Capacity Theorems for the Finite-State Broadcast Channel with Feedback

Size: px
Start display at page:

Download "Capacity Theorems for the Finite-State Broadcast Channel with Feedback"

Transcription

1 Capacity Theorems for the Fiite-State Broadcast Chael with Feedback Ro abora ad Adrea Goldsmith ept of Electrical Egieerig, Staford Uiversity Abstract We cosider the discrete, time-varyig broadcast chael with memory uder the assumptio that the chael states belog to a set of ite cardiality We study the achievable rates i two scearios where feedback (ad cooperatio is available Oe sceario is the geeral ite-state broadcast chael (FSBC where both receivers sed feedback to the trasmitter, ad i additio oe receiver seds his chael outputs to the other receiver through a cooperatio lik The secod sceario is the degraded FSBC where oly the strog receiver seds feedback to the trasmitter We d the capacity regios for both cases I both scearios we cosider o-idecomposable as well as a class of idecomposable FSBCs I INTROUCTION The ite-state chael (FSC was itroduced as early as 953 to model time-varyig chaels with memory [] I this model memory is captured by the state of the chael at the ed of the previous symbol trasmissio To make this precise, let X deote the chael iput, Y the chael output ad S the chael state The trasitio fuctio of the FSC at symbol time i satises p(y i, s i x i, y i, s i p(y i, s i x i, s i Capacity aalysis of time-varyig chaels with memory has bee the focus of cosiderable iterest, especially i the Gaussia setup This has bee motivated by the proliferatio of mobile commuicatios i which the chael is subject to multipath ad correlated fadig The correlatio of the fadig process itroduces memory ito the time-varyig chael The capacity of o-idecomposable FSCs without feedback was origially studied by Gallager [2] ad has bee applied to specic chaels, see [3] ad refereces therei Recetly, the capacity of FSCs with feedback has bee studied Specically, it was show i [4] that feedback ca icrease the capacity of some FSCs with memory The capacity of the FS-MAC with ad without feedback was also cosidered recetly [5] I this work we focus o the capacity of ite-state broadcast chaels with feedback I this sceario a sigle trasmitter commuicates with two receivers over a itestate chael characterized by p(y, z, s x, s (see Figure for otatios, where s deotes the state of the chael at the ed of the previous symbol trasmissio I a previous work we cosidered the degraded FSBC without feedback [6] Specically, we rst deed the otios of physical ad stochastic degradedess for chaels with memory ad the showed that capacity is achieved with a superpositio codebook with memory The capacity regio was obtaied as a limitig expressio by takig the blocklegth to iity I this work we study the effect of feedback ad cooperatio The authors are with the Wireless Systems Lab, epartmet of Electrical Egieerig, Staford Uiversity, Staford, CA ro,adrea@wslstafordedu This work was supported i part by the ARPA ITMANET program uder grat TFIN o the capacity regio of the FSBC The degraded FSBC with feedback is of special iterest, as it was show i [7] that feedback does ot icrease the capacity regio of the physically degraded memoryless broadcast chael (BC, ad it would be iterestig to cotrast this with the case with memory (see discussio i Sectio III I the cotext of discrete multi-user chaels with states, we ote that the capacity of the degraded arbitrarily varyig BC (AVBC has bee recetly ivestigated i [8] ad [9] This chael is characterized by the trasitio fuctio p(y, z x, s p(y i, z i x i, s i This models a memoryless chael whose parameters vary with time i a arbitrary maer I [8] AVBCs with causal ad o-causal side iformatio at the trasmitter were cosidered, ad i [9] the capacity for AVBCs with causal side iformatio at the trasmitter ad o-causal side iformatio at the good receiver was derived The most geeral sceario for the FSBC with feedback ad cooperatio is depicted i Figure M M 2 Ecoder X i B Broadcast Chael p(z,y,s x,s A 2 Y i- Z i- Y i Z i ecoder (Rx A Z i- ecoder 2 (Rx 2 ^ ^ M ^ ^ M 2 Fig The geeral FSBC with feedback ad cooperatio idicates a sigle symbol-time delay Each of the three switches A, A 2 or B ca be either ope or closed Switch A eables cooperatio from Rx 2 to Rx, switch A 2 eables feedback from Rx 2 to the trasmitter, ad switch B eables feedback from Rx to the trasmitter From Figure we see that there are eight possible coguratios I this work we cosider two of the eight scearios (the sceario with all switches ope was cosidered i [6] We deote them by SC ad SC2: SC: The geeral FSBC with all switches closed Therefore, Rx 2 seds its chael output to both Rx ad the trasmitter This results i a physically degraded chael X s 0 (Y, Z s 0 Z s 0, but it is ot required that X s 0 Y s 0 Z s 0 form a Markov chai The sceario i which the chael output at oe receiver is available to the other receiver is also called the augmeted BC [0] Note that the delay from Rx 2 to Rx is of o

