Retrodirective Distributed Transmit Beamforming with Two-Way Source Synchronization

Size: px
Start display at page:

Download "Retrodirective Distributed Transmit Beamforming with Two-Way Source Synchronization"

Transcription

1 Retrodrectve Dstrbuted Transmt Beamformng wth Two-Way Source Synchronzaton Robert D. Preuss and D. Rchard Brown III Abstract Dstrbuted transmt beamformng has recenty been proposed as a technque n whch severa snge-antenna sources cooperate to form a vrtua antenna array and smutaneousy transmt wth phase-agned carrers such that the passband sgnas coherenty combne at an ntended destnaton. The power gans of dstrbuted transmt beamformng can provde ncreased range, rate, energy effcency, and/or securty, as we as reduce nterference. Dstrbuted transmt beamformng, however, typcay requres precse synchronzaton between the sources wth tmng errors on the order of pcoseconds. In ths paper, a new two-way synchronzaton protoco s deveoped to factate precse source synchronzaton and retrodrectve dstrbuted transmt beamformng. The two-way synchronzaton protoco s deveoped under the assumpton that a processng at each source node s performed wth oca observatons n oca tme. An anayss of the statstca propertes of the phase and frequency estmaton errors n the two-way synchronzaton protoco and the resutng power gan of a dstrbuted transmt beamformer usng ths protoco s provded. Numerca exampes are aso presented characterzng the performance of dstrbuted transmt beamformng n a system usng two-way source synchronzaton. The numerca resuts demonstrate that near-dea beamformng performance can be acheved wth ow synchronzaton overhead. I. INTRODUCTION Dstrbuted transmt beamformng s a technque n whch mutpe ndvdua snge-antenna sources smutaneousy transmt a common message and contro the phase and frequency of ther carrers so that ther bandpass sgnas constructvey combne at an ntended destnaton. The transmtters n a dstrbuted transmt beamformer form a vrtua antenna array and, n prncpe, can acheve a of the gans of a conventona antenna array, e.g. ncreased range, rate, and/or energy effcency, wthout the sze, cost, and compexty of a conventona antenna array. Dstrbuted transmt beamformng can aso provde benefts n terms of securty and nterference reducton snce ess transmt power s scattered n unntended drectons. A common assumpton n the terature s that dstrbuted transmt beamformng can be performed by usng tmedvson-dupexng (TDD) and a phase conjugaton technque smar to the retrodrectve Pon array [1] technque deveoped for conventona antenna arrays. The approach s as foows: () the destnaton node frst broadcasts a sgna R.D. Preuss s a Senor Member of the IEEE vng n Arngton, MA USA. e-ma: r.preuss@eee.org D.R. Brown III s an Assocate Professor wth the Eectrca and Computer Engneerng Department, Worcester Poytechnc Insttute, Worcester, MA USA. e-ma: drb@ece.wp.edu. Ths work was supported by NSF award CCF receved by each source node and () each source node then transmts back to the destnaton at the same frequency but wth conjugate phase. In prncpe, the phase conjugaton at each transmtter cances the phase shft of the channe. Ths causes the carrers to arrve n phase and coherenty combne at the destnaton. Whe ths retrodrectve transmsson technque s known to be effectve for conventona antenna arrays where each antenna eement s connected to a common oca oscator, t s actuay key to be neffectve n dstrbuted transmsson systems n whch each source node has ts own ndependent oca oscator f the source nodes are not pre-synchronzed. To demonstrate the crtca roe of synchronzaton n dstrbuted transmt beamformng, consder a system wth two unsynchronzed source nodes, denoted as S 1 and S 2, and one destnaton node. Denote the tme at the destnaton node as t and the tme at the source nodes as t 1 and t 2. For purposes of ustraton, we assume that S has an unknown fxed oca tme offset wth respect to the destnaton node s tme such that t = t. Fgure 1 shows a TDD tmene n whch the unknown oca cock offsets 1 and 2 are dfferent,.e. the source nodes are not pre-synchronzed. In the frst step of TDD operaton, the destnaton node broadcasts the sgna x 0 (t) = exp{jω 0 t}i t [0,T) to the source nodes, where T s the sgna duraton and the ndcator functon I t A = 1 when t A, and s otherwse equa to zero. Ths sgna s represented as the sod-ne sgna n Fgure 1. Assumng snge-path unt-gan channes and gnorng nose, the sgna receved by S can then be wrtten as y (t) = exp{jω 0 (t τ 0, )}I t [τ0,,τ 0,T) (1) for = 1, 2 where τ 0, s the unknown propagaton deay of the channe from the destnaton node to S. These sgnas are ustrated as the sod-ne sgnas on the source node s tmenes n Fgure 1. Note that (1) s wrtten n the destnaton node s oca tme. In the source node s oca tme, y (t ) = exp{jω 0 (t τ 0, )}I t [ τ 0,, τ 0,T) for = 1, 2. The phase estmate at S s then cacuated as the phase at t = 0,.e. ω 0 ( τ 0, ). In the second step of TDD operaton, both source nodes transmt wth conjugate phase back to the destnaton. The carrer transmtted by S can be wrtten as x (t ) = exp{jω 0 (t τ 0, )}I t [s,s T ) (2) where T s the transmsson duraton and s s the startng tme of the transmsson for source node. These sgnas

2 source node 1 T destnaton node 2 1 source node 2 τ 0,1 τ 0,2 x 0 (t) y 1 (t) y 2 (t) x 2 (t) T x 1 (t) y 0 (t) Fg. 1. An exampe of tme-dvson-dupexng (TDD) n a system wth two unsynchronzed source nodes. The carrers transmtted by the source nodes n ths exampe fuy cance each other at the destnaton node. are shown as the dotted and dash-dotted sgnas for S 1 and S 2, respectvey, on the source node s tmenes n Fgure 1. Convertng (2) to the destnaton node s oca tme, we have x (t) = exp{jω 0 (t 2 τ 0, )}I t [s,s T ). The aggregate sgna receved by the destnaton node after propagaton from each source to the destnaton s then y 0 (t) = 2 exp{jω 0 (t 2 )}I t [s,s T ) (3) =1 where s := s τ 0,. Ths ast expresson exposes the two eements of synchronzaton necessary to ensure coherent combnng of the sgnas at the destnaton node. Frst, n order for the carrers to constructvey combne, (3) requres that 2ω 0 1 2ω 0 2 (mod 2π). Ths condton s necessary and suffcent to acheve carrer coherence. The second synchronzaton eement requred to ensure the sgnas coherenty combne at the destnaton node reates to the start tme of transmsson at each source node. In order for source nodes sgnas to arrve at the same tme at the destnaton, the transmsson start tmes must be staggered such that s 1 = s 2. Ths condton s necessary and suffcent to acheve message coherence. The focus of ths paper s prmary on the probem of achevng carrer coherence snce, as shown n ths exampe, the effects of carrer offset can be crtca. Moreover, carrer coherence s usuay consdered the more dffcut probem because the synchronzaton accuracy requred for carrer coherence s typcay on the order of pcoseconds. The probem of message coherence s aso mportant and has been consdered n [2], but the tmng accuracy requrements are ess strngent and the effects of message offset,.e. ntersymbo nterference, are usuay ess crtca. t 1 t t 2 Severa carrer synchronzaton technques have recenty been proposed for dstrbuted transmt beamformng ncudng fu-feedback cosed-oop [3], one-bt cosed-oop [4] [6], master-save open-oop [7], and round-trp open-oop carrer synchronzaton [8], [9]. Each of these technques has advantages and dsadvantages n partcuar appcatons, as dscussed n the survey artce [10]. In ths paper, we descrbe a new synchronzaton technque caed two-way synchronzaton [11] and demonstrate ts effcacy n nose-free and nosy channes. Two-way synchronzaton s smar n some aspects to round-trp synchronzaton, but, unke the round-trp carrer synchronzaton technques descrbed n [8], [9], two-way synchronzaton s performed among the source nodes pror to the transmsson of a beacon from the ntended destnaton. The man contrbutons of ths paper are a descrpton of the two-way carrer synchronzaton technque n a system where each source node has an ndependent oca oscator. We aso show how approprate transmsson phases can be generated to enabe beamformng to an ntended destnaton. We then anayze the statstca propertes of the two-way synchronzaton protoco n terms of the estmaton errors and oscator phase nose. We concude wth numerca exampes that show that the two-way synchronzaton overhead can be sma wth respect to the expected usefu beamformng tme. II. SYSTEM MODEL We consder the system ustrated n Fgure 2 one destnaton node, denoted as node 0, and M source nodes, denoted as nodes S 1,..., S M. A nodes are assumed to possess a snge sotropc antenna. The channe between the destnaton node and S m s modeed as a causa near tme-nvarant (LTI) system wth mpuse response g m (t). The channe between S m and S n s aso modeed as a causa near tme-nvarant (LTI) system wth mpuse response h m,n (t) The nose n each channe s addtve, whte, and Gaussan and the mpuse response of each channe n the system s assumed to be recproca,.e. h m,n (t) = h n,m (t). h 1,2 (t) S 2 Fg. 2. source nodes S 1 h 2,3 (t) h 1,3 (t) S 3 g 2 (t) g 1 (t) g 3 (t) destnaton node A system wth M = 3 source nodes and one destnaton node. We assume the oca tme at S has an unknown rate offset β and an unknown tme offset wth respect to a reference tme t such that t = β (t ). (4) 0

