A Low-Complexity Iterative Channel Estimation and Detection Technique for Doubly Selective Channels

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1 A Low-Compexity Iterative Canne Estimation and Detection ecnique for Douby Seective Cannes Qingua Guo and Li Ping City University of Hong Kong, Hong Kong SAR, Cina Emai: Abstract In tis paper, we propose a ow-compexity iterative oint canne estimation, detection and decoding tecnique for douby seective cannes. e ey is a segment-by-segment frequency domain equaization (FDE strategy under te assumption tat canne is approximatey static witin a sort segment. Guard gaps (for cycic prefixing or zero padding are not required between adacent segments, wic avoids te power and spectra overeads due to te use of cycic prefix (CP in te conventiona FDE tecnique. A ow-compexity bi-directiona canne estimation agoritm is aso deveoped to expoit correation information of time-varying cannes. Simuation resuts are provided to demonstrate te efficiency of te proposed agoritms. overead. e signa in a boc is transmitted continuousy in te same way as a conventiona sceme, i.e., te segmentation does not affect te structure of te transmitted signa. We assume tat te canne remains static witin a segment but not necessary witin a boc. e received signa is sown in Fig. (b were te observation vector r covers a te contribution of segment x. Due to deay spread, r is onger tan x, so it suffers from te interference form its adacent segments x - and x +. In tis paper, an iterative tecnique is deveoped to ande suc interference. Since te boc engt is not imited by te canne coerent time, it can be arge enoug to ensure a negigibe overead caused by te guard interva. I. INRODUCION Singe-carrier boc transmission wit frequency domain equaization (FDE [] is an efficient tecnique to aeviate inter-symbo interference (ISI in mutipat cannes. e use of cycic prefix (CP avoids inter-boc interference and converts inear convoution to cycic convoution, wic aows efficient impementation of receivers based on fast Fourier transform (FF. However, te use of CP incurs overeads (in terms of bot power and spectra efficiency oss tat can be measured by te foowing ratio: CP engt / boc engt. In te conventiona FDE tecnique, tis ratio is imited by te foowing two requirements: e canne soud be static witin a boc (and so te boc engt is imited by canne coerent time; CP soud be onger tan canne memory engt. Due to te above two requirements, te overead ratio can be ig in douby seective cannes (i.e., time-varying ISI cannes wen canne coerent time is sma and canne memory is ong. Using sorter CP is a way to reduce overead. However, CP engt ess tan canne memory engt may cause interference among consecutive bocs, and te assumption of cycic convoution for FDE is aso invaid in tis case. medies for tese probems ave been studied in [2-4]. In tis paper, we propose a nove detection tecnique for douby seective cannes, in wic eac boc of te transmitted signa is partitioned into a number of sort segments {x } as sown in Fig. (a. ere is no guard interva between two consecutive segments, wic avoids te reated Fig. (a e transmitted signa x is partitioned into a number of sort segments {x }. Eac segment is assumed to undergo a static ISI canne. (b ISI causes interference among adacent segments, as iustrated by te sadowing parts. We wi aso deveop a ow-compexity bi-directiona canne estimation agoritm tat can be incorporated in te iterative process for oint canne estimation, equaization and decoding. is provides a soution for canne estimation tat is anoter caenging probem in douby seective cannes. Simuation resuts are provided to demonstrate te efficiency of te proposed tecnique. e notations used in tis paper are as foows. Lower case etters denote scaars, bod ower case etters denote coumn vectors, and bod upper case etters denote matrices. We use superscript to denote transpose, * conugate and H conugate transpose. I denotes an identity matrix wit proper size. Expectation and (covariance are denoted by E( and V(, respectivey. For a compex variabe, e.g., x, we use x to denote its rea part and x Im its imaginary part. II. PRELIMINARY In tis section, we provide a brief outine of te underying MMSE estimation principe, and ist te ey resuts to be used in te foowing sections. A. MMSE Estimation for Gaussian Variabes is fu text paper was peer reviewed at te direction of IEEE Communications Society subect matter experts for pubication in te IEEE "GLOBECOM" 2008 proceedings /08/$ IEEE. Autorized icensed use imited to: CityU. Downoaded on February, 2009 at 02:27 from IEEE Xpore. strictions appy.

