CN1604512A - Joint detection method and device - Google Patents

Abstract

Each antenna unit in the receiving antenna array receives all the information of multiple users, and then converts the radio frequency signal into a baseband signal through the down converter, and the analog-to-digital converter converts the analog signal into a digital signal with a certain precision, and outputs The data sequence and the training sequence are separated by the signal splitter, the training sequence is used to estimate the channel impulse response, and the two parts of the data sequence separated by the training sequence are injected into the joint detector at the same time, and the same correlation matrix is used to estimate the data symbols sent by the user, After the estimation result is processed by the corresponding channel decoder, the performance of the joint detection device is evaluated by comparing with the transmitted data symbols.

Description

Associated detecting method and device

Technical field

The present invention is applied to wireless communication field, the multi-user comm that particularly divides multiple access based on direct sequence spread spectrum codes, associated detecting method that is specifically related to adopt in the mobile communication system and device, this method and apparatus can be applicable to the travelling carriage in the mobile communication system or the receiver of base station, is used for detecting simultaneously the data symbol that same sub-district different user that same transmission unit comprises sends.

Background technology

Divide in the system of multiple access (DS-CDMA:Direct Sequence-CodeDivision Multiple Access) technology in the application direct sequence spread spectrum codes, the user of same sub-district identifies with specific user code, is quadrature between the transmitting terminal user code.Because the influence of multipath fading, the orthogonality between the user code is destroyed, thereby causes receiving terminal to have intersymbol interference, and promptly so-called multiple access disturbs (MAI:Multi Access Interference).Traditional receiver disturbs multiple access regards noise as, causes receiving terminal signal interference ratio (power ratio of signal and interference) to reduce, and this is equivalent to the capacity that has reduced system; And multiuser detection is handled the multiple access interference according to useful information, is equivalent to the interference that has reduced between the user like this, has improved the signal interference ratio of receiving terminal.Joint-detection (JD:JointDetection) specially refers to be applied to the multiuser detection of Time Division-Code Division Multiple Access (TD-CDMA:Time Division-CDMA) system, and (The 3 as 3GPP ^RdGeneration Partnership Project) standardized TDS-CDMA system (TD-SCDMA:Time Division-Synchronous CDMA) based on time-multiplexed broadband CDMA system (UTRA-TDD:(UMTSterrestrial radio access-Time Division Duplex) and China Wireless Telecommunication Standar group (CWTS:China Wireless Telecommunications Standard Committee) suggestion.

In the TD-CDMA system, JD is a kind of key technology that is used for the expanding system capacity.The multiple access that it not only can be eliminated in the same sub-district disturbs, and can also eliminate same user's intersymbol interference.The nineties in 20th century, the basic principle about JD the earliest obtains embodying in document [1], and it has disclosed the dual-use function that JD had, and then A.Klein and P.W.Baier improves and with this technological expansion [2] in the TD-CDMA system.Now, the JD technology mainly contains 4 kinds of methods, be ZF piece linear equalization (ZF-BLE:Zero Forcing Block Linear Equalizer), ZF piece decision feedback equalization (ZF-BDFE:Zero Forcing Block Decision Feedback Equalizer), least mean-square error piece linear equalization (MMSE-BLE:Minimum Mean Square Error BlockLinear Equalizer), least mean-square error piece decision feedback equalization (MMSE-BDFE:MinimumMean Square Error Block Decision Feedback Equalizer), but detailed description list of references [3] and U.S. Pat 5 about these 4 kinds of methods, 970,060 A and US 6,307,851 B1.

The bottleneck that JD uses can reduce a problem, and promptly compute matrix is contrary.The contrary complexity of compute matrix is along with the different of the feature of the dimension of matrix and matrix and obey different rules, as index, square, logarithm etc.Certain methods is arranged at present, approach Qiao Lisiji (Approximate Cholesky) decomposition method as what document [4] proposed, general Shu Er (GeneralizedSchur) decomposition method that document [5] proposes, these methods are directly at the calculated in time domain matrix inversion.Document [6] has been advised another method at the frequency-domain calculations matrix inversion.In these methods, it is minimum to approach Qiao Lisiji decomposition method complexity.When yet these methods were applied to UTRA-TDD and TD-SCDMA system, complexity was still very high.The present invention has taken into full account the characteristic [7] of structuring matrix in joint-detection, has provided a kind of associated detecting method and device of new low complex degree in conjunction with Kumar algorithm [8].

United States Patent (USP)

5970060?A 10/1999?Baier?et?al...................................370/342

6307851?B1?10/2001?Jung?et?al.....................................370/342

6339612?B1?1/2002?Stewart?et?al.....................................375/140

Other publication

(1)Z.H.Xie，R.T.Short，and?C.K.Rushforth，“A?family?of?suboptimumdetectors?for?coherent?multiuser?communications，”IEEE?JSAC，Vol.8，No.4，May?1990，pp.683-690.