3 Ths mode does not ncude the effect of oscator phase nose but s reasonabe over short duratons, e.g. durng synchronzaton. The effects of oscator phase nose durng beamformng are consdered n Secton V. III. TWO-WAY SOURCE SYNCHRONIZATION PROTOCOL The two-way source synchronzaton protoco s ntated by S 1 transmttng a snusoda beacon to S 2. Ths snusoda beacon s retransmtted through ncreasng ndces S 2 S 3 S S M ( forward propagaton ), where each retransmsson s a perodc extenson of the beacon receved n the prevous tmesot. A second snusoda beacon, ntated by S M, s smary transmtted through the decreasng ndces S M S S 2 S 1 ( backward propagaton ). Assumng approxmatey the same frequency s used for the forward and backward propagated beacons, 2M 2 nonoverappng tme sots (enumerated as TS (1),...,TS (2M 2) ) are used to ensure there s no mutua nterference among the 2M 2 ndvdua transmssons n the two-way source synchronzaton protoco. The sgnas exchanged and estmates generated n each tmesot are expcty descrbed for the forward propagaton stage as foows. In TS (1), S 1 transmts a snusoda beacon x (1) 1 (t 1) = exp{j(ω 1 t 1 φ 1 )}I t1 T (1) 1 to S 2 where T (1) 1 s the transmsson nterva of S 1 n TS (1). Note that x (1) (t 1 ) s expressed n oca tme for S 1. Ths beacon propagates through the channe to S 2 and s receved n oca tme at S 2 as y (1) 2 (t 2) = a 1,2 exp {j (f 1,2 (t 2 ) φ 1 )} I t2 T w (1) (1) 2 (t 2) 2 where T (1) 2 s the recepton nterva of S 2 n TS (1), w (1) 2 (t 2) s the( nose n the sgna ) receved by S 2 n TS (1), f 1,2 (t 2 ) := β 1 ω t2 1 β ψ 1,2, and a 1,2 = H 1,2 (β 1 ω 1 ) and ψ 1,2 = H 1,2 (β 1 ω 1 ) are the amptude and phase shft, respectvey, of the LTI channe between S 1 and S 2 at the true frequency β 1 ω 1. Ths observaton s then used by S 2 to generate frequency and phase estmates ˆω (1) 2 = β 1ω 1 ω (1) 2, and β 2 (5) ˆφ (1) (1) 2 = β 1 ω 1 ( 1 2 ) ψ 1,2 φ 1 φ 2 (6) where ω (1) (1) 2 and φ 2 are the frequency and phase estmaton error, respectvey, at S 2 n TS (1). Ths process s repeated through ncreasng source node ndces. In each tmesot, a source node transmts a perodc extenson of the beacon t receved n the pror tmesot to the next source node. The sgna transmtted by S 1 to S n TS ( 1) s x ( 1) 1 (t 1) = exp{j(ˆω ( 2) 1 t 1 ˆφ ( 2). After propagaton through the LTI chan- 1 )}I t 1 T ( 1) 1 ne to S, the sgna s receved as y ( 1) (t ) = a 1, exp { j w ( 1) (t ) ( f 1, (t ) )} ( 2) ˆφ 1 I t T ( 1) where f 1, := β 1ˆω ( 2) 1 ( t β 1 )ψ 1,. Ths observaton s then used by S to generate frequency and phase estmates ˆω ( 1) ˆφ ( 1) = β 1ˆω ( 2) 1 ω ( 1), and (7) β = β 1ˆω ( 2) 1 ( 1 ) ψ 1, for = 3,...,M, where ω ( 1) φ ( 1) ( 2) ( 1) ˆφ 1 φ (8) and are the frequency and phase estmaton error, respectvey, at S n TS ( 1). The forward propagaton stage concudes at the end of TS (). Backward propagaton s the same as forward propagaton except S M ntates the process by transmttng a snusoda beacon x (M) M (t M) = exp{j(ω M t M φ M )}I tm to S T (M). M The beacons are retransmtted through decreasng ndces = M 1,...,1 and the backward propagaton stage concudes after S 1 receves the fna beacon n TS (2M 2). At the end of the two-way source synchronzaton protoco, each source except S 1 and S M has two sets of phase and frequency estmates. Sources S 1 and S M use ther nta beacon phase and frequency (ω 1 and φ 1 or ω M and φ M ) as ther other estmates. Note that these estmates have no estmaton error. For notatona convenence, we denote the four estmates obtaned by S as ˆω,1, ˆω,2, ˆφ,1, and ˆφ,2. IV. SYNCHRONIZATION AND BEAMFORMING After the exchange of beacons, each source adds ts frst and second estmates to synthesze a synchronzed oca oscator (SLO) wth frequency ˆω = ˆω,1 ˆω,2 and nta phase ˆφ = ˆφ,1 ˆφ,2. If we temporary assume that each source node s phase and frequency estmates are perfect 1 n the sense that there s no estmaton error n each tmesot, t s not dffcut to show that the SLO phase ξ := ˆω t ˆφ s dentca at a source nodes (moduo 2π). To see ths, we can use (7) n the forward propagaton stage to wrte the frst frequency estmate at S as ˆω,1 = β 1 ˆω 1,1 = β 1 ω 1 β β for = 2,...,M. The second equaty resuts from a recursve appcaton of the frst equaty and the fact that ˆω 1,1 := ω 1. Aong the same nes, we can use (7) n the backward propagaton stage to wrte the second frequency estmate at S as ˆω,2 = β 1 β ˆω 1,2 = β M β ω M for = M 1,...,1 where ˆω M,2 := ω M. The resutng frequency at S s then ˆω = ˆω,1 ˆω,2 = β 1ω 1 β M ω M β. (9) The frst phase estmate at S can be cacuated from (7) and (8) n the forward propagaton stage as ˆφ,1 = β 1 ω 1 ( 1 ) ψ 1, ˆφ 1,1 1 = β 1 ω 1 ( 1 ) ψ,1 φ 1 =1 1 Imperfect estmates are consdered n Secton V.

