CN107294885A - Allied signal detection and the method for estimation of channel in a kind of MIMO relay system - Google Patents
Allied signal detection and the method for estimation of channel in a kind of MIMO relay system Download PDFInfo
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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
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- 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
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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
- H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
- H04L25/03006—Arrangements for removing intersymbol interference
- H04L25/03178—Arrangements involving sequence estimation techniques
- H04L25/03305—Joint sequence estimation and interference removal
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Abstract
The invention discloses allied signal detection in a kind of MIMO relay system and the method for estimation of channel, allied signal detection and the method for estimation of channel are fitted using ALS algorithms to the PARAFAC models constructed in the MIMO relay system；Receiving terminal known extensions code Matrix C, C is generalized circular matrix；For two unknown loading matrixes, each step updates one of matrix, and with initial value of the matrix estimated as matrix to be estimated, alternating iteration renewal is until convergence successively.Compared with traditional channel estimation methods and the existing channel estimation methods based on PARAFAC models, institute's extracting method can effectively estimate signal in the case where that need not send channel training sequence, and estimate the CSI of relay system channel simultaneously.
Description
Technical field
Detected the invention belongs to allied signal in MIMO relay system technical field, more particularly to a kind of MIMO relay system
With the method for estimation of channel.
Background technology
MIMO relaying techniques can expand network coverage face, overcome shadow effect, improve the validity of communication system and reliable
Property turn into current wireless communication field a study hotspot.For MIMO relay system, relaying generally uses amplification forwarding
(AF), decoding forwarding (DF) and compression forwarding (CF) three kinds of strategies, wherein AF strategies have obtained extensive due to realizing simple
Use.The research such as trunk channel capacity analysis of current existing a large amount of relevant MIMO relayings, optimal power allocation and duality point
Analysis etc., these researchs under known accurate CSI condition all assuming that carry out.But in practical communication, the CSI of channel is
It is unknown, it is necessary to be estimated.Traditional channel estimation methods obtain CSI generally by the way of pilot signal transmitted, still
When channel status conversion is very fast, it is necessary to which continually pilot signal transmitted is estimated channel.The frequent transmission of pilot signal
Need to take significant component of frequency spectrum resource, thus reduce the spectrum efficiency of system.3 dimensions of PARAFAC models can
It is combined with the spatial domain in communication, time domain and code domain, without specifying informations such as channel and encoder matrixs, can be achieved with information symbol
Detection.Therefore, PARAFAC models have obtained extensive concern and the research of related scholar.There are some researches show PARAFAC is modeled
Technology, which is applied to MIMO relay system, also can effectively estimate channel, and the mode of channel training sequence is sent using information source, in letter
The egress docking collection of letters is handled, and constructs PARAGAC models, and is fitted the model, so as to realize information source to trunk channel
With the Combined estimator for relaying to stay of two nights channel.Launch the method for 1 secondary channel training using information source, and devise a kind of low complex degree
Algorithm.Compared with the channel estimation methods based on least square, less channel training sequence is required for；There is still a need for sending letter
Road training sequence, the advantage of PARAFAC models is not given full play to.
The content of the invention
It is an object of the invention to provide allied signal detection in a kind of MIMO relay system and the method for estimation of channel, purport
Solving to launch the method that 1 secondary channel is trained using information source, it is still desirable to send channel training sequence, do not give full play to
The advantage of PARAFAC models.
The present invention is achieved in that allied signal detection and the method for estimation of channel in a kind of MIMO relay system, this
Kind of method can exponentially improve the channel capacity of wireless channel under conditions of transmission bandwidth is not increased, therefore under being considered as
One of one Generation Mobile Communication System 4G key technology.Allied signal detection and the estimation side of channel in the MIMO relay system
Method is fitted using ALS algorithms to the PARAFAC models constructed；Receiving terminal known extensions code Matrix C, C is vandermonde square
Battle array；For two unknown loading matrixes, each step updates one of matrix, and the first of matrix to be estimated is used as with the matrix estimated
Initial value, alternating iteration renewal is until convergence, is obtained successively：
Wherein,WithH is represented respectively(SRD)With X estimate, | | | |FRepresent Frobenius norms.