(2)A.Klein，“Linear?unbiased?data?estimation?in?mobile?radio?systemsapplying?CDMA，”IEEE?JSAC，Vol.11，No.7，Sept.1993，pp.1058-1066.

(3)P.Jung，J.Blanz，M.Naβhan，P.W.Baier，“Simulation?of?the?uplinkof?JD?CDMA?mobile?radio?systems?with?coherent?receiver?antenna?diversity，”Wireless?Personal?Communication，vol.1，1994，pp.61-89.

(4)H.R.Karimi，N.W.Anderson，“A?novel?and?efficient?solution?toblock-based?Joint?Detection?using?approximate?cholesky?factorization，”IEEEPIMRC98，Boston，MA，Sept.1998，pp.1340-1345

(5)M.Vollmer，M.Haardt，and?J.Gotze，“Comparative?study?ofJoint-Detection?techniques?for?TD-CDMA?based?mobile?radio?systems，”IEEE?JSAC，Vol.19，No.8，Aug.2001，pp.1461-1475

(6)Nevio?Benvenuto，Giovanna?Sostrato，“Joint?Detection?with?lowcomputational?complexity?for?hybrid?TD-CDMA?systems，”IEEE?JSAC，Vol.19，No.1，Jan.2001，pp.245-253

(7)Victor?Y.Pan，Structured?Matrices?and?Polynomials：UnifiedSuperfast?Algorithm，Springer，New?York，USA，2001.

(8)Rajendra?Kumar，“A?fast?algorithm?for?solving?a?Toeplitz?system?ofequations，”IEEE?Trans?on?ASSP，Vol.ASSP-33，No.1，February?1985，pp.254-267.

Summary of the invention

The present invention can be used for any multi-user comm based on the DS-CDMA technology, particularly UTRA-TDD and TD-SCDMA system.Be intended to further reduce the JD complexity, so that when this technology of application, complexity can be accepted for current digital signal processor spare.

The present invention is used for detecting and estimates the data symbol that the different user from same sub-district that same transmission unit receives sends.Received signal is separated into two parts before using associated detection technique, and a part is the training sequence that comprises channel information, and another part is the data of transmission.Estimate channel impulse response according to training sequence, and set up the particular matrix structure that comprises user profile and channel status according to the diversity number of reception antenna and number of users, and this matrix structure carried out special processing to reduce the complexity of computing, use the data symbol that a kind of fast algorithm estimates that different user sends simultaneously.

According to an aspect of the present invention, a kind of joint-detection device that is applied to direct sequence spread spectrum CDMA multi-antenna diversity received communication system is provided, comprise: the data combiner is used for the data message from different antennae is made up according to the data block in the transmission unit correspondence; Main system matrix maker is used for the user code by calculating each user and the convolution of channel impulse response and obtains the main system matrix; Principal phase is closed the matrix maker, is used for obtaining principal phase by being correlated with of computing main system matrix and closes matrix; Digital matched filter is used for receiving the preliminary treatment before data block is carried out joint-detection; Principal phase is closed the matrix inversion calculator, is used to calculate principal phase and closes the inverse of a matrix matrix; Data estimation is used for estimating data symbol corresponding to different user according to the data behind the digital matched filtering and inverse matrix.

According to another aspect of the present invention, a kind of associated detecting method that is applied to direct sequence spread spectrum CDMA multi-antenna diversity received communication system is provided, may further comprise the steps: will make up according to data block corresponding in transmission unit from the data message of different antennae; Utilize each user's the user code and the convolution of channel impulse response to obtain the main system matrix; Obtain principal phase by being correlated with of computing main system matrix and close matrix; Data block is carried out digital matched filtering; Calculate principal phase and close the inverse of a matrix matrix; Estimate data symbol according to filtered data and inverse matrix corresponding to different user.

According to another aspect of the present invention, provide a kind of direct sequence spread spectrum CDMA multi-antenna diversity received communication system that uses above-mentioned joint-detection device, this system comprises: be used for the independent diversity antenna array that receives the information that sends from all users; Be used for radiofrequency signal is converted into the low-converter of baseband signal; Be used for receiving baseband signal is converted to the analog to digital converter of digital baseband signal with certain dynamic range; Be used for digital baseband signal is divided into the signal splitter of data message and training sequence; Be used for estimating the channel estimator of channel impulse response according to training sequence; Be used for joint-detection device according to the data symbol of channel impulse response and the transmission of data message estimating user; Be used for recovering the channel decoder of the data symbol of transmission according to estimated result.

Description of drawings

Fig. 1 is a structure chart of using the multi-antenna diversity reception wireless communication system of joint-detection.

Fig. 2 is the structure chart of joint-detection device.

Fig. 3 shows the transformat that sends signal.

Fig. 4 is the signal extraction schematic diagram before the joint-detection.