4 for = 2,...,M where we have used β 1ˆω ( 2) 1 = β 1ˆω 1,1 = β 1 ω 1 and where the second equaty resuts from a recursve appcaton of the frst equaty. Aong the same nes, we can use (7) and (8) n the backward propagaton stage to wrte the second phase estmate at S as ˆφ,2 = β M ω M ( M ) ψ 1, φ M for = M 1,...,1. Snce ψ 1, = ψ,1,.e. the channes have recproca phase shfts, the resutng phase at S can be wrtten as = ˆφ = β 1 ω 1 ( 1 )β M ω M ( M ) ψφ 1 φ M (10) where we have defned ψ := =1 ψ,1. Puttng t a together, the SLO phase at S s then ξ = β 1ω 1 β M ω M β t β 1 ω 1 ( 1 ) φ 1 β M ω M ( M ) φ M ψ = (β 1 ω 1 β M ω M )t γ 1 γ M ψ where the second equaty resuts from (4) and γ m := β m ω m m φ m. Hence, even though each source node possesses ts own oca noton of tme and operates ony on ts own oca estmates, each source node s abe to synthesze a synchronzed oca oscator after two-way synchronzaton. After the formaton of the SLOs, retrodrectve dstrbuted transmt beamformng can be performed usng TDD technques such as those descrbed n [1]. For notatona smpcty, assume that the destnaton s noton of tme s reference tme so that t 0 = t. After recevng the transmsson from the destnaton at frequency ω 0, each source, for exampe, estmates the frequency and phase of ths transmsson and subtract these ˆφ estmates, denoted as ˆω and, respectvey, from the SLO frequency and phase to generate the beamformng carrer { ( x (bf) (t ) = exp j (ˆω ˆω )t ˆφ )} ˆφ. (11) Assumng agan that the estmates are perfect, the sum of these carrers after propagaton to the destnaton can be wrtten as y (bf) 0 (t) = M =1 a 0, exp { j ( ωt γ ψ )} I t T (bf),0 w (bf) 0 (t) where ω := β 1 ω 1 β M ω M ω 0 and γ := γ 1 γ M γ 0. The receved power of the aggregate unmoduated carrers at the destnaton node n ths case s y (bf) 0 (t) 2 = ( a 0,) 2. Ths corresponds to the power of an dea transmt beamformer, when each source node transmts wth unt carrer amptude. V. PERFORMANCE ANALYSIS WITH ESTIMATION ERROR Estmaton errors ncurred durng two-way synchronzaton and source-destnaton channe phase estmaton as we as phase nose at each source node a ead to some oss of performance wth respect to the dea transmt beamformer. At tme t, the power of the aggregate carrers from the M source nodes receved at the destnaton can be expressed as y (bf) 0 (t) 2 = MX MX X a 2 0,m a 0,ma 0,n cos (δ m,n(t)) (12) m=1 m=1 n m where the non-dea nature of the dstrbuted beamformer s captured n the carrer offset terms between S m and S n δ m,n (t) := (ˆω m ˆω m )β m(t m ) (ˆω n ˆω n )β n (t n ) ˆφ (ˆφ m m ψ m,0) (ˆφ n ˆφ n ψ n,0 ) χ m (t) χ n (t) (13) where χ m (t) χ n (t) represents the dfference n the phase nose processes of the SLOs between S m and S n. Note that (13) s composed of three components: carrer frequency offset, nta carrer phase offset at t = 0, and phase nose. We can rewrte (13) n these terms as δ m,n (t) = ω m,n t φ m,n χ m,n (t). (14) The frequency and phase estmates n (13) can be wrtten as ˆω m = β 1ω 1 β M ω M ω m β m, (15) ˆω m = ω 0 ω m, β m (16) ˆφ m = β 1 ω 1 ( 1 m ) β M ω M ( M m ) φ 1 φ M ψ φ m (17) ˆφ m = ω 0 ( 0 m ) ψ 0,m φ 0 φ m. (18) Substtutng these expressons nto (13) aows us to wrte the frequency and phase offsets n (14) n terms of the ndvdua estmaton errors as ω m,n = ( ω m ω m ) ( ω n ω n ) (19) φ m,n = ( φ m φ m ) ( φ n φ n ) m ( ω m ω m ) n ( ω n ω n ). (20) The carrer frequency and phase offsets between S m and S n are anayzed n terms of the consttuent estmaton errors n the foowng sectons. The statstca propertes of the phase nose processes are dscussed n Secton V-D. A. Frequency and Phase Estmaton Error Statstcs To factate anayss, we assume a of the estmates are unbased and that the estmaton errors are jonty Gaussan dstrbuted. It can be shown that the covarances E{ˆω mˆω n }, E{ˆφ m ˆφn }, E{ˆω m ˆω n }, and E{ˆφ m ˆφ n } are a zero except when m = n snce observatons n dfferent tmesots are affected by ndependent nose reazatons and observatons at dfferent source nodes are aso affected by ndependent nose reazatons. It can aso be shown that a of the other ˆφ covarances are zero except E{ˆω m ˆφm } and E{ˆω m m } snce frequency and phase estmates obtaned from the same observaton at a partcuar source node are not ndependent.

5 It s possbe to bound the non-zero covarances wth the Cramer-Rao bound (CRB) [12]. Gven an N s -sampe observaton of a compex exponenta of amptude a, the CRB for the covarance of the frequency and phase estmates s [13] cov {[ω, φ] } σ2 a 2 [ 1 Ts 2Ns(Q P2 ) (n 0P) T sn s(q P 2 ) (n 0P) T sn s(q P 2 ) n 2 0 2n0PQ N s(q P 2 ) ] (21) where σ 2 s the varance of the uncorreated rea and magnary components of the ndependent, dentcay dstrbuted, zeromean, compex Gaussan nose sampes, T s s the sampng perod, n 0 s the ndex of the frst sampe of the observaton n the observer s oca tme, P := (N s 1)/2, Q := (N s 1)(2N s 1)/6, and A B means that A B s postve semdefnte. These resuts can be used as a reasonabe approxmaton for the non-zero covarances when each source node uses an unbased and effcent estmator, e.g. the maxmum kehood estmator for arge N s [12], to generate the oca phase and frequency estmates. B. Carrer Frequency Offset In the forward propagaton stage of the two-way synchronzaton protoco, the estmaton error ω ( 1) n (7) s defned wth respect to the true frequency of the sgna transmtted by S 1 n TS ( 1). In TS (1), the true frequency of transmsson s β 1 ω 1. In TS ( 1) for = 3,...,M, the true frequency of transmsson s β 1ˆω ( 2) 1. The sera nature of the transmssons n the two-way synchronzaton protoco mpes that the frequency error at S wth respect to the nta true beacon frequency β 1 ω 1 s an accumuaton of the ndvdua frequency estmaton errors,.e. ω (1) 2 ω ( 1). The same s true for the backward propagaton stage except the true frequency of the nta beacon s β M ω M. The frequency error of the SLO at S m can thus be computed from recursve appcaton of (7) for the forward and backward propagaton stages as ω m = m =2 ω ( 1) =m ω (2M 1) (22) where the frst and second sums correspond to the accumuated estmaton error at S n the forward and backward propagaton stages, respectvey. Based on (19) and the assumptons n Secton V-A, ths resut shows that the carrer frequency offsets between S m and S n are zero-mean and jonty Gaussan dstrbuted wth covarances that can be straghtforwardy computed n terms of the consttuent estmaton error covarances. C. Carrer Phase Offset Smar to the frequency estmaton errors, the phase estmaton errors n the forward and backward propagaton stages of the two-way synchronzaton protoco accumuate as the sgnas propagate through ncreasng and decreasng source node ndces. The accumuaton of phase error at S, however, s due to both consttuent phase and frequency estmaton errors. In the forward propagaton stage of the two-way synchronzaton protoco, we can recursvey appy (7) and (8) to wrte the frst oca phase estmate at S m as m ˆφ ( 1) m = β 1 ω 1 ( 1 m ) φ 1 ψ 1, m =2 m 1 φ ( 1) =2 =2 ω ( 1) ( m ) for m = 2,...,M. Smary, the second oca phase estmate obtaned durng backward propagaton at S m s X ˆφ (2M m 1) m = β Mω M( M m) φ M X =m φ (2M 1) X =m1 =m ψ,1 ω (2M 1) ( m) for m = 1,...,M 1. These estmates are summed at S m to generate the SLO phase. The resutng phase error s then φ = 1 φ ( 1) =2 =2 = ω ( 1) ( ) φ (2M 1) =1 ω (2M 1) ( ). Based on (20), (22), and the assumptons n Secton V-A, ths resut shows that the carrer phase offsets between S m and S n are zero-mean and jonty Gaussan dstrbuted wth covarances that can be straghtforwardy computed n terms of the consttuent estmaton error covarances. D. Phase Nose Phase nose causes the phase of the SLO at each source node to randomy wander from the phase obtaned at the end of the two-way synchronzaton protoco. As shown n [9], ths can estabsh a ceng on the reabe beamformng tme even n the absence of estmaton error. The phase nose χ (t) at S can be modeed as a zero-mean non-statonary Gaussan random process, ndependent of the estmaton errors, wth varance ncreasng neary wth tme,.e. σχ 2 (t) = r(t T (sync) ) for t T (sync), where T (sync) s the tme at whch S generates estmates ˆω and ˆφ. The varance parameter r s a functon of the physca propertes of the oscator ncudng ts natura frequency and physca type [14]. We assume that a source nodes share the same vaue of r but have ndependent phase nose processes. VI. NUMERICAL RESULTS Ths secton presents numerca exampes of the performance retrodrectve dstrbuted transmt beamformng n a system usng two-way source synchronzaton. To provde a far comparson wth snge-source transmsson, we normaze the transmt power of each source node by M so that the tota transmt power s fxed. We compute the mean beamformng gan wth respect to snge-source transmsson. The scenaro consdered n ths secton assumes 1 ms observatons durng the forward and backward propagaton

