method.
The technical scheme is as follows: the channel joint estimation technology of the multi-user large-scale MIMO relay system comprises the following steps:
establishing a multi-user large-scale MIMO relay system model;
each user side sends an orthogonal channel training sequence to the relay;
the relay amplifies the received signals and forwards the signals to all users;
constructing a Tucker-2 tensor model at a user side;
designing a simple and feasible iterative algorithm fitting tensor model to realize the joint estimation of the channel matrix;
the CRB of the proposed method is derived.
Further, the establishment of the multi-user large-scale MIMO relay system model specifically includes:
the present invention considers a multi-user MIMO relay system in which I users exchange information by means of a relay, as shown in fig. 1. Per user configuration MSAntenna, relay node configuration MRAn antenna. Subject to independent same distribution CN (0, eta)ri) Is/are as follows
Figure BDA0002727064920000021
Is a channel matrix from user to relay, obeying independent and same distribution CN (0, eta)ir) Is/are as follows
Figure BDA0002727064920000022
Is the channel matrix from the relay to user I (I1.., I), all nodes in the system operate in half-duplex mode.
Further, each ue sends an orthogonal channel training sequence to the relay, including:
Figure BDA0002727064920000023
are sent by users respectively, and
Figure BDA0002727064920000024
t represents the length of the training sequence. All signals received by the relay node are stored in the third-order tensor
Figure BDA0002727064920000025
In (1).
Relaying the received signal
Figure BDA0002727064920000026
Comprises the following steps:
Figure BDA0002727064920000027
wherein ,
Figure BDA0002727064920000028
is complex gaussian noise subject to independent co-distributed zero mean and unit variance at the relay.
Figure BDA0002727064920000029
Is the noise tensor at the relay
Figure BDA00027270649200000210
The ith slice of (1).
Figure BDA00027270649200000211
Is the ith slice of the received signal tensor.
Further, the relay amplifies and forwards the received signal to all users, including:
the relay amplifies the received signals and forwards the signals to each user respectively. Signals received by the ith user
Figure BDA00027270649200000212
Expressed as:
Figure BDA00027270649200000213
wherein ,
Figure BDA00027270649200000214
is the tensor
Figure BDA00027270649200000215
The ith slice of (1).
Figure BDA00027270649200000216
And
Figure BDA00027270649200000217
respectively the noise tensor
Figure BDA0002727064920000031
And the tensor of the signal received at user i
Figure BDA0002727064920000032
The matrix slice of (2).
Further, constructing a Tucker-2 tensor model at the user terminal, comprising:
at the user end, multiplying both sides of the received signal by S simultaneouslyj HThe following can be obtained:
Figure BDA0002727064920000033
wherein
Figure BDA0002727064920000034
By definition
Figure BDA0002727064920000035
Hi＝[Hr1,...,HrI]
Figure BDA0002727064920000036
Can obtain
Figure BDA0002727064920000037
According to the Tucker-2 decomposition characteristics, a compact form can be obtained:
Figure BDA0002727064920000038
Figure BDA0002727064920000039
wherein
Figure BDA00027270649200000310
Figure BDA00027270649200000311
Further, designing a simple and feasible iterative algorithm fitting tensor model to realize joint estimation of the channel matrix comprises:
Figure BDA00027270649200000312
is LS fitted to
Figure BDA00027270649200000313
So HiIs
Figure BDA0002727064920000041
wherein
Figure BDA0002727064920000042
Is the last iteration HirAn estimate of (d).
Figure BDA0002727064920000043
LS fitting and H ofirIs
Figure BDA0002727064920000044
The cost function of the ith iteration of the algorithm is
Figure BDA0002727064920000045
Alternately updating H with LSi and Hir. And repeating iteration until the epsilon is less than or equal to the epsilon in the range of | delta (i) -delta (i-1) | and meets the convergence condition.