Fig. 5 shows the structural relation between the main matrix in the joint-detection device.

Fig. 6 is the realization flow figure of joint-detection device.

Embodiment

With reference to Fig. 1, in same sub-district, the signal that maximum K different users 101 send by M different wireless channel 102 can be received machine 103 and detect, and the transmission form of signal as shown in Figure 3.The array 104 that M antenna formed receives all K user's signal respectively, according to method shown in Figure 4 by signal splitter 107 extracted data information and training sequence information after, uses the data of K user's transmission of joint-detection device shown in Figure 2 109 estimations.Fig. 6 has provided the realization flow of joint-detection device 109, and the structural relation in implementation procedure between the matrix as shown in Figure 5.

With reference to Fig. 1, each antenna element in the receiving antenna array 104 has received all information from K user respectively, transfer radiofrequency signal to baseband signal by low-converter 105 then, after analog to digital converter 106 was quantified as the digital signal with certain precision with analog signal, the data message of output was used signal splitter 107 with training sequence according to method shown in Figure 4 and is separated.Wherein, training sequence is used to estimate channel impulse response, and this function realizes by channel estimator 108.Two parts data message that training sequence separates injects combined detector 109 simultaneously, and they use the last data message corresponding to K user's transmission of same correlation matrix estimation.After estimated result is handled by corresponding channel decoder 110, by with the performance of relatively estimating the joint-detection device that sends information symbol.

With reference to Fig. 2, main system matrix maker 202 is realized the convolution that the channel impulse of local K user code 201 that generates and channel estimator 108 outputs is rung, formation main system matrix.Channel estimator 108 is used the training sequence of signal splitter 107 outputs and is estimated channel impulse response.Principal phase is closed matrix maker 203 and is used for the relevant of computing system matrix, closes matrix thereby generate principal phase.Matrix inversion 204 mainly is to calculate principal phase to close inverse of a matrix.Data combiner 207 will be from the data message of different antennae (by signal splitter 107 outputs) according to data block combinations corresponding in transmission unit, multiplies each other with the conjugate transpose of main system matrix then and realizes the function of digital matched filtering 206.Data estimation 205 is actual to be a matrix multiplier, and its output is corresponding to the data symbol of the different user of estimating.After cutting apart by simple data, output to the channel decoder 110 of corresponding different user.

With reference to Fig. 3, the sending order of signal is data block 301 successively, and training sequence 302 and data block 303 have one section space not send any useful information between adjacent transmission unit, is called protection interval 304.Because after the user sent the transmission of signal process wireless channel, the signal that antenna receives mutual existence in transmission unit was disturbed, annoyance level can be measured by the memory span W of channel impulse response filter.Before using combined detector, first extracted data information and training sequence, the method for extraction is with reference to Fig. 4.First data sequence that is used for joint-detection is made up of undisturbed data block 401 and interference piece 402, its length adds W-1 for sending data sequence length, training sequence is by disturbing piece 402, undisturbed training piece 403 and interference piece 404 are formed, its length is identical with the transmission training sequence, second data sequence is by disturbing piece 404 and undisturbed data block 405 to form, and its length adds W-1 for sending data sequence length.Protection 406 has been avoided the interference between the data block in adjacent two transmission units at interval.

Fig. 6 shows the flow process of whole joint-detection, data message and training sequence that the expression of the 601st step is extracted from transmission unit shown in Figure 4, training sequence is used for estimating channel impulse response in the 602nd step that user code 603 and channel impulse sound are used at the 604th step computing main system matrix.Next, at first two data blocks are carried out digital matched filtering respectively in the 605th step, ask the relevant corresponding principal phase pass matrix that obtains in the 606th step by the main system matrix, by the corresponding major cycle matrix of the 607th step structure, next cut apart multinomial and calculate principal phase in the 609th step in the 608th step and close inverse of a matrix, the 610th step was realized the estimation to the data symbol.

The matrix that participates in computing among the present invention has certain special construction, and Fig. 5 shows the variation of this structure in the computing.Be sytem matrix 501, correlation matrix 503 and circular matrix 507 from left to right successively.It is that main system matrix 502 is asked and obtained after relevant that principal phase is closed matrix 504 and 505, and matrix 506 is conjugate transposes of matrix 504.Main system matrix 502 is a kind of---Xi Er Vista (Block-Sylvester) matrix promptly is the component of metric unit matrix with the piece, and all nonzero block matrixes all concentrate on the piece leading diagonal.The correlation matrix 503 that they constituted is pieces---Teoplitz (Block-Toeplitz) matrix, all nonzero block all concentrate on the piece leading diagonal with the piece diagonal parallel with it on, and to be distributed in same piece on the diagonal be identical.With correlation matrix 503 relatively, circular matrix 507 is except at the piece back-diagonal with in parallel have on to intersection the nonzero block, its complementary space is zero.