2 Consider a standard estimation probem based on te foowing inear mode r = A+ n, ( were r is an observation vector, A a system transfer matrix, a vector to be estimated, and n a sampe vector of Gaussian noise. Assume tat is Gaussian distributed and its a priori mean vector and covariance matrix are denoted by E( and V(, respectivey. en te a posteriori mean and variance of can be computed as [0] ( ( H ( E( p = E( + V( A H AV( A H + V( n r AE( E( n,(2a p V ( = V( + A V( n A. (2b B. Joint Gaussian Estimation for Binary Variabes e estimation becomes muc more compicated for te foowing inear mode r = Ax+ n, (3 were te entries in x are binary. We first assume tat a te variabes invoved in (3 are rea. In tis case, te estimation is usuay given in an entry-by-entry extrinsic ogaritm of ieiood ratio (LLR form as [5], [6], [4] p( r x =+ ev ( = n, (4 p ( r x = were x is te t entry of x. e optima approac to computing (4 is based on te maximum a posteriori probabiity (MAP criterion, but its compexity is usuay proibitive. A ow-compexity sub-optima aternative is te so-caed oint Gaussian (JG approac [4], in wic (3 is rewritten in te foowing form, r = a x +ξ, (5 were a is te t coumn of A and ξ = a ' ' x ' + n. (6 Note tat ξ is te sum of te contributions of a entries in x except x and noise. By te centra imit teorem, we can approximate te entries of ξ as oint Gaussian variabes wit E( ξ = AE( x a E( x + E( n, (7a = r x aa V( ξ V( V(, (7b V( r = AV( x A + V( n. (7c Based on te above assumption, (4 can be computed as exp ( r a E( ξ V( ξ ( r a E( ξ 2 ex ( = n exp ( r+ a E( ξ V( ξ ( r+ a E( ξ 2 ( + x = ξ 2aV( r AE( x E( n ae( ( a r r A x n + a r a x = 2 V( x a V( r a V( E( E( V( E(. (8 e resut (8 is te same as tat in te so-caed LMMSE approac derived in [5] and [6]. Compared wit te derivation in [5] and [6], te derivation described above is more concise and straigtforward. e above resut can be extended to compex systems. In tis Im case, denote x = x + ix were i = and { Im x, x } are binary. Define Im ex ( = ex ( + iex (, (9 were Im ( ( n p r x =+ ex =, and ( p r x = ( Im n p( r x =+ ex =. (0 ( Im p r x = Let Im V( x = V( x = 0.5V( x and assume Im V( x, x = 0 (i.e., te rea and imaginary parts of x are uncorreated. en e(x can be cacuated as H H a V( r ( r AE( x E( n + a V( r ae( x ex ( = 4, ( H V( x a V( r a or in a vector form as (etting e(x = [e(x 0, e(x, ] H e( x = 4( I VU ( A V( r ( r AE( x E( n + UE( x, (2 were V(r = AV(xA H +V(n, (3a V = diag{v(x 0, V(x,...}, (3b U = (A H V(r - A diag. (3c In (3c, te operator ( diag returns a diagona matrix consisting of te diagona eements of te matrix in te parenteses. For space imitation, we omit te derivation of (. III. HE OVERALL JOIN PROCESS In tis section, we introduce te signa mode and te framewor of oint canne estimation, detection and decoding. e detaied estimation and detection agoritms wi be discussed in te foowing sections. Consider a time-varying (compex ISI canne mode L r = x + η, (4 = 0 were {x } are te transmitted signa formed by te outputs of a forward error correction (FEC encoder wit quadrature pase sift eying (QPSK and Gray mapping, {r } te observations, {η } te sampes of additive witen Gaussian noise wit zero mean and variance 2σ 2, and {, = 0,,, L} te canne state information (CSI at time. turn to Fig. were te transmitted signa x is partitioned into K sort segments {x }, eac wit engt M. We assume tat eac segment x undergoes a static ISI canne 0 L = [,,..., ]. As sown in Fig., te observation vector reated to x can be represented in a convoution form as r = x + y + y+ + η, (5 were denotes inear convoution operation. Note tat te engt of r is M+L (see Fig. (b, and a te information about x is incuded in r. In (5, y and y + represent te interference from te adacent segments x - and x + respectivey, and tey can be expressed as y = [ tai ( * x,0,...,0], and y + = [0,...,0, ead( + * x + ] Here te detection of x is based on r. In contrast, te detection of x discussed in [2-4] is based on te first M eements of r, and ence some usefu information is ost. is fu text paper was peer reviewed at te direction of IEEE Communications Society subect matter experts for pubication in te IEEE "GLOBECOM" 2008 proceedings /08/$ IEEE. 2 Autorized icensed use imited to: CityU. Downoaded on February, 2009 at 02:27 from IEEE Xpore. strictions appy.