Further, the ALS algorithms realize that step is as follows：
Step one, random initializtion matrixIf δ (0)=∞, i=1；
Step 2, is utilizedAccording to formula
Update matrix X；
Step 3, is utilizedAccording to formulaMore
New matrix H(SRD)；
Step 4, is calculated
Step 5, if | δ (i-1)-δ (i) |/δ (i)≤10-6, then iteration terminate；Otherwise another i=i+1, program is adjusted to step
Two；
Wherein, i represents iterations, due to Matrix C, it is known that estimated matrixWith original matrix H(SRD)、X
Between only exist yardstick and obscure, the yardstick is fuzzy to be eliminated by way of standardization；Now, estimated signal
Further, allied signal detection and the method for estimation of channel utilize KRST and coding pair in the MIMO relay system
The transmission signal of information source end is encoded, and the information symbol matrix that information source is sent isEach of which
Information symbol vector issnMeet power limitation conditionUsingTo each
Information symbol vector snEncoded, then to the signal Ξ s after codingnDiagonalization is carried out, signal can be obtainedFinally to multiplying extended code matrix after signalObtain time diversity, the transmission signal matrix of information source
It is expressed as：
U=diag (Ξ sn)C；
In n-th of information symbol vector, the reception signal of the stay of two nights is：
WhereinFor aggregate channel matrix,To make an uproar
Sound matrix,Diagonalization operation is represented, that is, takes matrix in bracket
Line n element be placed on the diagonal to gained matrix, the other positions element of gained matrix is all 0.
Further, the PARAFAC models are：
Wherein, y=(n, mD, l) with v (n, mD, l) it is respectively three-dimensional matriceWithIn typical case member
Element, x (n, ms)、h(mD,mS) and c (mS, l) it is respectively X, H(SRD)With the corresponding element in C；X、H(SRD)It is PARAFAC models with C
Three loading matrixes, according to PARAFAC model section characteristics；The representation of other two kinds of section matrixes is：
WhereinDefine k(·)K orders are represented, according to
PARAFAC model uniqueness theorems, if：
Then the PARAFAC models, which have, decomposes uniqueness, i.e. estimated matrixWithWith original matrix X, H(SRD)、C
Between only exist that row are fuzzy to be obscured with yardstick.
Another object of the present invention is to provide a kind of detected using allied signal in the MIMO relay system and channel
Method of estimation MIMO relay system.
Allied signal detection and the method for estimation of channel in the MIMO relay system that the present invention is provided, in Unknown Channel CSI
Under conditions of, this method effectively the number of detection and can estimate channel condition information in the stay of two nights.The present invention analyzes system comprehensively
Influence of the parameters to institute's extracting method performance.When extend code length compared with when, signal detection BER performances are close to non-blind Detecting side
The performance of method, the simulating, verifying validity of institute's extracting method.The present invention is directed to MIMO relay system, and information symbol is entered in information source
The signal of reception is forwarded to the stay of two nights using AF modes by row Khatri-Rao Space Time Coding, relaying, at stay of two nights end to being received
Signal constructs PARAFAC models and is fitted the model so as to Combined estimator signal and channel matrix.
Compared with traditional channel estimation methods and the existing channel estimation methods based on PARAFAC models, the side of carrying
Method can effectively estimate signal in the case where that need not send channel training sequence, and estimate relay system channel simultaneously
CSI.
Brief description of the drawings
Fig. 1 is double bounce AFMIMO relay system structural representations provided in an embodiment of the present invention.
Fig. 2 is the BER curve schematic diagram under various information source number of antennas provided in an embodiment of the present invention.
Fig. 3 is the NMSE curve synoptic diagrams under various information source number of antennas provided in an embodiment of the present invention.
Fig. 4 is the BER curve schematic diagram under different relay antenna numbers provided in an embodiment of the present invention.
Fig. 5 is the NMSE curve synoptic diagrams under different relay antenna numbers provided in an embodiment of the present invention.
Fig. 6 is the BER curve schematic diagram under different stay of two nights number of antennas provided in an embodiment of the present invention.
Fig. 7 is the NMSE curve synoptic diagrams under different stay of two nights number of antennas provided in an embodiment of the present invention.
Fig. 8 is that difference provided in an embodiment of the present invention is extended under code length, the BER curve schematic diagram of two methods.
Fig. 9 is that difference provided in an embodiment of the present invention is extended under code length, the NMSE curve synoptic diagrams of two methods.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to embodiments, to the present invention
It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to
Limit the present invention.
The application principle of the present invention is explained in detail below in conjunction with the accompanying drawings.