2 sigicace as the receivers ca wait util the ed of the etire block before begiig to decode SC2: The physically degraded BC with oly switch B closed Therefore, Rx 2 does ot sed feedback, ad the codebook is geerated without kowledge of the chael output at Rx 2 For SC2 we shall assume that the degradedess coditio X s 0 Y s 0 Z s 0 holds The sceario SC ca be viewed as represetig a uplik sceario that combies receiver cooperatio ad feedback: the weak (eg far receiver seds its chael output to the strog receiver, ad the feedback from the strog receiver to the trasmitter cotais both its ow feedback ad the feedback of the weak receiver, obtaied through the cooperatio lik The sceario SC2 ca represet a cellular situatio without cooperatio i which the mobile Rx 2 is too far from the base statio to reliably sed feedback to it, but the mobile Rx is close to the base statio ad thus has a reliable feedback path to it I Sectio III we explai why, despite the fact that oly oe receiver seds feedback, the rates to both receivers icrease This is a importat beet of feedback i multi-user scearios II CHANNEL MOEL First, a word about otatio I the followig we deote radom variables with upper case letters, eg X, Y, ad their realizatios with lower case letters x, y A radom variable (RV X takes values i a set X We use X to deote the cardiality of a ite, discrete set X, X to deote the -fold Cartesia product of X, ad p X (x to deote the probability mass fuctio (pmf of a discrete RV X o X For brevity we may omit the subscript X whe it is obvious from the cotext We use p X Y (x y to deote the coditioal pmf of X give Y We deote vectors with boldface letters, eg x, y; the i'th elemet of a vector x is deoted with x i ad we use x j i where i < j to deote the vector (x i, x i+,, x j, x j ; x j is short form otatio for x j, ad x x A vector of radom variables is deoted by X X, ad similarly we dee X j i (X i, X i+,, X j, X j for i < j We use H( to deote the etropy of a discrete radom variable ad I( ; to deote the mutual iformatio betwee two radom variables, as deed i [2, Chapter 2] I( ; q deotes the mutual iformatio evaluated with a pmf q o the radom variable ad co R deotes the covex hull of the set R Fially, we recall the deitios of directed mutual iformatio ad causal coditioig (see also [5]: I(X Y Z I(X i ; Y i Y i, Z I(X Y Z Q(x y, z I(X i ; Y i Y i, Z i p(x i x i, y i, z i eitio : The ite-state broadcast chael is deed by the triplet X S, p(y, z, s x, s, Y Z S where X is the iput symbol, Y ad Z are the output symbols, S is the chael state at the ed of the previous symbol trasmissio ad S is the chael state at the ed of the curret symbol trasmissio S, X, Y ad Z are discrete alphabets of ite cardialities The pmf of a block of trasmissios is p(y, z, s, x s 0 p(y i, z i, s i, x i y i, z i, s i, x i, s 0 (a (b p(x i y i, z i, x i p(y i, z i, s i y i, z i, s i, x i, s 0 p(x i y i, z i, x i p(y i, z i, s i x i, s i, where s 0 is the iitial state Here, (a is because the trasmitter is oblivious of the chael states ad (b captures the fact that give S i, the symbols at time i are idepedet of the past eitio 2: The FSBC is called physically degraded if its pmf satises p(y i x i, y i, z i, s 0 p(y i x i, y i, s 0, (a p(z i x i, y i, z i, s 0 p(z i y i, z i, s 0 (b Coditio (a captures the ituitive otio of degradedess, amely that Z i is a degraded versio of Y i, thus it does ot add iformatio whe Y i is give Note that i the memoryless case this coditio is ot ecessary as, give X i, Y i is idepedet of the history Coditio (b follows from the stadard otio of degradedess, amely that Y i makes Z i idepedet of X i Note that coditio (b does ot elimiate memory Usig coditios (a ad (b i sceario SC2 we obtai (whe p(y, x s 0 > 0 p(z y, x, s 0 p(z, y, x s 0 p(y, x s 0 p(z i, y i, x i z i, y i, x i, s 0 p(y, x s 0 p(x i z i, y i, x i p(z i, y i z i, y i, x i, s 0 p(y, x s 0 (a p(x i y i, x i p(z i, y i z i, y i, x i, s 0 p(x i y i, x i p(y i y i, x i, s 0 (b p(y i y i, x i, s 0 p(z i z i, y i, x i, s 0 p(y i y i, x i, s 0 (c p(z i z i, y i, s 0 where (a is because A 2 is ope i SC2, (b follows from (a ad (c follows from (b We therefore coclude that whe ( holds, p(z y, x, s 0 p(z y, s 0 Hece, p(y, z x, s 0 p(y x, s 0 p(z y, s 0 (2 Note that Z is a degraded versio of Y but still depeds o the state sequece (ie degradedess does ot elimiate the memory A special case of the physically degraded FSBC occurs whe i (b we have p(z i x i, y i, z i, s 0 p(z i y i Hece, p(z y, s 0 p(z y p(z i y i (3 Equatio (3 is similar to the deitio of degradedess for the AVBC used i [8] Coditio (3 does ot costitute oly a