3 were tai( and ead( represent two truncation functions tat return te tai part and ead part of te sequence in te parenteses, respectivey. e engt of tai/ead part is L. We rewrite (5 in a more compact form as r = x + n, (6a wit n = y + y + η. (6b + We assume tat a guard interva in te form of zero padding is appended to eac boc (see Fig. (a. us te ast segment x K ony sees te interference from x K- and tere is no inter-boc interference. is aows independenty processing te bocs. e iterative receiver is sown in te ower part of Fig. 2. It consists of tree modues: a canne estimator, a canne equaizer and a decoder. We foow te framewor of iterative canne estimation and detection detaied in [7] and [8]. e foowing is a brief outine of te function of eac modue in te iterative process. e canne estimator provides te estimates of { } (in te form of {E( } and {V( } based on te output of te canne decoder and te statistica property of te time-varying canne. Based on te a priori information about {x } from te canne decoder and te canne estimates from te canne estimator, te canne equaizer computes te extrinsic LLRs for {x } as given in (. Based on te output of te canne equaizer and te FEC coding constraint, te canne decoder refines te data estimates. We assume tat standard a posteriori probabiity (APP decoding [5] is used. e above tree modues wor in an iterative manner. e decoder maes ard decisions on te information bits during te fina iteration. fer to [5-9] for te detaied discussions for te above iteration process. ˆd Π Π Π Fig. 2. e transmitter and turbo receiver. and - denote intereaver and de-intereaver, respectivey. In te foowing, we wi discuss te detais for te canne estimator and equaizer, respectivey. η Aternativey, to avoid matrix inversion, we can empoy te foowing ow-compexity tap-by-tap estimation tecnique. We focus on and rewrite (7 as r = a, + ζ,, (8 were a, is te t coumn of A, and ' ζ, = a, ' + n, (9 ' represents te noise pus interference from oter taps. Its mean vector and covariance matrix are given by ' Ε( ζ, = a, ' Ε( + Ε( n, (20a ' H, = A A + n V( ζ V( V(. (20b In genera V( ζ, is a fu matrix. o reduce compexity, we approximate V( ζ, using its diagona part D = V( ζ. (2 (,, diag Based on te above approximation and (2, we ave H p V( a, D, ( r Ε( ζ, a, Ε( E( E( +, (22a H V( a D a +,,, p V( H V( a, D, a, + and V( V (. (22b In te above equations, E( denote te a priori mean and variance of te concerned variabe, and Ε(ζ, and D, are reated to noise pus interference. In te iterative process, te canne estimation resuts in te ast iteration can be used as a priori information for te current iteration. Specificay, { E p ( ',V p ( ', ' } cacuated in te ast iteration can be used to update Ε(ζ, and D, in (22 based on (20 and (2. 2 p p In contrast, E ( and V ( cacuated in te ast iteration soud not be used as te a priori information E( and V( in (22 to estimate in te current iteration, since te a priori information soud be extrinsic according to te turbo principe. However, te extrinsic a priori information of can come from its adacent segments due to te correation of time-varying cannes. In oter words, te estimation resuts for segments - and + can be used as extrinsic a priori information for te estimation of te concerned segment, as discussed beow. We assume tat te canne taps are independent of eac oter, and use te foowing first-order autoregressive mode to approximatey caracterize te time-varying canne: = β + w, = 0,,, L, (23 IV. CHANNEL ESIMAION write (6 in a matrix form as r = A + n, (7 0 L were A is formed based on x and = [,,..., ]. First, we assume tat {x } are perfect nown. Based on (7, we can estimate using te standard MMSE estimator (2. 2 { Ε(ζ,, D,, = 0,, L } can be updated in parae based on te resuts in te ast iteration. Aternativey, we can update tem in seria to acceerate te agoritm convergence. In tis case, te updating of { Ε(ζ,, D,, = 0,, L } soud be based on te most updated { E p ( ',V p ( ' } (i.e., some of tem are computed in te current iteration. is means tat te estimation of {, = 0,, L} based on (22 is aso performed in seria. is fu text paper was peer reviewed at te direction of IEEE Communications Society subect matter experts for pubication in te IEEE "GLOBECOM" 2008 proceedings /08/$ IEEE. 3 Autorized icensed use imited to: CityU. Downoaded on February, 2009 at 02:27 from IEEE Xpore. strictions appy.