The present invention selects the ZF-BLE algorithm as an example embodiment of low-complexity joint detection method and device to be described, this algorithm can be represented with formula (1).

d＝(A ^HA) ^-1A ^He (1)

Wherein, d uses the data message that K user that combined detector estimates sends, e be M antenna reception corresponding to K user's data information.() ^HThe expression conjugate transpose, () ^-1Representing matrix is contrary.A is a sytem matrix, correlation matrix C A ^HA represents.The contrary of correlation matrix is the main contributor of this algorithm complex.Correlation matrix C at first is a symmetric positive definite matrix, also is a piece---Xi Er Vista matrix has also kept the Teoplitz characteristic of sytem matrix.Thereby these characteristics make matrix can further be developed when computing reduces computation complexity.

Suppose the piece diagonal number that comprises null matrix is defined as the width of block matrix, it is wide to be called for short piece, as the formula (2).

P _cor＝2P _sys+1 (2)

Wherein, P _CorThe piece of expression correlation matrix is wide, the wide P of the piece of sytem matrix _SysThe length that depends on channel impulse response is defined as (3) formula.

Symbol

Expression rounds along negative infinity, and Q is a spreading factor, and W is the length of channel impulse response.

Sytem matrix A is defined as structure shown in (4) formula.

$A=\left[\begin{array}{cccc}{A}_{11}& A_{12}& \&CenterDot;\&CenterDot;\&CenterDot;& A_{1N}\\ A_{21}& A_{22}& \&CenterDot;\&CenterDot;\&CenterDot;& A_{2N}\\ \&CenterDot;& \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;& \&CenterDot;\\ A_{N1}& A_{\mathrm{<figure-callout\; id="2"\; label="N"\; filenames="A0313475700191.png"\; state="\{\{state\}\}">N</figure-callout>}2}& \&CenterDot;\&CenterDot;\&CenterDot;& A_{\mathrm{NN}}\end{array}\right]----\left(4\right)$

Matrix-block A _Ij, i, j=1,2 ..., N is an element of forming sytem matrix A, is defined as formula (5).N is transmission symbolic number that data block is held in the transmission unit.

Wherein, Z represents null matrix.The complexity of computing system matrix A is equivalent to the complexity of computing main system matrix, and the element of main system matrix A is by calculating user code c ^(k)And channel impulse response h ^{(k, kn)}Convolution obtain.The main system matrix A has K _a(Q+W-1) go K row.

$A=\&lsqb;{\mathrm{<figure-callout\; id="1"\; label="A"\; filenames="A0313475700191.png"\; state="\{\{state\}\}">A</figure-callout>}}_1,{\mathrm{<figure-callout\; id="2"\; label="A"\; filenames="A0313475700191.png"\; state="\{\{state\}\}">A</figure-callout>}}_2,\&CenterDot;\&CenterDot;\&CenterDot;,A_{K_n}{\&rsqb;}^{T_b}----\left(6\right)$

$A_{k_a}=c^k\&CircleTimes;h^{(k,k_n)}----\left(7\right)$

Wherein, T _bExpression piece transposition, c ^(k)And h ^{(k, kn)}Be respectively that length is the column vector of Q and W, k=1,2 ..., K is the open ended user's yardage of each transmission unit, k _a=1,2 ..., K _aBe the reception antenna number, _ expression convolution algorithm.

Correlation matrix C has inherited the architectural feature of sytem matrix, is a new piece---Xi Er Vista matrix.Because multiple access disturbs MAI, the adjacent block on the sytem matrix piece leading diagonal (with reference to Fig. 5) interlaced with each other, interleave depth is that W-1 is capable.By formula (2) as can be known, the sparse degree of correlation matrix C is lower than sytem matrix A.

$C=\left[\begin{array}{cccc}{C}_{11}& C_{12}& \&CenterDot;\&CenterDot;\&CenterDot;& C_{1N}\\ C_{21}& C_{22}& \&CenterDot;\&CenterDot;\&CenterDot;& C_{2N}\\ \&CenterDot;& \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;& \&CenterDot;\\ C_{N1}& {\mathrm{<figure-callout\; id="2"\; label="C\; N"\; filenames="A0313475700191.png"\; state="\{\{state\}\}">C</figure-callout>}}_N& \&CenterDot;\&CenterDot;\&CenterDot;& C_{\mathrm{NN}}\end{array}\right]----\left(8\right)$

i＝1，2，…，N，j＝1，2，…，N-1，k＝1，2，…，N，l＝1，2，…，N-1.

If be applied to the TD-SCDMA system, formula (9) can be reduced to formula (10).

C _ij＝Z，|i-j|＞1 (10)

Wherein, | () | the absolute value of expression ().