Further, deriving the CRB of the proposed method comprises:
unbiased estimation of the parameter vector is defined as θ
Figure BDA0002727064920000046
wherein ,
Figure BDA0002727064920000047
covariance matrix of unbiased estimate satisfies
Figure BDA0002727064920000048
The Fisher information matrix F (theta) can be expressed as
Figure BDA0002727064920000049
Where J is the erasure matrix for deleting the rows and columns corresponding to the fixed parameters. Of vectorised versions of noise components
The covariance matrix can be expressed as
Figure BDA00027270649200000410
Matrix gamma(i)Can be written as
Figure BDA00027270649200000411
When the signal-to-noise ratio is high, the first term of F (theta) is more dominant than the second term, so that
Figure BDA0002727064920000051
Drawings
FIG. 1 is a flow chart of a channel estimation method of the present invention;
FIG. 2 is a schematic diagram of a multi-user massive MIMO relay system according to the present invention;
FIG. 3 is a diagram of the channel estimation performance of the present invention for different numbers of user antennas;
FIG. 4 is a diagram of the channel estimation performance of the present invention at different numbers of relay antennas;
Detailed Description
The present invention will be described in detail with reference to the attached drawings in order to make the features and advantages of the invention more comprehensible.
FIG. 2 is a schematic diagram of a multi-user massive MIMO relay system according to the present invention, such as the massive MIMO relay shown in FIG. 2, in which I users exchange information through the relay, and each user and the relay are respectively configured with MS and MRA root antenna. All nodes in the cooperative communication system operate in a half duplex (TDD) manner.
Example of implementation
Referring to fig. 3, fig. 3 is a diagram illustrating the performance of channel estimation for different numbers of user antennas according to the present invention. The system parameters are: t100, L100, relay antenna 64. As can be seen from fig. 3, the channel H is estimated as the signal-to-noise ratio increasesi and HirGradually decreases in NMSE. FIG. 3 also shows that with MSThe estimated channel NMSE is also reduced, improving the estimation performance of the method and being closer to the CRB.
Example two
Referring to fig. 4, fig. 4 is a diagram illustrating channel estimation performance under different numbers of relay antennas according to the present invention. The system parameters are: t100, L100, user antenna number MSIs 4. Fig. 4 shows that the NMSE of the estimated channel increases and the channel estimation performance of the method decreases as the number of relay antennas increases. This is because the number of relay antennas increases, and when other conditions remain unchanged, the estimated channel parameters need to increase. However, as can be seen from fig. 4, when the number of relay antennas is 48, the proposed channel estimation method has high channel estimation accuracy even if channel parameters required to be estimated increase. When the number of the relay antennas is small, the channel estimation result is closer to the CRB.
In summary, the present invention can provide any user with the full knowledge of all channel matrices in the considered communication network for channel estimation of multi-user massive MIMO relay system.
The above description of the embodiments is only intended to facilitate the understanding of the method of the present invention and its main idea. The content of the present specification should not be limited to the scope of the present invention, and therefore, the scope of the present invention should be determined by the appended claims.

Claims (7)

1. The joint channel estimation technology of the multi-user large-scale MIMO relay network is characterized by comprising the following steps:
establishing a multi-user large-scale MIMO relay system model;
each user side sends an orthogonal channel training sequence to the relay;
the relay amplifies the received signals and forwards the signals to all users;
constructing a Tucker-2 tensor model at a user side;
designing a simple and feasible iterative algorithm fitting tensor model to realize the joint estimation of the channel matrix;
the CRB of the proposed method is derived.
2. The joint channel estimation technique for the multi-user massive MIMO relay network according to claim 1, wherein the building of the multi-user massive MIMO relay system model specifically comprises:
i users exchange information by means of a relay, HriIs the channel matrix from user to relay, HirIs the channel matrix from the relay to user i, all nodes in the system operate in half-duplex mode.