6 stages of the two-way synchronzaton protoco. A channes are assumed to have unt gan and a sgnas are assumed to be receved at a sgna to nose rato of 10dB. At the concuson of the fna synchronzaton tmesot, the source nodes form ther SLOs and the destnaton mmedatey broadcasts a 1 ms beacon. The CRB resuts n (21) are used to generate the jonty Gaussan consttuent estmaton errors wth approprate covarances. The beamformng power at the destnaton for each reazaton of the estmaton errors and phase nose processes s computed usng the resuts n Secton V. Fgure 3 shows the beamformng gan as a functon of tme for dfferent numbers of source nodes (M) and dfferent eves of oca oscator phase nose (r). The r = 0 resuts correspond to the case wth no phase nose and soate the effect of carrer phase and frequency offsets on the mean beamformng gan. The r = 1 resuts correspond to the case when the each source node has an ndependent phase nose process typca of a ow-cost oscator. In ths case, as expected, the mean beamformng gan degrades more qucky. In both cases, perodc resynchronzaton s necessary to prevent the nodes from sppng out of synchroncty and transmttng ncoherenty. The overhead requred for perodc resynchronzaton, however, can be ow wth respect to the amount of beamformng tme. For exampe, n the case wth M = 8 source nodes, the mean beamformng gan of source nodes wth ow-cost oscators s wthn 1dB of dea for approxmatey 240 ms. The synchronzaton tme n ths case s 14 ms, correspondng to an overhead of approxmatey 5%. Even ower overheads can be acheved by usng oscators wth better phase nose characterstcs, e.g. temperature controed oscators. mean beamformng gan (db) ncoherent beamformng tme (seconds) dea dea dea M=32, r=1 M=32, r=0 M=8, r=1 M=8, r=0 M=2, r=1 M=2, r=0 Fg. 3. Mean receved beamformng gan as a functon of beamformng tme, number of source nodes, and oca oscator phase nose parameter. The performance gap wth respect to dea, however, tends to be arger when M s arge because the amount of tme spent synchronzng the nodes eads to arger nta phase offsets at the start of beamformng. VII. CONCLUSION Ths paper presented the two-way carrer synchronzaton protoco and descrbed ts use n retrodrectve dstrbuted transmt beamformng. An anayss of the statstca propertes of the phase and frequency estmaton errors and resutng power of a retrodrectve dstrbuted transmt beamformer was aso provded. Numerca exampes characterzng the performance of a dstrbuted transmt beamformer n a system usng two-way synchronzaton were presented and demonstrated that near-dea beamformng performance can be acheved wth ow synchronzaton overhead. REFERENCES [1] C. Pon, Retrodrectve array usng the heterodyne technque, IEEE Trans. on Antennas and Prop., vo. 12, no. 2, pp , Mar [2] V. Jungncke, T. Wrth, M. Schemann, T. Hausten, and W. Zrwas, Synchronzaton of cooperatve base statons, n IEEE Int. Symp. on Wreess Communcaton Systems (ISWCS), October 2008, pp [3] Y. Tu and G. Potte, Coherent cooperatve transmsson from mutpe adjacent antennas to a dstant statonary antenna through AWGN channes, n IEEE Vehcuar Technoogy Conf. (VTC), vo. 1, Brmngham, AL, Sprng 2002, pp [4] R. Mudumba, J. Hespanha, U. Madhow, and G. Barrac, Scaabe feedback contro for dstrbuted beamformng n sensor networks, n IEEE Internatona Symp. on Informaton Theory (ISIT), Adeade, Austraa, September 2005, pp [5] R. Mudumba, B. Wd, U. Madhow, and K. Ramchandran, Dstrbuted beamformng usng 1 bt feedback: from concept to reazaton, n 44th Aerton Conf. on Comm., Contro, and Computng, Montceo, IL, Sep. 2006, pp [6] R. Mudumba, J. Hespanha, U. Madhow, and G. Barrac, Dstrbuted transmt beamformng usng feedback contro, IEEE Trans. on Informaton Theory, n revew. [7] R. Mudumba, G. Barrac, and U. Madhow, On the feasbty of dstrbuted beamformng n wreess networks, IEEE Trans. on Wreess Communcatons, vo. 6, no. 5, pp , May [8] D.R. Brown III, G. Prnce, and J. McNe, A method for carrer frequency and phase synch. of two autonomous cooperatve transmtters, n IEEE Sgna Proc. Advances n Wreess Comm. (SPAWC), New York, NY, June 5-8, 2005, pp [9] D.R. Brown III and H.V. Poor, Tme-sotted round-trp carrer synchronzaton for dstrbuted beamformng, IEEE Trans. on Sgna Processng, vo. 56, no. 11, pp , November [10] R. Mudumba, D.R. Brown III, U. Madhow, and H.V. Poor, Dstrbuted transmt beamformng: Chaenges and recent progress, IEEE Communcatons Magazne, vo. 47, no. 2, pp , February [11] R. D. Preuss and T. P. Bdgare, Methods and systems for dstrbuted synchronzaton, U.S. Patent Appcaton 12/383,192, March 19, [12] H.V. Poor, An Introducton to Sgna Detecton and Estmaton, 2nd ed. New York: Sprnger-Verag, [13] D. Rfe and R. Boorstyn, Snge-tone parameter estmaton from dscrete-tme observatons, IEEE Trans. on Informaton Theory, vo. 20, no. 5, pp , September [14] A. Demr, A. Mehrotra, and J. Roychowdhury, Phase nose n oscators: A unfyng theory and numerca methods and characterzaton, IEEE Trans. on Crcuts and Systems I: Fund. Theory and App., vo. 47, no. 5, pp , May The resuts n Fgure 3 aso show that ncreasng the number of source nodes partcpatng n the dstrbuted transmt beamformer ncreases the mean receved power at the destnaton, up to the pont n tme when ncoherent transmsson begns.

Optimum Selection Combining for M-QAM on Fading Channels

Optimum Selection Combining for M-QAM on Fading Channels Optmum Seecton Combnng for M-QAM on Fadng Channes M. Surendra Raju, Ramesh Annavajjaa and A. Chockangam Insca Semconductors Inda Pvt. Ltd, Bangaore-56000, Inda Department of ECE, Unversty of Caforna, San

More information

Research on Complex Networks Control Based on Fuzzy Integral Sliding Theory

Research on Complex Networks Control Based on Fuzzy Integral Sliding Theory Advanced Scence and Technoogy Letters Vo.83 (ISA 205), pp.60-65 http://dx.do.org/0.4257/ast.205.83.2 Research on Compex etworks Contro Based on Fuzzy Integra Sdng Theory Dongsheng Yang, Bngqng L, 2, He

More information

Nested case-control and case-cohort studies

Nested case-control and case-cohort studies Outne: Nested case-contro and case-cohort studes Ørnuf Borgan Department of Mathematcs Unversty of Oso NORBIS course Unversty of Oso 4-8 December 217 1 Radaton and breast cancer data Nested case contro

More information

Delay tomography for large scale networks

Delay tomography for large scale networks Deay tomography for arge scae networks MENG-FU SHIH ALFRED O. HERO III Communcatons and Sgna Processng Laboratory Eectrca Engneerng and Computer Scence Department Unversty of Mchgan, 30 Bea. Ave., Ann

More information

Neural network-based athletics performance prediction optimization model applied research

Neural network-based athletics performance prediction optimization model applied research Avaabe onne www.jocpr.com Journa of Chemca and Pharmaceutca Research, 04, 6(6):8-5 Research Artce ISSN : 0975-784 CODEN(USA) : JCPRC5 Neura networ-based athetcs performance predcton optmzaton mode apped

More information

COXREG. Estimation (1)

COXREG. Estimation (1) COXREG Cox (972) frst suggested the modes n whch factors reated to fetme have a mutpcatve effect on the hazard functon. These modes are caed proportona hazards (PH) modes. Under the proportona hazards

More information

Interference Alignment and Degrees of Freedom Region of Cellular Sigma Channel

Interference Alignment and Degrees of Freedom Region of Cellular Sigma Channel 2011 IEEE Internatona Symposum on Informaton Theory Proceedngs Interference Agnment and Degrees of Freedom Regon of Ceuar Sgma Channe Huaru Yn 1 Le Ke 2 Zhengdao Wang 2 1 WINLAB Dept of EEIS Unv. of Sc.