In fact, it all is the subclass of correlation matrix that any one principal phase is closed matrix, is symmetric positive definite matrix.For fear of high complexity, close matrix with principal phase and replace correlation matrix participation computing will inevitably cause evaluated error.Although this error can be compensated such as channel coding technology by other, how to select principal phase pass matrix still very crucial.

Based on the analysis to the correlation matrix structure, with reference to formula (8), optimum principal phase is closed matrix should be by C _{N, N}, C _{N-1, N}And C _{N, N-1}Form, wherein, the relation between latter two matrix can be defined as (11) formula.

C _N-1，N＝[C _N，N-1] ^H (11)

Wherein, subscript H represents the hermitian transposition.

Formula (12) has defined the structure of principal phase pass Matrix C.

$C=\left[\begin{array}{c}{C}_{N-1,N}\\ C_{N,N}\end{array}\right]----\left(12\right)$

In fact, there is no need to calculate correlation matrix C, only need to calculate principal phase pass Matrix C and get final product according to formula (13) to (15).

$a_{11}=A^H(1:K,1:K_a(Q+W-1)),b_{11}=A(1:\mathrm{Ka}(Q+W-1),1:K)$

$C_{N,N}=a_{11}\&CenterDot;b_{11}\stackrel{\mathrm{def}}{=}C1---\left(13\right)$

$a_{12}=A^H(1:K,K_{a}Q+1:K_a(Q+W-1)),b_{12}=A(1:\mathrm{Ka}(W-1),1-K),$

$C_{N-1,N}=a_{12}\&CenterDot;b_{12}\stackrel{\mathrm{def}}{=}C2----\left(14\right)$

$C_{N,N-1}={\&lsqb;C_{N-1,N}\&rsqb;}^H\stackrel{\mathrm{def}}{=}C3----\left(15\right)$

Wherein, symbol

The expression assignment operation.

Close Matrix C 1 based on principal phase, C2 and C3, dimension are that the structure of circular matrix R of (N+1) * (N+1) is shown in (16) formula.

R _1r＝[C1?C2…C3] (17)

$R_{1c}={\left[\begin{array}{cccc}C1& C3& \&CenterDot;\&CenterDot;\&CenterDot;& C2\end{array}\right]}^{T_b}----\left(18\right)$

Formula (16) is in (18), and horizontal chain line and vertical dotted line are represented null matrix Z, and diagonal dashed lines is represented identical matrix.First line display of circular matrix R is R _1r, first tabulation is shown R _1c, R _1rAnd R _1cThe major cycle matrix that is called circular matrix R, its contrary going on foot according to following 3 calculated.

Use first row of fast fourier transform computation cycles matrix R _1cEigenmatrix λ _1c

λ _1c＝FR _1c (19)

Wherein, F is the Fourier matrix of shape as (20).

Wherein, ω=2 π i/ (N+1), $i=\sqrt{-1},$ The dimension of Fourier matrix F is (N+1) * (N+1).The inverse matrix F of Fourier matrix ^-1Can directly obtain by the inverse that calculates each scaling element among the Fourier matrix F, that is, and F ^-1(i, j)=1/F (i, j), i=1,2 ..., N+1, j=1,2 ..., N+1.

Calculated characteristics matrix λ _1cMuller---(Moore-Penrose) is contrary for Roger Penrose.

η _1c＝λ _1c ⁺ (21)

First capable R of computation cycles matrix _1rMuller---Roger Penrose is contrary.

${\mathrm{CI}}_{R_{1c}}=F\&CenterDot;\mathrm{BlkDiag}\left({\mathrm{\&eta;}}_{1c}\right)\&CenterDot;F^{-1}----\left(22\right)$

Wherein, BlkDiag (η _1c) the expression block diagonal matrix, the matrix on the piece diagonal is η _1cAt last, first row of circular matrix R _1cContrary CI _R1cAs the formula (23).

${\mathrm{CI}}_{R_{1c}}={\&lsqb;{\mathrm{<figure-callout\; id="1"\; label="B"\; filenames="A0313475700191.png"\; state="\{\{state\}\}">B</figure-callout>}}_1,{\mathrm{<figure-callout\; id="2"\; label="B"\; filenames="A0313475700191.png"\; state="\{\{state\}\}">B</figure-callout>}}_2,\&CenterDot;\&CenterDot;\&CenterDot;,B_{N+1}\&rsqb;}^{T_b}----\left(23\right)$

Wherein, B _iBe the block matrix of K * K, i=1,2 ..., N+1.First capable R of circular matrix _1rContrary CI _R1rAs the formula (24).