3. The joint channel estimation technique for multi-user massive MIMO relay network according to claim 1, wherein each ue transmits an orthogonal channel training sequence to the relay, comprising:
Siare sent by users respectively, and
Figure FDA0002727064910000011
the signals received at the relay are:
Figure FDA0002727064910000012
4. the multi-user massive MIMO relay network joint channel estimation technique of claim 1, wherein relaying amplifies and forwards the received signal to all users comprises:
the signal received by the ith user is expressed as:
Figure FDA0002727064910000013
5. the joint channel estimation technique for the multi-user massive MIMO relay network according to claim 1, wherein constructing a Tucker-2 tensor model at a user end comprises:
at the user, multiplying both sides of the received signal by S simultaneouslyj HTo obtain
Figure FDA0002727064910000014
wherein
Figure FDA0002727064910000015
By definition
Figure FDA0002727064910000021
Hi＝[Hr1,...,HrI]
Figure FDA0002727064910000022
Can obtain the product
Figure FDA0002727064910000023
According to the Tucker-2 decomposition characteristics, a compact form can be obtained:
Figure FDA0002727064910000024
Figure FDA0002727064910000025
wherein
Figure FDA0002727064910000026
Figure FDA0002727064910000027
6. The joint channel estimation technology for the multi-user large-scale MIMO relay network according to claim 1, wherein the design of a simple and feasible iterative algorithm fitting tensor model to realize the joint estimation of the channel matrix comprises the following steps:
Figure FDA0002727064910000028
is LS fitted to
Figure FDA0002727064910000029
So HiIs
Figure FDA00027270649100000210
Figure FDA00027270649100000211
LS fitting and H ofirIs
Figure FDA00027270649100000212
The cost function of the ith iteration of the algorithm is
Figure FDA00027270649100000213
Alternately updating H with LSi and HirAnd repeating iteration until the | delta (i) -delta (i-1) | is less than or equal to epsilon and meets the convergence condition.
7. The joint channel estimation technique for multi-user massive MIMO relay network according to claim 1, wherein deriving the CRB of the proposed method comprises:
unbiased estimation of the parameter vector is defined as θ
Figure FDA0002727064910000031
wherein ,
Figure FDA0002727064910000032
covariance matrix of unbiased estimate satisfies
Figure FDA0002727064910000033
The Fisher information matrix F (theta) can be expressed as
Figure FDA0002727064910000034
The covariance matrix of the vectorized version of the noise component may be expressed as
Figure FDA0002727064910000035
Matrix gamma(i)Can be written as
Figure FDA0002727064910000036
When the signal-to-noise ratio is high, the first term of F (theta) is more dominant than the second term, so that
Figure FDA0002727064910000037
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2017049666A1 (en) * 2015-09-24 2017-03-30 Hong Kong Applied Science and Technology Research Institute Company Limited Method and apparatus of channel estimation in multi-user massive mimo systems
CN107294885A (en) * 2017-07-27 2017-10-24 玉溪师范学院 Allied signal detection and the method for estimation of channel in a kind of MIMO relay system
CN107786474A (en) * 2017-11-02 2018-03-09 中国传媒大学 A kind of channel estimation methods based on the models of Tucker 2 in MIMO relay system
CN107911154A (en) * 2017-11-02 2018-04-13 中国传媒大学 A kind of signal and channel estimation methods based on parallel factor model in decoding forwarding MIMO relay system
CN108111439A (en) * 2017-11-02 2018-06-01 中国传媒大学 A kind of non-iterative channel estimation methods in two-way MIMO relay system
CN110808764A (en) * 2019-10-14 2020-02-18 中国传媒大学 Joint information estimation method in large-scale MIMO relay system

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2017049666A1 (en) * 2015-09-24 2017-03-30 Hong Kong Applied Science and Technology Research Institute Company Limited Method and apparatus of channel estimation in multi-user massive mimo systems
CN107294885A (en) * 2017-07-27 2017-10-24 玉溪师范学院 Allied signal detection and the method for estimation of channel in a kind of MIMO relay system
CN107786474A (en) * 2017-11-02 2018-03-09 中国传媒大学 A kind of channel estimation methods based on the models of Tucker 2 in MIMO relay system
CN107911154A (en) * 2017-11-02 2018-04-13 中国传媒大学 A kind of signal and channel estimation methods based on parallel factor model in decoding forwarding MIMO relay system
CN108111439A (en) * 2017-11-02 2018-06-01 中国传媒大学 A kind of non-iterative channel estimation methods in two-way MIMO relay system
CN110808764A (en) * 2019-10-14 2020-02-18 中国传媒大学 Joint information estimation method in large-scale MIMO relay system

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