More information

A finite difference method for heat equation in the unbounded domain

A finite difference method for heat equation in the unbounded domain Internatona Conerence on Advanced ectronc Scence and Technoogy (AST 6) A nte derence method or heat equaton n the unbounded doman a Quan Zheng and Xn Zhao Coege o Scence North Chna nversty o Technoogy

More information

On Uplink-Downlink Sum-MSE Duality of Multi-hop MIMO Relay Channel

On Uplink-Downlink Sum-MSE Duality of Multi-hop MIMO Relay Channel On Upn-Downn Sum-MSE Duat of Mut-hop MIMO Rea Channe A Cagata Cr, Muhammad R. A. handaer, Yue Rong and Yngbo ua Department of Eectrca Engneerng, Unverst of Caforna Rversde, Rversde, CA, 95 Centre for Wreess

More information

MARKOV CHAIN AND HIDDEN MARKOV MODEL

MARKOV CHAIN AND HIDDEN MARKOV MODEL MARKOV CHAIN AND HIDDEN MARKOV MODEL JIAN ZHANG JIANZHAN@STAT.PURDUE.EDU Markov chan and hdden Markov mode are probaby the smpest modes whch can be used to mode sequenta data,.e. data sampes whch are not

More information

Demodulation of PPM signal based on sequential Monte Carlo model

Demodulation of PPM signal based on sequential Monte Carlo model Internatona Journa of Computer Scence and Eectroncs Engneerng (IJCSEE) Voume 1, Issue 1 (213) ISSN 232 428 (Onne) Demoduaton of M sgna based on seuenta Monte Caro mode Lun Huang and G. E. Atkn Abstract

More information

ECE559VV Project Report

ECE559VV Project Report ECE559VV Project Report (Supplementary Notes Loc Xuan Bu I. MAX SUM-RATE SCHEDULING: THE UPLINK CASE We have seen (n the presentaton that, for downlnk (broadcast channels, the strategy maxmzng the sum-rate

More information

3. Stress-strain relationships of a composite layer

3. Stress-strain relationships of a composite layer OM PO I O U P U N I V I Y O F W N ompostes ourse 8-9 Unversty of wente ng. &ech... tress-stran reatonshps of a composte ayer - Laurent Warnet & emo Aerman.. tress-stran reatonshps of a composte ayer Introducton

More information

Supplementary Material: Learning Structured Weight Uncertainty in Bayesian Neural Networks

Supplementary Material: Learning Structured Weight Uncertainty in Bayesian Neural Networks Shengyang Sun, Changyou Chen, Lawrence Carn Suppementary Matera: Learnng Structured Weght Uncertanty n Bayesan Neura Networks Shengyang Sun Changyou Chen Lawrence Carn Tsnghua Unversty Duke Unversty Duke

More information

Structure and Drive Paul A. Jensen Copyright July 20, 2003

Structure and Drive Paul A. Jensen Copyright July 20, 2003 Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.

More information

L-Edge Chromatic Number Of A Graph

L-Edge Chromatic Number Of A Graph IJISET - Internatona Journa of Innovatve Scence Engneerng & Technoogy Vo. 3 Issue 3 March 06. ISSN 348 7968 L-Edge Chromatc Number Of A Graph Dr.R.B.Gnana Joth Assocate Professor of Mathematcs V.V.Vannaperuma

More information

The Concept of Beamforming

The Concept of Beamforming ELG513 Smart Antennas S.Loyka he Concept of Beamformng Generc representaton of the array output sgnal, 1 where w y N 1 * = 1 = w x = w x (4.1) complex weghts, control the array pattern; y and x - narrowband

More information

Achieving Optimal Throughput Utility and Low Delay with CSMA-like Algorithms: A Virtual Multi-Channel Approach

Achieving Optimal Throughput Utility and Low Delay with CSMA-like Algorithms: A Virtual Multi-Channel Approach IEEE/AM TRANSATIONS ON NETWORKING, VOL. X, NO. XX, XXXXXXX 20X Achevng Optma Throughput Utty and Low Deay wth SMA-ke Agorthms: A Vrtua Mut-hanne Approach Po-Ka Huang, Student Member, IEEE, and Xaojun Ln,

More information

Achieving Optimal Throughput Utility and Low Delay with CSMA-like Algorithms: A Virtual Multi-Channel Approach

Achieving Optimal Throughput Utility and Low Delay with CSMA-like Algorithms: A Virtual Multi-Channel Approach Achevng Optma Throughput Utty and Low Deay wth SMA-ke Agorthms: A Vrtua Mut-hanne Approach Po-Ka Huang, Student Member, IEEE, and Xaojun Ln, Senor Member, IEEE Abstract SMA agorthms have recenty receved

More information

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

More information

Single-Source/Sink Network Error Correction Is as Hard as Multiple-Unicast

Single-Source/Sink Network Error Correction Is as Hard as Multiple-Unicast Snge-Source/Snk Network Error Correcton Is as Hard as Mutpe-Uncast Wentao Huang and Tracey Ho Department of Eectrca Engneerng Caforna Insttute of Technoogy Pasadena, CA {whuang,tho}@catech.edu Mchae Langberg

More information

On the Power Function of the Likelihood Ratio Test for MANOVA

On the Power Function of the Likelihood Ratio Test for MANOVA Journa of Mutvarate Anayss 8, 416 41 (00) do:10.1006/jmva.001.036 On the Power Functon of the Lkehood Rato Test for MANOVA Dua Kumar Bhaumk Unversty of South Aabama and Unversty of Inos at Chcago and Sanat

More information

Performance of SDMA Systems Using Transmitter Preprocessing Based on Noisy Feedback of Vector-Quantized Channel Impulse Responses

Performance of SDMA Systems Using Transmitter Preprocessing Based on Noisy Feedback of Vector-Quantized Channel Impulse Responses Performance of SDMA Systems Usng Transmtter Preprocessng Based on Nosy Feedback of Vector-Quantzed Channe Impuse Responses Du Yang, Le-Lang Yang and Lajos Hanzo Schoo of ECS, Unversty of Southampton, SO7

More information

Chapter - 2. Distribution System Power Flow Analysis

Chapter - 2. Distribution System Power Flow Analysis Chapter - 2 Dstrbuton System Power Flow Analyss CHAPTER - 2 Radal Dstrbuton System Load Flow 2.1 Introducton Load flow s an mportant tool [66] for analyzng electrcal power system network performance. Load

More information

Consider the following passband digital communication system model. c t. modulator. t r a n s m i t t e r. signal decoder.

Consider the following passband digital communication system model. c t. modulator. t r a n s m i t t e r. signal decoder. PASSBAND DIGITAL MODULATION TECHNIQUES Consder the followng passband dgtal communcaton system model. cos( ω + φ ) c t message source m sgnal encoder s modulator s () t communcaton xt () channel t r a n

More information

Polite Water-filling for Weighted Sum-rate Maximization in MIMO B-MAC Networks under. Multiple Linear Constraints

Polite Water-filling for Weighted Sum-rate Maximization in MIMO B-MAC Networks under. Multiple Linear Constraints 2011 IEEE Internatona Symposum on Informaton Theory Proceedngs Pote Water-fng for Weghted Sum-rate Maxmzaton n MIMO B-MAC Networks under Mutpe near Constrants An u 1, Youjan u 2, Vncent K. N. au 3, Hage

More information

Optimal and Suboptimal Linear Receivers for Time-Hopping Impulse Radio Systems

Optimal and Suboptimal Linear Receivers for Time-Hopping Impulse Radio Systems MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.mer.com Optma and Suboptma Lnear Recevers for Tme-Hoppng Impuse Rado Systems Snan Gezc Hsash Kbayash H. Vncent Poor Andreas F. Mosch TR2004-052 May

More information

Outline. Communication. Bellman Ford Algorithm. Bellman Ford Example. Bellman Ford Shortest Path [1]

Outline. Communication. Bellman Ford Algorithm. Bellman Ford Example. Bellman Ford Shortest Path [1] DYNAMIC SHORTEST PATH SEARCH AND SYNCHRONIZED TASK SWITCHING Jay Wagenpfel, Adran Trachte 2 Outlne Shortest Communcaton Path Searchng Bellmann Ford algorthm Algorthm for dynamc case Modfcatons to our algorthm

More information

Lecture 3: Shannon s Theorem

Lecture 3: Shannon s Theorem CSE 533: Error-Correctng Codes (Autumn 006 Lecture 3: Shannon s Theorem October 9, 006 Lecturer: Venkatesan Guruswam Scrbe: Wdad Machmouch 1 Communcaton Model The communcaton model we are usng conssts

More information

Downlink Power Allocation for CoMP-NOMA in Multi-Cell Networks

Downlink Power Allocation for CoMP-NOMA in Multi-Cell Networks Downn Power Aocaton for CoMP-NOMA n Mut-Ce Networs Md Shpon A, Eram Hossan, Arafat A-Dwe, and Dong In Km arxv:80.0498v [eess.sp] 6 Dec 207 Abstract Ths wor consders the probem of dynamc power aocaton n

More information

Chapter 6. Rotations and Tensors

Chapter 6. Rotations and Tensors Vector Spaces n Physcs 8/6/5 Chapter 6. Rotatons and ensors here s a speca knd of near transformaton whch s used to transforms coordnates from one set of axes to another set of axes (wth the same orgn).