${\mathrm{CI}}_{R_{1r}}=\&lsqb;{\mathrm{<figure-callout\; id="1"\; label="B"\; filenames="A0313475700191.png"\; state="\{\{state\}\}">B</figure-callout>}}_1,B_{N+1},\&CenterDot;\&CenterDot;\&CenterDot;,{\mathrm{<figure-callout\; id="2"\; label="B"\; filenames="A0313475700191.png"\; state="\{\{state\}\}">B</figure-callout>}}_2\&rsqb;----\left(24\right)$

Obviously, first of circular matrix row and first row are made up of the same matrix piece.Principal phase is closed inverse of a matrix and is calculated according to the major cycle inverse of a matrix, principal phase is closed inverse of a matrix and is used and cut apart the polynomial method of major cycle matrix inversion and calculate, the deconvolution of computing block row matrix and piece column matrix at first, the piece multinomial remainder after the deconvolution are principal phase and close inverse of a matrix.Here, major cycle matrix inversion multinomial is equal to the multinomial of general meaning, just the multinomial of mentioning among the present invention with matrix-block as coefficient, rather than general be the multinomial of coefficient with the scalar.The concrete steps of calculating principal phase pass matrix inversion are as follows:

The upset of major cycle matrix

$B0=\mathrm{Rev}\left({\mathrm{CI}}_{R_{1r}}\right)=\&lsqb;{\mathrm{<figure-callout\; id="2"\; label="B"\; filenames="A0313475700191.png"\; state="\{\{state\}\}">B</figure-callout>}}_2,B_3,\&CenterDot;\&CenterDot;\&CenterDot;,B_{N+1},{\mathrm{<figure-callout\; id="1"\; label="B"\; filenames="A0313475700191.png"\; state="\{\{state\}\}">B</figure-callout>}}_1\&rsqb;----\left(25\right)$

$\mathrm{BI}=\mathrm{Rev}\left(C{I}_{R_{1c}}\right)={\&lsqb;B_{N+1},B_N,\&CenterDot;\&CenterDot;\&CenterDot;,{\mathrm{<figure-callout\; id="2"\; label="B"\; filenames="A0313475700191.png"\; state="\{\{state\}\}">B</figure-callout>}}_2,{\mathrm{<figure-callout\; id="1"\; label="B"\; filenames="A0313475700191.png"\; state="\{\{state\}\}">B</figure-callout>}}_1\&rsqb;}^{T_b}----\left(28\right)$

Wherein, Rev () is a piece upset operator, definition in formula (25)-(26).Major cycle matrix-block uncoiling

E＝B0-F ^-1{[inv(F·B1)·(F·B0)]·(F·B1)} (27)

Calculating matrix inversion principal phase pass, principal phase pass inverse of a matrix G can obtain by flipping block multinomial remainder E.

$G=\mathrm{Rev}\left(E\right)=\&lsqb;{\mathrm{<figure-callout\; id="1"\; label="S"\; filenames="A0313475700191.png"\; state="\{\{state\}\}">S</figure-callout>}}_1,{\mathrm{<figure-callout\; id="2"\; label="S"\; filenames="A0313475700191.png"\; state="\{\{state\}\}">S</figure-callout>}}_2,\&CenterDot;\&CenterDot;\&CenterDot;,S_{P_{\mathrm{cor}}}\&rsqb;----\left(28\right)$

The inverse matrix of correlation matrix can close inverse of a matrix G with principal phase constructs.Although correlation matrix contrary is not piece---the toeplitz matrix on the strict mathematical meaning, on the joint-detection problem, can be similar to and regards piece as---toeplitz matrix.The wide P of the piece of inverse correlation matrix _CiThe piece that is different from correlation matrix is wide, P _CiBe defined as following formula (29).

P _ci＝2P _cor ^-1 (29)

P wherein _CorDefinition in formula (2).

Among the present invention, the inverse matrix of correlation matrix is that principal phase is closed matrix and its conjugation realizes by rearranging, and this process relates to arithmetical operation hardly, so to not obviously influence of computation complexity.

Formula (30) has shown correlation matrix inverse matrix C ^-1Structure, white space is represented null matrix, C ^-1Be exactly (A ^HA) ^-1

Next, finish last data estimation according to formula (1).Wherein, by calculating A ^HE realizes the data preliminary treatment before the joint-detection, promptly so-called digital matched filtering.It is different from disturbs multiple access and traditional matched filter of noise processed is used as in intersymbol interference, but owing to sytem matrix A is the convolution of user code and channel impulse response, so digital matched filtering has comprised the function of matched filtering.