1st, system model
1.1 double bounce AF are relayed
Fig. 1 is double bounce AFMIMO relay system block diagrams, including an information source, a relaying and an Xinsu；Information source,
M is respectively configured in relaying and Xinsus、MRAnd MDRoot antenna, relaying uses amplification forwarding mode,With
Respectively information source is to the channel matrix for relaying and relaying to the stay of two nights, it is assumed that channel H(SR)And H(RD)All believe for quasistatic flat fading
Road, i.e., within a certain period of time, H(SR)And H(RD)Channel status be it is invariable, the element in channel be all average be 0 variance
For 1 independent identically distributed multiple Gauss stochastic variable.
Communication process in the system between information source and the stay of two nights is two time slots, in the first time slot, and information source sends signalExtremely relay, now the signal of relay receptionFor：
WhereinThe noise vector relayed out for t.
In second time slot, the signal y to reception is relayed(R)(t) it is amplified and forwards a stay of two nights, the now stay of two nights connects
The signal of receiptsFor:
y(D)(t+1)=H(RD)Fy(R)(t)+V(D)(t+1)=
H(RD)FH(SR)u(t)+H(RD)Fv(R)(t)+V(D)(t+1) (2)
Wherein,Amplification matrix is relayed to be diagonal,WithRespectively represent relaying and
Noise matrix at the stay of two nights, V(R)And V(D)In element be all that average is that the independent identically distributed multiple Gauss that 0 variance is 1 becomes at random
Amount.
1.2 signals are constructed
The present invention is encoded using KRST with encoding to the transmission signal of information source end, it is assumed that the information symbol that information source is sent
Matrix isEach of which information symbol vector issnMeet power limit
Condition processedIn order to obtain diversity purpose, use firstTo each information symbol vector snCompiled
Code, then to the signal Ξ s after codingnDiagonalization is carried out, signal can be obtainedFinally to multiplying extension after signal
Code matrixIts purpose is to obtain time diversity, now, the transmission signal matrix of information source is represented by：
U=diag (Ξ sn)C (4)
From formula (3), for n-th of information symbol vector, the reception signal of the stay of two nights is：
WhereinFor aggregate channel matrix,To make an uproar
Sound matrix,Dn() represents diagonalization operation, that is, takes matrix in bracket
Line n element be placed on the diagonal to gained matrix, the other positions element of gained matrix is all 0.
2 PARAFAC models and its decomposition uniqueness
DefinitionThe matrix that all N songs information symbol vectors are constituted is received by the stay of two nights, according to formula
(5) it can obtain：
Khatri-Rao products are wherein represented,Formula (6) can be modeled as following PARAFAC
Master pattern：
Wherein, y=(n, mD, l) with v (n, mD, l) it is respectively three-dimensional matriceWithIn typical case member
Element, x (n, ms)、h(mD,mS) and c (mS, l) it is respectively X, H(SRD)With the corresponding element in C.X、H(SRD)It is the PARAFAC moulds with C
Three loading matrixes of type, according to PARAFAC model section characteristics, the representation of other two kinds of section matrixes of formula (7) is：
WhereinDefine k(·)K orders are represented, according to
PARAFAC model uniqueness theorems, if：
Then the PARAFAC models, which have, decomposes uniqueness, i.e. estimated matrixWithWith original matrix X, H(SRD)、C
Between only exist that row are fuzzy to be obscured with yardstick.
3rd, the design of ALS receiving algorithms
Because ALS algorithms are realized simply, and fitting precision is higher, therefore is widely used in being fitted tensor model, the present invention
The PARAFAC models constructed are fitted using ALS algorithms, it is assumed that receiving terminal known extensions code Matrix C, C is vandermonde
Matrix, the general principle of designed ALS algorithms is, for two unknown loading matrixes, and each step updates one of matrix, uses
The matrix estimated is as the initial value of matrix to be estimated, and alternating iteration renewal successively, can using formula (8) and formula (9) up to convergence
：
Wherein,WithH is represented respectively(SRD)With X estimate, | | | |FFrobenius norms are represented, ALS is calculated
Method realizes that step is as follows：
Step 1), random initializtion matrixIf δ (0)=∞, i=1；
Step 2), utilizeMatrix X is updated according to formula (12)；
Step 3) utilizeMatrix H is updated according to formula (11)(SRD)；
Step 4) calculate
Step 5) if | δ (i-1)-δ (i) |/δ (i)≤10-6, then iteration terminate；Otherwise another i=i+1, program is adjusted to step
2)。
Wherein, i represents iterations, due to Matrix C, it is known that estimated matrixWith original matrix H(SRD)、X
Between only exist yardstick and obscure, the yardstick is fuzzy to be eliminated by way of standardization.Now, estimated signal
The application effect of the present invention is explained in detail with reference to simulation analysis.