4 were β is a constant, and w denotes wite Gaussian process wit power p. e parameters β and p can be determined based on te correation function of te time-varying canne. We can use te foowing bi-directiona agoritm (simiar to Kaman smooting to expoit correation information of time-varying cannes.. Forward cursion p p Assume tat { E Fwd ( -,V Fwd ( - } are avaiabe, were te subscript Fwd denotes forward. Set p 2 p E( = βe Fwd ( - and V( = β V Fwd ( - + p, and p p ten compute { E Fwd (,V Fwd ( } using ( Bacward cursion p p Assume tat { E Bwd ( +,V Bwd ( + } are avaiabe, were te subscript Bwd denotes bacward. Set p E( β 2 p = E Bwd ( + and V( = β (V Bwd ( + + p, p p and ten compute { E Fwd (,V Fwd ( } using ( Combining te Forward and Bacward Information After te forward and bacward recursions, te fina estimates { E p (,V p ( } are cacuated as p V ( = + p 2 p, (24a V Fwd ( β (V Bwd ( + + p p p E Fwd ( β E Bwd ( p + p E ( = + V ( p 2 p. (24b V Fwd ( β (V Bwd ( + + p Cacuating E( and V( in te forward and bacward recursions and combining information in (24 are reated to te Gaussian message passing tecnique. fer to [] for detais. e compexity of te above described canne estimation agoritm is O(M per tap. In te above discussions, we assume tat {x } are exacty nown to construct matrices {A }. In practice, ony te means {E(x } are avaiabe. In tis case, we simpy use E(x to form A. V. SEGMEN-BY-SEGMEN EQUALIZAION We now turn our attention to te equaizer in Fig. 2. We first assume perfect nowedge of { } at te receiver. e equaizer in (2 can be reaized in a segment-by-segment manner (see Fig.. Based on te assumption tat te canne is static witin a segment, we can efficienty impement te equaizer using FF. is is in principe equivaent to FDE [] [3], but te matrix form derivation beow is more concise and insigtfu. We define te foowing two vectors wit engt N = M+L: = [, 0,...,0 ], and [,0,...,0] x = x. (25 M repicas Lrepicas en (6a can be rewritten as r = x + n, (26 were denotes te cycic convoution operation. Here, appending zeros to and x transforms te inear convoution in (6a into te cycic convoution in (26. Define F as te normaized discrete Fourier transform (DF matrix wit size N N, i.e., te (m, nt eement of F is /2 i2 π mn/ N given by F ( mn, = N e. Hence FF H = I. According to te property of cycic convoution, we can rewrite (26 as NFr = NF NFx + NFn, (27 were denotes eement-wise product. Denote te DF of as: NF [,0,,,..., ] = g g g N, (28 and define a diagona matrix G = diag{ g,0, g,,... g, N }. (29 en (27 can be rewritten in a matrix form as H r = F G F x +n. (30 A From (6b, we can see tat E( n = E( y + E( y +, (3a V( n = V( y + V( y I. (3b + σ Define v as te average variance of {x }, and α as te average of diagona eements of matrix V(n. e foowing two approximations may incur margina performance oss but ead to considerabe cost reduction: V( x~ vi, (32a V(n α I, (32b were α can be cacuated as L α = 2 σ + vm ( + L ( + ( L +. (33 = 0 Based on (30 and (32, we ave V( r H V( H ( H = vaa + n = F νgg + αi F. (34 Hence H U = A V( r A = u I, (35 ( diag were N u 2 ( 2 = N g 0, n ν g, n + α n=. (36 Based on (34, (35 and (2, we ave H H H e( x = 4( νu S F G ( νgg + αi, (37 ( z GF E( x FE( n + ue( x ] were S = [ I M M, 0] M N, and z =Fr. mars:. e matrix inversion invoved in (37 is trivia because te reated matrix is diagona. 2. e operation reated to F and F H can be efficienty reaized using FF. e compexity invoved in (37 is ony O(og 2 N per entry. 3. e term -FE(n invoved in (37 provides soft canceation of te interference from te adacent segments + and -. (See (6b for te definition of n. Here, E( n = E( y + E( y+ can be efficienty computed based on FF as foows: E( y = E([ tai( * x,0,...,0] = E([ tai( x,0,...,0]. H = [ tai( F G FE( x,0,...,0] is fu text paper was peer reviewed at te direction of IEEE Communications Society subect matter experts for pubication in te IEEE "GLOBECOM" 2008 proceedings /08/$ IEEE. 4 Autorized icensed use imited to: CityU. Downoaded on February, 2009 at 02:27 from IEEE Xpore. strictions appy.