Claims (26)

1. A joint detection device applied to a direct sequence spread spectrum CDMA multi-antenna diversity reception communication system, comprising: a data combiner, configured to combine data information from different antennas according to corresponding data blocks in the transmission unit; The main system matrix generator is used to obtain the main system matrix by calculating the convolution of the user code and the channel impulse response of each user; A main correlation matrix generator for obtaining a main correlation matrix by computing the correlation of the main system matrix; A digital matched filter is used for preprocessing the received data block before joint detection; Main correlation matrix inverse calculator, used to calculate the inverse matrix of the main correlation matrix; The data estimator is used for estimating the data symbols corresponding to different users according to the digital matched filtered data and the inverse matrix. 2. The joint detection device according to claim 1, wherein the data information from different antennas combined by the data combiner is a spread-spectrum data stream. 3. The joint detection device according to claim 2, wherein the data combiner is a device for sequentially concatenating data information from different antennas without destroying received data information. 4. The joint detection device according to claim 1, wherein the main system matrix is a matrix conforming to the property of the block-Sylvester structure. 5. The joint detection device according to claim 1, wherein the main system matrix has K _a (Q+W-1) rows and K columns, expressed as $\mathrm{<span\; class="notranslate">\; A</span>}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; [</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}}<span\; class="notranslate">\; ]</span>}^{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}}$ ${\mathrm{<span\; class="notranslate">\; A</span>}}_{{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; c</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \&CircleTimes;</span>{\mathrm{<span\; class="notranslate">\; h</span>}}^{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; )</span>}$ Among them, A represents the main system matrix, T _b represents the block transposition, c ^(k) and h ^{(k, ka)} are the user code vector of length Q and the channel impulse response vector of length W, k=1, 2, ..., K is the number of user codes that each transmission unit can accommodate, k _a =1, 2, ..., K _a is the number of receiving antennas, Q is the spreading factor, W is the length of the channel impulse response, _ means convolution operation. 6. The joint detection device according to claim 1, wherein the main correlation matrix is made up of 3 different matrices C _{N, N} , C _{N-1, N} and C _{N, N-1} , as the following formula ${\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; N</span>}}\stackrel{\mathrm{<span\; class="notranslate">\; def</span>}}{<span\; class="notranslate">\; =</span>}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 11</span>}}<span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 11</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; N</span>}}\stackrel{\mathrm{<span\; class="notranslate">\; def</span>}}{<span\; class="notranslate">\; =</span>}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 12</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 12</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}\stackrel{\mathrm{<span\; class="notranslate">\; def</span>}}{<span\; class="notranslate">\; =</span>}{<span\; class="notranslate">\; [</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; ]</span>}^{\mathrm{<span\; class="notranslate">\; h</span>}}$ As shown, where, a ₁₁ ＝A ^H (1:K, 1:K _a (Q+W-1)), b ₁₁ ＝A(1:Ka(Q+W-1), 1:K) a ₁₂ =A ^H (1:K,K _a Q+1: _{K a} (Q+W-1)), b ₁₂ =A(1:Ka(W-1),1:K), The superscript H indicates the Hermitian transpose, N is the number of transmitted symbols contained in a data block in the transmission unit, and the symbol Represents an assignment operation. 7. The joint detection device according to claim 1, wherein the transmission unit as a basic unit for joint detection is one of burst, subframe and frame. 8. The joint detection apparatus according to claim 1, wherein the main system matrix is a non-zero matrix among the system matrices, and N is the number of data symbols contained in one transmission unit. 9. The joint detection device according to claim 1, wherein the main correlation matrix is a symmetric positive definite matrix. 10. The joint detection device of claim 1, wherein the digital matched filter is a device that multiplies the data from the data combiner with the conjugate transpose of the main system matrix. 11. The joint detection device according to claim 1, the main correlation matrix inverse calculator comprises the following devices: The means for constructing the row-major circulant matrix R _1r and the column-major circulant matrix R _1c using the main correlation matrix, denoted as R _1r = [C _{N, N} C _{N-1, N} ... C _{N, N-1} ] ${\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; [</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; ]</span>}^{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}}$ Wherein, T _b represents block transposition, the horizontal dotted line in the formula represents the zero matrix z, any main circulant matrix contains N+1 matrix blocks, and N is the number of data symbols contained in a transmission unit; Means for computing an eigenvalue matrix of a row-major circulant matrix using a Fast Fourier Transform; means for computing the Muller-Penrose inverse of the eigenvalue matrix; means for calculating the inverse of the row-major circulant matrix from the column-major circulant matrix; and Means for computing the inverse of the main correlation matrix by dividing the inverse polynomial of the main circulant matrix. 12. A joint detection method applied to a direct sequence spread spectrum CDMA multi-antenna diversity reception communication system, comprising the following steps: Combine data information from different antennas according to corresponding data blocks in the transmission unit; The main system matrix is obtained by convolution of the user code of each user and the channel impulse response; The main correlation matrix is obtained by computing the correlation of the main system matrix; Perform digital matched filtering on the data block; Calculate the inverse matrix of the main correlation matrix; Data symbols corresponding to different users are estimated from the filtered data and the inverse matrix. 13. The joint detection method according to claim 12, wherein the data information from different antennas is a data stream after spectrum spreading. 14. The joint detection method according to claim 12, wherein the combining step is sequentially concatenating the data information from different antennas without destroying the received data information. 15. The joint detection method according to claim 12, wherein the main system matrix is a matrix conforming to the block-Sylvester structure property. 16. The joint detection method according to claim 12, wherein the main system matrix has K _a (Q+W-1) rows and K columns, expressed as $\mathrm{<span\; class="notranslate">\; A</span>}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; [</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{{\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}}<span\; class="notranslate">\; ]</span>}^{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}}$ ${\mathrm{<span\; class="notranslate">\; A</span>}}_{{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; c</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \&CircleTimes;</span>{\mathrm{<span\; class="notranslate">\; h</span>}}^{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; )</span>}$ Among them, A represents the main system matrix, T _b represents the block transposition, c ^(k) and h ^{(k, ka)} are the user code vector of length Q and the channel impulse response vector of length W, k=1, 2, ..., K is the number of user codes that each transmission unit can accommodate, k _a =1, 2, ..., K _a is the number of receiving antennas, Q is the spreading factor, W is the length of the channel impulse response, _ means convolution operation. 17. The joint detection method according to claim 12, wherein the main correlation matrix is made up of 3 different matrices C _{N, N} , C _{N-1, N} and C _{N, N-1} , as following formula ${\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; N</span>}}\stackrel{\mathrm{<span\; class="notranslate">\; def</span>}}{<span\; class="notranslate">\; =</span>}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 11</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 11</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; N</span>}}\stackrel{\mathrm{<span\; class="notranslate">\; def</span>}}{<span\; class="notranslate">\; =</span>}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 12</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 12</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}\stackrel{\mathrm{<span\; class="notranslate">\; def</span>}}{<span\; class="notranslate">\; =</span>}{<span\; class="notranslate">\; [</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; ]</span>}^{\mathrm{<span\; class="notranslate">\; h</span>}}$ As shown, where, a ₁₁ ＝A ^H (1:K, 1:K _a (Q+W-1)), b ₁₁ ＝A(1:Ka(Q+W-1), 1:K) a ₁₂ =A ^H (1:K,K _a Q+1: _{K a} (Q+W-1)), b ₁₂ =A(1:Ka(W-1),1:K), The superscript H indicates the Hermitian transpose, N is the number of transmitted symbols contained in a data block in the transmission unit, and the symbol