The performance of institute's extracting method is verified using Computer Simulation, the performance of institute's extracting method by signal detection mistake ratio
Special rate (BER) performance and the estimated accuracy of channel are weighed, and are sent signal and are used QPSK modulation systems, aggregate channel H(SRD)'s
Estimated accuracy is indicated by normalized mean squared error (NMSE)：
Wherein, Z represents Monte Carlo simulation number of times, H(SRD),ZWithIt is illustrated respectively in the z times Monte Carlo simulation
The actual value and estimate of middle channel matrix, below all simulation results gained is all averaging by Monte Carlo simulation 5000 times,
That is Z=5000.Assuming that information source transmitting signal to noise ratio (SNR) is equal with repeat transmitted signal to noise ratio, separately below to various information source antenna
The performance of institute's extracting method is emulated and analyzed under number, relay antenna number, stay of two nights number of antennas and extension code length, and
Compared with non-blind signal detecting method.
The influence of 1 information source number of antennas
First, various information source number of antennas (M is investigatedS=2, MS=4 and MS=influence 6) to carried algorithm performance.Fig. 2
It has been shown that, with SNR increase, the BER of institute's extracting method signal detection is gradually reduced；But when the increase of information source number of antennas, carried
The BER of method is consequently increased, because in institute's extracting method, the number of data flow is equal to information source number of antennas, therefore works as
When other conditions are constant, if information source number of antennas increase, the number of signal data stream is consequently increased, the difficulty phase of signal detection
It is big to becoming.Fig. 3 is given under various information source number of antennas, the channel NMSE performance curves of institute's extracting method.It is consistent with Fig. 2, channel
NMSE reduced with SNR increase, but increase with the increase of information source number of antennas.
The influence of 2 relay antenna numbers
Secondly, different relay antenna number (M are investigatedR=2, MR=4 and MR=influence 6) to carried algorithm performance.Fig. 4
It has been shown that, with the increase of relay antenna number, the BER for putting forward algorithm signal detection is gradually reduced, and this is due to work as relay antenna
When number increases, system obtains more diversity, therefore the signal detection performance of institute's extracting method is also better.Fig. 5 is shown, in
After the increase of number of antennas, although the channel NMSE of institute's extracting method has reduced, the amplitude of reduction is not obvious, especially
For MR=4 and MR=6 both situations.This is due to increasing for relay antenna number, although obtain more diversity, still
Need estimation information source also gradually increase with relaying to the parameter of stay of two nights channel to trunk channel, therefore channel estimation performance simultaneously
It is not significantly improved.
The influence of 3 stay of two nights number of antennas
Again, different stay of two nights number of antennas (M are investigatedD=2, MD=4 and MD=influence 6) to carried algorithm performance.Fig. 6
Show with Fig. 7, with the increase of stay of two nights number of antennas, the BER and channel NMSE of institute's extracting method signal detection accordingly reduce.
Because when stay of two nights number of antennas increase, system obtains more reception diversity, therefore, with more preferable BER performances, and
And the fitting precision of ALS algorithms also obtains raising by a relatively large margin；Because stay of two nights number of antennas increases the fitting for causing ALS algorithms
Precision obtains raising by a relatively large margin, although the increase of stay of two nights number of antennas can cause channel estimation parameter to increase, but simply only
Only so that relay to the stay of two nights channel parameter increase, therefore channel NMSE still can with stay of two nights number of antennas increase and present compared with
To be obviously reduced.
The influence of 4 extension code lengths
Finally, influence of the different extension code lengths (L=4, L=5, L=6 and L=7) to proposed algorithm performance is investigated, and
It is compared with non-blind signal detecting method, the non-blind signal detecting method assumes that the CSI of channel is accurately known.Due to non-fanaticism
The CSI of number detection method is, it is known that the NMSE perseverances of channel are zero, therefore Fig. 9 does not provide its corresponding NMSE curve.Fig. 8 and Fig. 9
Show, with the increase of extension code length, the BER and channel NMSE of institute's extracting method signal detection accordingly reduce.This be by
In the increase of extension code length the time diversity that system is obtained also accordingly is increased, when other conditions are constant, institute's extracting method
Signal detection increase therewith with channel estimating performance.Fig. 8 is also shown that (L=7), institute's extracting method when extension code length is larger
BER performances close to non-blind checking method.For example, in 3 BER=10-3When, institute's extracting method only has with non-blind checking method
0.7dB or so gap.