5 Note tat te term GF E( x is invoved in (37, wic indicates tat G FE( x can be sared by e(x - and e(x. A simiar treatment can be appied to E( y +. e above provides a fast tecnique to impement te equaizer in (2. In practice, ony te means {E( } are avaiabe. In tis case, we simpy repace by E( in te above discussed agoritm. VI. SAR UP HE IERAIVE PROCESS USING PILO SIGNAL In te iterative oint process discussed above, canne estimation soud be first performed at te receiver. According to te discussions in Section IV, we need set up matrices {A } based on te mean vectors {E{x }} tat are updated based on te feedbacs from te decoder in te iterative process. However, in te first iteration, tere are no decoder feedbacs avaiabe, and in genera we don t ave a priori information about tem, wic means {E(x = 0}. is causes difficuty in evauating (22. Piot signa can be used to sove tis probem. Fig. 3 sows te pacement of te piot signa. e piot signa is superposed wit te data signa, wic ony incurs power oss (witout rate oss. Exampe : Quasi-static cannes wit perfect CSI In tis exampe, te encoding sceme is a rate-/2 convoutiona code wit generator (23, 35 8, and te information engt is QPSK moduation is used. Hence te engt of boc (i.e., engt of x is aso e segment engt (i.e., te engt of x is set to be 64. e number of iterations is 0. e engt of ISI cannes is 7. e 7 coefficients remain constant for a te segments in a boc, and tey are independenty drawn from a compex Gaussian distribution wit mean 0 and variance for different bocs. In eac canne reaization, te canne energy is normaized to. We now tat te performance of te system over suc ISI cannes is bounded by te performance of te code over an AWGN canne. e performance is sown in Fig. 4, from wic we can ceary see tat te proposed equaization agoritm can amost acieve te ISI-free performance at reativey ig E b /N o, wic aso impies tat te inter-segment interference is amost eiminated..e+00.e-0.e-02 Proposed agoritm AWGN BER.E-03.E-04 Fig. 3. Pacement of piot signa. e piot signa ony invoves power oss (witout spectra oss. e transmitted signa can be represented as x = p + c, (38 were p denotes te piot signa and c denotes te data signa. In te first iteration, E(x = E(p + E (c= p. (39 e iterative process can be started ere. Note tat, in equaization, some extra operations soud be incuded to ande te contribution of te piot signa. Now we concude te overa iterative process as foows: Step. Based on te feedbacs from te decoder, te canne estimator performs canne estimation via forward and bacward recursions based on (22 and (24. Step 2. Based on te canne estimate information from te canne estimator and te feedbacs from te decoder, te canne equaizer resoves ISI and inter-segment interference to provide data estimates based on (37. Step 3. e decoder refines te data estimates, and te resuts wi be used by te canne estimator and equaizer in te next iteration. en go to Step. Hard decisions on te information bits are made by te decoder during te fina iteration. VII. SIMULAION RESULS We first examine te proposed segment-by-segment equaization agoritm in quasi-static ISI cannes under te assumption of perfect CSI at receiver side. is exampe is to compare te performance of te proposed metod wit te nown performance imit..e Eb/No (db Fig. 4. Performance of te proposed agoritm in 7-tap quasi-static ISI cannes wit energy. Segment engt is 64 and te number of iterations is 0. Next we examine te proposed oint canne estimation, equaization and decoding sceme over a douby seective canne. Exampe 2: Douby seective cannes wit estimated CSI We adopt te basis expansion mode (BEM detaied in [2]. In tis mode, te canne coefficients are generated by Q q= 0 i q, qe ω = λ, = 0,, L, (40 were ω q = 2π(q-Q/2/N, Q = f max, f max represents Dopper spread, and is te time duration of a boc. Define s and N s as te symbo duration and te number of symbos in one boc, ten = N s s. e BEM coefficients λ,q is a zero-mean, compex Gaussian random variabe wit variance σ 2,q. We set carrier frequency f 0 = 2 GHz, samping period s = 0 μs, and mobie speed v = 40 m/r. e corresponding Dopper spread f max = 259Hz (i.e., te normaized Dopper spread f max s = e Dopper power spectrum is cosen as 2 2 ( π fmax f, f fmax Sc ( f = (4 0, f > fmax e number of muti-pats is 9, and te mutipat intensity profie is seected as p( τ = exp( 0. τ / s. e variance is fu text paper was peer reviewed at te direction of IEEE Communications Society subect matter experts for pubication in te IEEE "GLOBECOM" 2008 proceedings /08/$ IEEE. 5 Autorized icensed use imited to: CityU. Downoaded on February, 2009 at 02:27 from IEEE Xpore. strictions appy.