More information

Image Classification Using EM And JE algorithms

Image Classification Using EM And JE algorithms Machne earnng project report Fa, 2 Xaojn Sh, jennfer@soe Image Cassfcaton Usng EM And JE agorthms Xaojn Sh Department of Computer Engneerng, Unversty of Caforna, Santa Cruz, CA, 9564 jennfer@soe.ucsc.edu

More information

Correspondence. Performance Evaluation for MAP State Estimate Fusion I. INTRODUCTION

Correspondence. Performance Evaluation for MAP State Estimate Fusion I. INTRODUCTION Correspondence Performance Evauaton for MAP State Estmate Fuson Ths paper presents a quanttatve performance evauaton method for the maxmum a posteror (MAP) state estmate fuson agorthm. Under dea condtons

More information

Chapter 7 Channel Capacity and Coding

Chapter 7 Channel Capacity and Coding Wreless Informaton Transmsson System Lab. Chapter 7 Channel Capacty and Codng Insttute of Communcatons Engneerng atonal Sun Yat-sen Unversty Contents 7. Channel models and channel capacty 7.. Channel models

More information

Distributed Moving Horizon State Estimation of Nonlinear Systems. Jing Zhang

Distributed Moving Horizon State Estimation of Nonlinear Systems. Jing Zhang Dstrbuted Movng Horzon State Estmaton of Nonnear Systems by Jng Zhang A thess submtted n parta fufment of the requrements for the degree of Master of Scence n Chemca Engneerng Department of Chemca and

More information

Composite Hypotheses testing

Composite Hypotheses testing Composte ypotheses testng In many hypothess testng problems there are many possble dstrbutons that can occur under each of the hypotheses. The output of the source s a set of parameters (ponts n a parameter

More information

Introduction to Antennas & Arrays

Introduction to Antennas & Arrays Introducton to Antennas & Arrays Antenna transton regon (structure) between guded eaves (.e. coaxal cable) and free space waves. On transmsson, antenna accepts energy from TL and radates t nto space. J.D.

More information

Greyworld White Balancing with Low Computation Cost for On- Board Video Capturing

Greyworld White Balancing with Low Computation Cost for On- Board Video Capturing reyword Whte aancng wth Low Computaton Cost for On- oard Vdeo Capturng Peng Wu Yuxn Zoe) Lu Hewett-Packard Laboratores Hewett-Packard Co. Pao Ato CA 94304 USA Abstract Whte baancng s a process commony

More information

Example: Suppose we want to build a classifier that recognizes WebPages of graduate students.

Example: Suppose we want to build a classifier that recognizes WebPages of graduate students. Exampe: Suppose we want to bud a cassfer that recognzes WebPages of graduate students. How can we fnd tranng data? We can browse the web and coect a sampe of WebPages of graduate students of varous unverstes.

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Analyss of Varance and Desgn of Experment-I MODULE VII LECTURE - 3 ANALYSIS OF COVARIANCE Dr Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur Any scentfc experment s performed

More information

Autonomous State Space Models for Recursive Signal Estimation Beyond Least Squares

Autonomous State Space Models for Recursive Signal Estimation Beyond Least Squares Autonomous State Space Modes for Recursve Sgna Estmaton Beyond Least Suares Nour Zama, Reto A Wdhaber, and Hans-Andrea Loeger ETH Zurch, Dept of Informaton Technoogy & Eectrca Engneerng ETH Zurch & Bern

More information

Resource Allocation with a Budget Constraint for Computing Independent Tasks in the Cloud

Resource Allocation with a Budget Constraint for Computing Independent Tasks in the Cloud Resource Allocaton wth a Budget Constrant for Computng Independent Tasks n the Cloud Wemng Sh and Bo Hong School of Electrcal and Computer Engneerng Georga Insttute of Technology, USA 2nd IEEE Internatonal

More information

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction ECONOMICS 5* -- NOTE (Summary) ECON 5* -- NOTE The Multple Classcal Lnear Regresson Model (CLRM): Specfcaton and Assumptons. Introducton CLRM stands for the Classcal Lnear Regresson Model. The CLRM s also

More information

GENERATION OF GOLD-SEQUENCES WITH APPLICATIONS TO SPREAD SPECTRUM SYSTEMS

GENERATION OF GOLD-SEQUENCES WITH APPLICATIONS TO SPREAD SPECTRUM SYSTEMS GENERATION OF GOLD-SEQUENCES WITH APPLICATIONS TO SPREAD SPECTRUM SYSTEMS F. Rodríguez Henríquez (1), Member, IEEE, N. Cruz Cortés (1), Member, IEEE, J.M. Rocha-Pérez (2) Member, IEEE. F. Amaro Sánchez

More information

Numerical Investigation of Power Tunability in Two-Section QD Superluminescent Diodes

Numerical Investigation of Power Tunability in Two-Section QD Superluminescent Diodes Numerca Investgaton of Power Tunabty n Two-Secton QD Superumnescent Dodes Matta Rossett Paoo Bardea Ivo Montrosset POLITECNICO DI TORINO DELEN Summary 1. A smpfed mode for QD Super Lumnescent Dodes (SLD)

More information

Uncertainty Specification and Propagation for Loss Estimation Using FOSM Methods

Uncertainty Specification and Propagation for Loss Estimation Using FOSM Methods Uncertanty Specfcaton and Propagaton for Loss Estmaton Usng FOSM Methods J.W. Baer and C.A. Corne Dept. of Cv and Envronmenta Engneerng, Stanford Unversty, Stanford, CA 94305-400 Keywords: Sesmc, oss estmaton,

More information

[WAVES] 1. Waves and wave forces. Definition of waves

[WAVES] 1. Waves and wave forces. Definition of waves 1. Waves and forces Defnton of s In the smuatons on ong-crested s are consdered. The drecton of these s (μ) s defned as sketched beow n the goba co-ordnate sstem: North West East South The eevaton can

More information

Globally Optimal Multisensor Distributed Random Parameter Matrices Kalman Filtering Fusion with Applications

Globally Optimal Multisensor Distributed Random Parameter Matrices Kalman Filtering Fusion with Applications Sensors 2008, 8, 8086-8103; DOI: 10.3390/s8128086 OPEN ACCESS sensors ISSN 1424-8220 www.mdp.com/journa/sensors Artce Gobay Optma Mutsensor Dstrbuted Random Parameter Matrces Kaman Fterng Fuson wth Appcatons

More information

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4) I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes

More information

A General Method for SER Computation of M-PAM and M-PPM UWB Systems for Indoor Multiuser Communications

A General Method for SER Computation of M-PAM and M-PPM UWB Systems for Indoor Multiuser Communications A Genera Method for SER Computaton of M-PAM and M-PPM UWB Systems for Indoor Mutuser Communcatons G Durs Isttuto Superore Maro Boea Corso Trento 21 I-10129 Torno, Itay E-ma: durs@smt J Romme IMST GmH Car

More information

Associative Memories

Associative Memories Assocatve Memores We consder now modes for unsupervsed earnng probems, caed auto-assocaton probems. Assocaton s the task of mappng patterns to patterns. In an assocatve memory the stmuus of an ncompete

More information

ON AUTOMATIC CONTINUITY OF DERIVATIONS FOR BANACH ALGEBRAS WITH INVOLUTION

ON AUTOMATIC CONTINUITY OF DERIVATIONS FOR BANACH ALGEBRAS WITH INVOLUTION European Journa of Mathematcs and Computer Scence Vo. No. 1, 2017 ON AUTOMATC CONTNUTY OF DERVATONS FOR BANACH ALGEBRAS WTH NVOLUTON Mohamed BELAM & Youssef T DL MATC Laboratory Hassan Unversty MORO CCO

More information

Low Complexity Soft-Input Soft-Output Hamming Decoder

Low Complexity Soft-Input Soft-Output Hamming Decoder Low Complexty Soft-Input Soft-Output Hammng Der Benjamn Müller, Martn Holters, Udo Zölzer Helmut Schmdt Unversty Unversty of the Federal Armed Forces Department of Sgnal Processng and Communcatons Holstenhofweg

More information

Journal of Multivariate Analysis

Journal of Multivariate Analysis Journa of Mutvarate Anayss 3 (04) 74 96 Contents sts avaabe at ScenceDrect Journa of Mutvarate Anayss journa homepage: www.esever.com/ocate/jmva Hgh-dmensona sparse MANOVA T. Tony Ca a, Yn Xa b, a Department

More information

Distributed Transmit Beamforming using Feedback Control

Distributed Transmit Beamforming using Feedback Control 1 Dstrbuted Transmt Beamformng usng Feedback Control R. Mudumba, Student Member, IEEE, J. Hespanha, Senor Member, IEEE, U. Madhow, Fellow, IEEE and G. Barrac, Member, IEEE arxv:cs/6372v1 [cs.it] 18 Mar

More information

Chapter 7 Channel Capacity and Coding

Chapter 7 Channel Capacity and Coding Chapter 7 Channel Capacty and Codng Contents 7. Channel models and channel capacty 7.. Channel models Bnary symmetrc channel Dscrete memoryless channels Dscrete-nput, contnuous-output channel Waveform