The present invention is directed to MIMO AF relay systems, and the present invention proposes a kind of union of symbol and channel estimation methods.Not
Under conditions of knowing channel CSI, this method effectively the number of detection and can estimate channel condition information in the stay of two nights.It is of the invention to divide comprehensively
Influence of the system parameters to institute's extracting method performance is analysed.When extend code length compared with when, signal detection BER performances are close to non-
The performance of blind checking method, the simulating, verifying validity of institute's extracting method.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
Any modifications, equivalent substitutions and improvements made within refreshing and principle etc., should be included in the scope of the protection.
Claims (5)
1. allied signal detection and the method for estimation of channel in a kind of MIMO relay system, it is characterised in that the MIMO relayings
Allied signal detection and the method for estimation of channel are fitted using ALS algorithms to the PARAFAC models constructed in system；Connect
Receiving end known extensions code Matrix C, C is generalized circular matrix；For two unknown loading matrixes, each step updates one of square
Battle array, with initial value of the matrix estimated as matrix to be estimated, alternating iteration renewal is until convergence, is obtained successively：
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Wherein,WithH is represented respectively(SRD)With X estimate, | | | |FRepresent Frobenius norms.
2. allied signal detection and the method for estimation of channel in MIMO relay system as claimed in claim 1, it is characterised in that
The ALS algorithms realize that step is as follows：
Step one, random initializtion matrixIf δ (0)=∞, i=1；
Step 2, is utilizedAccording to formula
Update matrix X；
Step 3, is utilizedAccording to formulaMore
New matrix H(SRD)；
Step 4, is calculated
Step 5, if | δ (i-1)-δ (i) |/δ (i)≤10-6, then iteration terminate；Otherwise another i=i+1, program is adjusted to step 2；
Wherein, i represents iterations, due to Matrix C, it is known that estimated matrixWith original matrix H(SRD), between X
Only exist yardstick to obscure, the yardstick is fuzzy to be eliminated by way of standardization；Now, estimated signal
3. allied signal detection and the method for estimation of channel in MIMO relay system as claimed in claim 1, it is characterised in that
Allied signal detection and the method for estimation of channel are believed the transmission of information source end using KRST with coding in the MIMO relay system
Number encoded, the information symbol matrix that information source is sent isEach of which information symbol vector issnMeet power limitation conditionUsingTo each information symbol vector sn
Encoded, then to the signal Ξ s after codingnDiagonalization is carried out, signal can be obtainedFinally to signal after
Multiply extended code matrixTime diversity is obtained, the transmission signal matrix of information source is expressed as：
U=diag (Ξ sn)C；
In n-th of information symbol vector, the reception signal of the stay of two nights is：
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<mo>=</mo>
<msub>
<mi>H</mi>
<mrow>
<mo>(</mo>
<mi>S</mi>
<mi>R</mi>
<mi>D</mi>
<mo>)</mo>
</mrow>
</msub>
<msub>
<mi>D</mi>
<mi>n</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>X</mi>
<mo>)</mo>
</mrow>
<mi>C</mi>
<mo>+</mo>
<msub>
<mi>V</mi>
<mi>n</mi>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>;</mo>
</mrow>
1
WhereinFor aggregate channel matrix,For noise square
Battle array,Dn() represents diagonalization operation, that is, takes n-th of matrix in bracket
Row element is placed on the diagonal to gained matrix, and the other positions element of gained matrix is all 0.