6 λq, = α p( s Sc( q /( Ns wit α = p (' S ( q '/( N ', q' s c s. e coding sceme is a rate-/2 convoutiona code wit generator (23, QPSK moduation is used. e information engt is 4096, and so is te boc engt. We first examine te performance of te proposed sceme wit CSI avaiabe at te receiver. In tis case, our focus is te effect of different segment engts on system performance. We assume tat CSI corresponding to te midde point of eac boc is exacty nown at te receiver. Fig. 5 sows te system performance wen te segment engt M is 6, 32, 64, and 28, respectivey. We can ceary observe tat te performance degrades wit te increase of segment engt. (Performance is reativey poor at M = 64 compared wit M = 32 and 6. A ig error foor occurs at M = 28. is is because te agoritm assumes a static ISI canne witin a segment. is assumption is invaid wen M = 64 and 28. We can aso see tat te performance wit M = 6 and 32 is amost te same, wic indicates tat te static canne assumption is vaid wen M 32. If te conventiona FDE is used wit M = 32, te use of CP wi incur extra power oss of db and spectra oss of 20%. BER.E+00.E-0.E-02.E-03.E-04.E Eb/No (db CSI nown at te midde of eac segment CSI estimated Fig. 5. Performance of te proposed approac over douby seective cannes. e power oss due to te use of piot is incuded in Eb/No. e normaized Dopper spread f max s = e number of iterations is 0. We next examine te performance of te proposed sceme wit estimated CSI. We set M = 32 at wic te canne can be regarded as approximatey static witin a segment. e piot segments are superposed wit te data segments. e power ratio of te piot signa and data signa is /4. Hence te power oss due to piot signa is 0og 0 (5/4 db. From Fig. 5, we can see tat te performance gap between te sceme wit CSI and estimated CSI is ess tan 3dB at te BER of Here te power oss of about db due to piot signa is incuded in Eb/No. time-varying cannes. e proposed canne equaizer inerits te ow-compexity advantage of FDE tecnique, but does not resort to cycic prefixing and so avoids te reated power and spectra overeads. Simuation resuts demonstrate te efficiency of te proposed agoritms. ACKNOWLEDGEMEN is wor was performed in te framewor of te IC proect IC WHERE party funded by te European Union. REFERENCES [] M. ücer, and J. Hagenauer, urbo equaization using frequency domain equaizers, in Proc. Of Aerton Conference, Monticeo, IL, USA, Oct [2] D. Kim and G. Stüber, sidua ISI canceation for OFDM wit appications to HDV broadcasting, IEEE. J. Se. Areas Commun., vo. 6, no. 8, pp , Oct [3] Y. Li, S. McLaugin and D.G.M. Cruicsan, Bandwidt efficient singe carrier systems wit frequency domain equaization, Eectron. 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CONCLUSIONS We ave proposed a ow-compexity iterative oint canne estimation, equaization and decoding sceme for douby seective cannes. e ey is a segment-by-segment processing tecnique. A bi-directiona canne estimation agoritm as been deveoped to expoit correation of is fu text paper was peer reviewed at te direction of IEEE Communications Society subect matter experts for pubication in te IEEE "GLOBECOM" 2008 proceedings /08/$ IEEE. 6 Autorized icensed use imited to: CityU. Downoaded on February, 2009 at 02:27 from IEEE Xpore. strictions appy.