More information

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family IOSR Journal of Mathematcs IOSR-JM) ISSN: 2278-5728. Volume 3, Issue 3 Sep-Oct. 202), PP 44-48 www.osrjournals.org Usng T.O.M to Estmate Parameter of dstrbutons that have not Sngle Exponental Famly Jubran

More information

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification E395 - Pattern Recognton Solutons to Introducton to Pattern Recognton, Chapter : Bayesan pattern classfcaton Preface Ths document s a soluton manual for selected exercses from Introducton to Pattern Recognton

More information

Adaptive Beamforming in Multi path fading Channels for Voice Enhancements

Adaptive Beamforming in Multi path fading Channels for Voice Enhancements Adaptve Beamformng n ut path fadng Channes for Voce Enhancements usnan Abbas Internatona Isamc Unversty Isamabad Waqas Ahmed Internatona Isamc Unversty Isamabad Shehzad Ashraf Internatona Isamc Unversty

More information

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:

More information

Secret Communication using Artificial Noise

Secret Communication using Artificial Noise Secret Communcaton usng Artfcal Nose Roht Neg, Satashu Goel C Department, Carnege Mellon Unversty, PA 151, USA {neg,satashug}@ece.cmu.edu Abstract The problem of secret communcaton between two nodes over

More information

Multispectral Remote Sensing Image Classification Algorithm Based on Rough Set Theory

Multispectral Remote Sensing Image Classification Algorithm Based on Rough Set Theory Proceedngs of the 2009 IEEE Internatona Conference on Systems Man and Cybernetcs San Antono TX USA - October 2009 Mutspectra Remote Sensng Image Cassfcaton Agorthm Based on Rough Set Theory Yng Wang Xaoyun

More information

4 Analysis of Variance (ANOVA) 5 ANOVA. 5.1 Introduction. 5.2 Fixed Effects ANOVA

4 Analysis of Variance (ANOVA) 5 ANOVA. 5.1 Introduction. 5.2 Fixed Effects ANOVA 4 Analyss of Varance (ANOVA) 5 ANOVA 51 Introducton ANOVA ANOVA s a way to estmate and test the means of multple populatons We wll start wth one-way ANOVA If the populatons ncluded n the study are selected

More information

G : Statistical Mechanics

G : Statistical Mechanics G25.2651: Statstca Mechancs Notes for Lecture 11 I. PRINCIPLES OF QUANTUM STATISTICAL MECHANICS The probem of quantum statstca mechancs s the quantum mechanca treatment of an N-partce system. Suppose the

More information

22.51 Quantum Theory of Radiation Interactions

22.51 Quantum Theory of Radiation Interactions .51 Quantum Theory of Radaton Interactons Fna Exam - Soutons Tuesday December 15, 009 Probem 1 Harmonc oscator 0 ponts Consder an harmonc oscator descrbed by the Hamtonan H = ω(nˆ + ). Cacuate the evouton

More information

LECTURE 21 Mohr s Method for Calculation of General Displacements. 1 The Reciprocal Theorem

LECTURE 21 Mohr s Method for Calculation of General Displacements. 1 The Reciprocal Theorem V. DEMENKO MECHANICS OF MATERIALS 05 LECTURE Mohr s Method for Cacuaton of Genera Dspacements The Recproca Theorem The recproca theorem s one of the genera theorems of strength of materas. It foows drect

More information

Xin Li Department of Information Systems, College of Business, City University of Hong Kong, Hong Kong, CHINA

Xin Li Department of Information Systems, College of Business, City University of Hong Kong, Hong Kong, CHINA RESEARCH ARTICLE MOELING FIXE OS BETTING FOR FUTURE EVENT PREICTION Weyun Chen eartment of Educatona Informaton Technoogy, Facuty of Educaton, East Chna Norma Unversty, Shangha, CHINA {weyun.chen@qq.com}

More information

/ n ) are compared. The logic is: if the two

/ n ) are compared. The logic is: if the two STAT C141, Sprng 2005 Lecture 13 Two sample tests One sample tests: examples of goodness of ft tests, where we are testng whether our data supports predctons. Two sample tests: called as tests of ndependence

More information

On the correction of the h-index for career length

On the correction of the h-index for career length 1 On the correcton of the h-ndex for career length by L. Egghe Unverstet Hasselt (UHasselt), Campus Depenbeek, Agoralaan, B-3590 Depenbeek, Belgum 1 and Unverstet Antwerpen (UA), IBW, Stadscampus, Venusstraat

More information

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U) Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of

More information

Notes on Frequency Estimation in Data Streams

Notes on Frequency Estimation in Data Streams Notes on Frequency Estmaton n Data Streams In (one of) the data streamng model(s), the data s a sequence of arrvals a 1, a 2,..., a m of the form a j = (, v) where s the dentty of the tem and belongs to

More information

STAT 511 FINAL EXAM NAME Spring 2001

STAT 511 FINAL EXAM NAME Spring 2001 STAT 5 FINAL EXAM NAME Sprng Instructons: Ths s a closed book exam. No notes or books are allowed. ou may use a calculator but you are not allowed to store notes or formulas n the calculator. Please wrte

More information

Lecture 8: Time & Clocks. CDK: Sections TVS: Sections

Lecture 8: Time & Clocks. CDK: Sections TVS: Sections Lecture 8: Tme & Clocks CDK: Sectons 11.1 11.4 TVS: Sectons 6.1 6.2 Topcs Synchronzaton Logcal tme (Lamport) Vector clocks We assume there are benefts from havng dfferent systems n a network able to agree

More information

Research Article H Estimates for Discrete-Time Markovian Jump Linear Systems

Research Article H Estimates for Discrete-Time Markovian Jump Linear Systems Mathematca Probems n Engneerng Voume 213 Artce ID 945342 7 pages http://dxdoorg/11155/213/945342 Research Artce H Estmates for Dscrete-Tme Markovan Jump Lnear Systems Marco H Terra 1 Gdson Jesus 2 and

More information

Suppose that there s a measured wndow of data fff k () ; :::; ff k g of a sze w, measured dscretely wth varable dscretzaton step. It s convenent to pl

Suppose that there s a measured wndow of data fff k () ; :::; ff k g of a sze w, measured dscretely wth varable dscretzaton step. It s convenent to pl RECURSIVE SPLINE INTERPOLATION METHOD FOR REAL TIME ENGINE CONTROL APPLICATIONS A. Stotsky Volvo Car Corporaton Engne Desgn and Development Dept. 97542, HA1N, SE- 405 31 Gothenburg Sweden. Emal: astotsky@volvocars.com

More information

Performing Modulation Scheme of Chaos Shift Keying with Hyperchaotic Chen System

Performing Modulation Scheme of Chaos Shift Keying with Hyperchaotic Chen System 6 th Internatonal Advanced echnologes Symposum (IAS 11), 16-18 May 011, Elazığ, urkey Performng Modulaton Scheme of Chaos Shft Keyng wth Hyperchaotc Chen System H. Oğraş 1, M. ürk 1 Unversty of Batman,

More information

ECONOMICS 351*-A Mid-Term Exam -- Fall Term 2000 Page 1 of 13 pages. QUEEN'S UNIVERSITY AT KINGSTON Department of Economics

ECONOMICS 351*-A Mid-Term Exam -- Fall Term 2000 Page 1 of 13 pages. QUEEN'S UNIVERSITY AT KINGSTON Department of Economics ECOOMICS 35*-A Md-Term Exam -- Fall Term 000 Page of 3 pages QUEE'S UIVERSITY AT KIGSTO Department of Economcs ECOOMICS 35* - Secton A Introductory Econometrcs Fall Term 000 MID-TERM EAM ASWERS MG Abbott

More information

DISTRIBUTED PROCESSING OVER ADAPTIVE NETWORKS. Cassio G. Lopes and Ali H. Sayed

DISTRIBUTED PROCESSING OVER ADAPTIVE NETWORKS. Cassio G. Lopes and Ali H. Sayed DISTRIBUTED PROCESSIG OVER ADAPTIVE ETWORKS Casso G Lopes and A H Sayed Department of Eectrca Engneerng Unversty of Caforna Los Angees, CA, 995 Ema: {casso, sayed@eeucaedu ABSTRACT Dstrbuted adaptve agorthms

More information

Cyclic Codes BCH Codes

Cyclic Codes BCH Codes Cycc Codes BCH Codes Gaos Feds GF m A Gaos fed of m eements can be obtaned usng the symbos 0,, á, and the eements beng 0,, á, á, á 3 m,... so that fed F* s cosed under mutpcaton wth m eements. The operator

More information

Signal space Review on vector space Linear independence Metric space and norm Inner product

Signal space Review on vector space Linear independence Metric space and norm Inner product Sgnal space.... Revew on vector space.... Lnear ndependence... 3.3 Metrc space and norm... 4.4 Inner product... 5.5 Orthonormal bass... 7.6 Waveform communcaton system... 9.7 Some examples... 6 Sgnal space

More information

EEE 241: Linear Systems

EEE 241: Linear Systems EEE : Lnear Systems Summary #: Backpropagaton BACKPROPAGATION The perceptron rule as well as the Wdrow Hoff learnng were desgned to tran sngle layer networks. They suffer from the same dsadvantage: they

More information

LOW-DENSITY Parity-Check (LDPC) codes have received

LOW-DENSITY Parity-Check (LDPC) codes have received IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 59, NO. 7, JULY 2011 1807 Successve Maxmzaton for Systematc Desgn of Unversay Capacty Approachng Rate-Compatbe Sequences of LDPC Code Ensembes over Bnary-Input

More information

The optimal delay of the second test is therefore approximately 210 hours earlier than =2.