4. allied signal detection and the method for estimation of channel in MIMO relay system as claimed in claim 1, it is characterised in that
The PARAFAC models are：
<mrow>
<mi>y</mi>
<mo>=</mo>
<mrow>
<mo>(</mo>
<mi>n</mi>
<mo>,</mo>
<msub>
<mi>m</mi>
<mi>D</mi>
</msub>
<mo>,</mo>
<mi>l</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<msub>
<mi>m</mi>
<mi>s</mi>
</msub>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>M</mi>
<mi>S</mi>
</msub>
</munderover>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>n</mi>
<mo>,</mo>
<msub>
<mi>m</mi>
<mi>S</mi>
</msub>
<mo>)</mo>
</mrow>
<msub>
<mi>h</mi>
<mrow>
<mo>(</mo>
<mi>S</mi>
<mi>R</mi>
<mi>D</mi>
<mo>)</mo>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>m</mi>
<mi>D</mi>
</msub>
<mo>,</mo>
<msub>
<mi>m</mi>
<mi>S</mi>
</msub>
<mo>)</mo>
</mrow>
<mi>c</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>m</mi>
<mi>S</mi>
</msub>
<mo>,</mo>
<mi>l</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mi>v</mi>
<mrow>
<mo>(</mo>
<mi>n</mi>
<mo>,</mo>
<msub>
<mi>m</mi>
<mi>D</mi>
</msub>
<mo>,</mo>
<mi>l</mi>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
Wherein, y=(n, mD, l) with v (n, mD, l) it is respectively three-dimensional matriceWithIn typical element, x
(n,ms)、h(mD,mS) and c (mS, l) it is respectively X, H(SRD)With the corresponding element in C；X、H(SRD)With three that C is PARAFAC models
Individual loading matrix, according to PARAFAC model section characteristics；The representation of other two kinds of section matrixes is：
<mrow>
<msup>
<mi>Y</mi>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</msup>
<mo>=</mo>
<mrow>
<mo>(</mo>
<msup>
<mi>C</mi>
<mi>T</mi>
</msup>
<mi>X</mi>
<mo>)</mo>
</mrow>
<msubsup>
<mi>H</mi>
<mrow>
<mo>(</mo>
<mi>S</mi>
<mi>R</mi>
<mi>D</mi>
<mo>)</mo>
</mrow>
<mi>T</mi>
</msubsup>
<mo>+</mo>
<msup>
<mi>V</mi>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</msup>
<mo>;</mo>
</mrow>
Y(2)=(HSRDCT)XT+V(2)；
WhereinDefine k(·)K orders are represented, according to
PARAFAC model uniqueness theorems, if：
<mrow>
<msub>
<mi>k</mi>
<mi>X</mi>
</msub>
<mo>+</mo>
<msub>
<mi>k</mi>
<msub>
<mi>H</mi>
<mrow>
<mo>(</mo>
<mi>S</mi>
<mi>R</mi>
<mi>D</mi>
<mo>)</mo>
</mrow>
</msub>
</msub>
<mo>+</mo>
<msub>
<mi>k</mi>
<mi>C</mi>
</msub>
<mo>&GreaterEqual;</mo>
<mn>2</mn>
<msub>
<mi>M</mi>
<mi>S</mi>
</msub>
<mo>+</mo>
<mn>1</mn>
<mo>;</mo>
</mrow>
Then the PARAFAC models, which have, decomposes uniqueness, i.e. estimated matrixWithWith original matrix X, H(SRD), between C
Only exist row fuzzy fuzzy with yardstick.
5. allied signal detection and the estimation of channel in MIMO relay system described in a kind of any one of utilization Claims 1 to 44
The MIMO relay system of method.
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| CN107863998A (en) * | 2017-11-02 | 2018-03-30 | 中国传媒大学 | A kind of allied signal detection and channel estimation methods based on multiuser 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 |
| CN112491752A (en) * | 2020-10-16 | 2021-03-12 | 中国传媒大学 | Multi-user large-scale MIMO relay network joint channel estimation technology |
| CN114172546A (en) * | 2021-12-10 | 2022-03-11 | 中国传媒大学 | Multi-parameter iterative estimation method in RIS auxiliary MIMO system |
| CN114710381A (en) * | 2022-04-01 | 2022-07-05 | 中国人民解放军国防科技大学 | Channel capacity estimation method, device, equipment and medium |
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107863998A (en) * | 2017-11-02 | 2018-03-30 | 中国传媒大学 | A kind of allied signal detection and channel estimation methods based on multiuser 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 |
| CN108111439B (en) * | 2017-11-02 | 2022-03-08 | 中国传媒大学 | Non-iterative channel estimation method in bidirectional 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 |
| CN110808764B (en) * | 2019-10-14 | 2023-02-24 | 中国传媒大学 | Joint information estimation method for large-scale MIMO relay system |
| CN112491752A (en) * | 2020-10-16 | 2021-03-12 | 中国传媒大学 | Multi-user large-scale MIMO relay network joint channel estimation technology |
| CN112491752B (en) * | 2020-10-16 | 2023-09-05 | 中国传媒大学 | Multi-user large-scale MIMO relay network joint channel estimation method |
| CN114172546A (en) * | 2021-12-10 | 2022-03-11 | 中国传媒大学 | Multi-parameter iterative estimation method in RIS auxiliary MIMO system |
| CN114710381A (en) * | 2022-04-01 | 2022-07-05 | 中国人民解放军国防科技大学 | Channel capacity estimation method, device, equipment and medium |
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