Represents an assignment operation. 18. The joint detection method according to claim 12, wherein the transmission unit as a basic unit for joint detection is one of a burst, a subframe and a frame. 19. The joint detection method according to claim 12, wherein the main system matrix is one of system matrices composed of N main system matrices, N being the number of data symbols contained in one transmission unit. 20. The joint detection method according to claim 12, wherein the main correlation matrix is a symmetric positive definite matrix. 21. The joint detection method of claim 12, wherein digitally matched filtering is performed by multiplying the data from the data combiner with the conjugate transpose of the main system matrix. 22. The joint detection method according to claim 12, the calculation of the main correlation matrix inverse may further comprise the steps: Use the main correlation matrix to construct the row-major circulant matrix R _1r and the column-major circulant matrix R _1c , expressed as R _1r = [C _{N, N} C _{N-1, N} ... C _{N, N-1} ] ${\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; [</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; ]</span>}^{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}}$ Wherein, T _b represents the block transposition, the horizontal dotted line in the formula represents the zero matrix z, and the dimension of any main circulant matrix is N+1, and N is the number of data symbols contained in a transmission unit; Calculate the eigenvalue matrix of the row-major circulant matrix using the Fast Fourier Transform; Calculate the Muller-Penrose inverse of the eigenvalue matrix; computes the inverse of a column-major circulant matrix from a row-major circulant matrix; and The inverse matrix of the main correlation matrix is calculated by dividing the inverse polynomial of the main circulant matrix. 23. The joint detection method according to claim 22, wherein the method of dividing the inverse polynomial of the main circulant matrix is realized by block deconvolution. 24. The joint detection method according to claim 23, wherein block deconvolution comprises the steps of: Reverse the order of the blocks in the block-row and block-column matrices; Calculate the fast Fourier transform of the block row matrix and the block column matrix; Calculate the quotient of the fast Fourier transform of the block row matrix and the block column matrix; Calculate the inverse Fourier transform of the quotient; Calculate the remainder. 25. The joint detection method of claim 12, which can be used in any multi-target receiving system that requires signal detection and signal estimation. 26. A direct sequence spread spectrum CDMA multi-antenna diversity reception communication system using the joint detection device according to claim 1, the system comprising: Diversity antenna array for independently receiving transmissions from all users; A down-converter for converting radio frequency signals to baseband signals; An analog-to-digital converter for converting an analog baseband signal into a digital baseband signal with a certain dynamic range; A signal divider for dividing the digital baseband signal into data information and training sequences; a channel estimator for estimating a channel impulse response from the training sequence; The joint detection means for estimating the data symbols sent by the user according to the channel impulse response and data information; A channel decoder for recovering the transmitted data symbols from the joint detector estimates.

Images