The optimal delay of the second test is therefore approximately 210 hours earlier than =2. THE IEC 61508 FORMULAS 223 The optmal delay of the second test s therefore approxmately 210 hours earler than =2. 8.4 The IEC 61508 Formulas IEC 61508-6 provdes approxmaton formulas for the PF for smple

More information

COMBINING SPATIAL COMPONENTS IN SEISMIC DESIGN

COMBINING SPATIAL COMPONENTS IN SEISMIC DESIGN Transactons, SMRT- COMBINING SPATIAL COMPONENTS IN SEISMIC DESIGN Mchae O Leary, PhD, PE and Kevn Huberty, PE, SE Nucear Power Technooges Dvson, Sargent & Lundy, Chcago, IL 6060 ABSTRACT Accordng to Reguatory

More information

A DIMENSION-REDUCTION METHOD FOR STOCHASTIC ANALYSIS SECOND-MOMENT ANALYSIS

A DIMENSION-REDUCTION METHOD FOR STOCHASTIC ANALYSIS SECOND-MOMENT ANALYSIS A DIMESIO-REDUCTIO METHOD FOR STOCHASTIC AALYSIS SECOD-MOMET AALYSIS S. Rahman Department of Mechanca Engneerng and Center for Computer-Aded Desgn The Unversty of Iowa Iowa Cty, IA 52245 June 2003 OUTLIE

More information

Scientia Iranica, Vol. 13, No. 4, pp 337{347 c Sharif University of Technology, October 2006 Performance Evaluations and Comparisons of Several LDPC C

Scientia Iranica, Vol. 13, No. 4, pp 337{347 c Sharif University of Technology, October 2006 Performance Evaluations and Comparisons of Several LDPC C Scenta Iranca, Vo. 13, No. 4, pp 337{347 c Sharf Unversty of Technoogy, Octoer 006 Performance Evauatons and Comparsons of Severa LDPC Coded MC-FH-CDMA Systems H. Behrooz, J. Haghghat 1, M. Nasr-Kenar

More information

A Hybrid Variational Iteration Method for Blasius Equation

A Hybrid Variational Iteration Method for Blasius Equation Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 1932-9466 Vol. 10, Issue 1 (June 2015), pp. 223-229 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) A Hybrd Varatonal Iteraton Method

More information

PHYS 450 Spring semester Lecture 02: Dealing with Experimental Uncertainties. Ron Reifenberger Birck Nanotechnology Center Purdue University

PHYS 450 Spring semester Lecture 02: Dealing with Experimental Uncertainties. Ron Reifenberger Birck Nanotechnology Center Purdue University PHYS 45 Sprng semester 7 Lecture : Dealng wth Expermental Uncertantes Ron Refenberger Brck anotechnology Center Purdue Unversty Lecture Introductory Comments Expermental errors (really expermental uncertantes)

More information

Topic 23 - Randomized Complete Block Designs (RCBD)

Topic 23 - Randomized Complete Block Designs (RCBD) Topc 3 ANOVA (III) 3-1 Topc 3 - Randomzed Complete Block Desgns (RCBD) Defn: A Randomzed Complete Block Desgn s a varant of the completely randomzed desgn (CRD) that we recently learned. In ths desgn,

More information

ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM

ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM An elastc wave s a deformaton of the body that travels throughout the body n all drectons. We can examne the deformaton over a perod of tme by fxng our look

More information

Lab 2e Thermal System Response and Effective Heat Transfer Coefficient

Lab 2e Thermal System Response and Effective Heat Transfer Coefficient 58:080 Expermental Engneerng 1 OBJECTIVE Lab 2e Thermal System Response and Effectve Heat Transfer Coeffcent Warnng: though the experment has educatonal objectves (to learn about bolng heat transfer, etc.),

More information

Optimization of JK Flip Flop Layout with Minimal Average Power of Consumption based on ACOR, Fuzzy-ACOR, GA, and Fuzzy-GA

Optimization of JK Flip Flop Layout with Minimal Average Power of Consumption based on ACOR, Fuzzy-ACOR, GA, and Fuzzy-GA Journa of mathematcs and computer Scence 4 (05) - 5 Optmzaton of JK Fp Fop Layout wth Mnma Average Power of Consumpton based on ACOR, Fuzzy-ACOR, GA, and Fuzzy-GA Farshd Kevanan *,, A Yekta *,, Nasser

More information

Note 2. Ling fong Li. 1 Klein Gordon Equation Probablity interpretation Solutions to Klein-Gordon Equation... 2

Note 2. Ling fong Li. 1 Klein Gordon Equation Probablity interpretation Solutions to Klein-Gordon Equation... 2 Note 2 Lng fong L Contents Ken Gordon Equaton. Probabty nterpretaton......................................2 Soutons to Ken-Gordon Equaton............................... 2 2 Drac Equaton 3 2. Probabty nterpretaton.....................................

More information

Physics 5153 Classical Mechanics. D Alembert s Principle and The Lagrangian-1

Physics 5153 Classical Mechanics. D Alembert s Principle and The Lagrangian-1 P. Guterrez Physcs 5153 Classcal Mechancs D Alembert s Prncple and The Lagrangan 1 Introducton The prncple of vrtual work provdes a method of solvng problems of statc equlbrum wthout havng to consder the

More information

Digital Modems. Lecture 2

Digital Modems. Lecture 2 Dgtal Modems Lecture Revew We have shown that both Bayes and eyman/pearson crtera are based on the Lkelhood Rato Test (LRT) Λ ( r ) < > η Λ r s called observaton transformaton or suffcent statstc The crtera

More information

Chapter 6 Hidden Markov Models. Chaochun Wei Spring 2018

Chapter 6 Hidden Markov Models. Chaochun Wei Spring 2018 896 920 987 2006 Chapter 6 Hdden Markov Modes Chaochun We Sprng 208 Contents Readng materas Introducton to Hdden Markov Mode Markov chans Hdden Markov Modes Parameter estmaton for HMMs 2 Readng Rabner,

More information

Lower bounds for the Crossing Number of the Cartesian Product of a Vertex-transitive Graph with a Cycle

Lower bounds for the Crossing Number of the Cartesian Product of a Vertex-transitive Graph with a Cycle Lower bounds for the Crossng Number of the Cartesan Product of a Vertex-transtve Graph wth a Cyce Junho Won MIT-PRIMES December 4, 013 Abstract. The mnmum number of crossngs for a drawngs of a gven graph

More information

Inthem-machine flow shop problem, a set of jobs, each

Inthem-machine flow shop problem, a set of jobs, each THE ASYMPTOTIC OPTIMALITY OF THE SPT RULE FOR THE FLOW SHOP MEAN COMPLETION TIME PROBLEM PHILIP KAMINSKY Industra Engneerng and Operatons Research, Unversty of Caforna, Bereey, Caforna 9470, amnsy@eor.bereey.edu

More information

x = x 1 + :::+ x K and the nput covarance matrces are of the form ± = E[x x y ]. 3.2 Dualty Next, we ntroduce the concept of dualty wth the followng t

x = x 1 + :::+ x K and the nput covarance matrces are of the form ± = E[x x y ]. 3.2 Dualty Next, we ntroduce the concept of dualty wth the followng t Sum Power Iteratve Water-fllng for Mult-Antenna Gaussan Broadcast Channels N. Jndal, S. Jafar, S. Vshwanath and A. Goldsmth Dept. of Electrcal Engg. Stanford Unversty, CA, 94305 emal: njndal,syed,srram,andrea@wsl.stanford.edu

More information

Lecture 4 Hypothesis Testing

Lecture 4 Hypothesis Testing Lecture 4 Hypothess Testng We may wsh to test pror hypotheses about the coeffcents we estmate. We can use the estmates to test whether the data rejects our hypothess. An example mght be that we wsh to

More information