3 mathematical coveiece, but represets a physical sceario, as show i [6] eitio 3: The FSBC is called stochastically degraded if there exists a pmf p(z y such that p(z x, s 0 Y p(z, y x, s 0 Y p(y x, s 0 p(z i y i (4 eitio 4: (see [2, Sectio 46] The FSBC is called idecomposable if for every ɛ > 0 there exists N 0 (ɛ such that for all > N 0 (ɛ, p(s x, s 0 p(s x, s 0 < ɛ, for all s, x, ad iitial states s 0 ad s 0 eitio 5: A (R 0, R, R 2, determiistic code for the FSBC with feedback cosists of three message sets,, 2,, 2 R0, M, 2,, 2 R ad M 2, 2,, 2 R2, ad a collectio of mappigs (f i, g y, g z such that f i : M M 2 Z i Y i X (5 is the ecoder, ad g y : Y M, g z : Z M 2, are the decoders Here, is the set of commo messages ad M ad M 2 are the sets of private messages to Rx ad Rx 2, respectively Note that we assume o kowledge of the states at the trasmitter ad receivers eitio 6: The average probability of error of a code of blocklegth is give by max s0 S P e ( (s 0 where P ( e (s 0 Pr (g y (Y (, M or g z (Z (, M 2 s 0, ad the messages, M M ad M 2 M 2 are selected idepedetly ad uiformly Remark: I eitios 5 ad 6 we assume o receiver cooperatio However, sice i SC Z is also available at Rx, the for this sceario we modify eitios 5 ad 6 to iclude cooperatio by replacig g y (Y with g y (Y, Z eitio 7: A rate triplet (R 0, R, R 2 is called achievable for the FSBC if for every ɛ > 0 ad δ > 0 there exists a (ɛ, δ N such that > (ɛ, δ a (R 0 δ, R δ, R 2 (s 0 ɛ, s 0 S ca be costructed eitio 8: The capacity regio of the FSBC is the covex hull of all achievable rate triplets δ, code with P ( e III MAIN RESULTS AN ISCUSSION The mai results are stated i the followig theorems The proof of Theorem is outlied i Sectio IV The proof of Theorem 2 follows from similar argumets ad is thus omitted Theorem (capacity regio for SC: For the FSBC p(y, z, s x, s, let Q SC be the set of all distributios o ( U i X Y Z S that satisfy q(u, x, y, z, s s 0 : q(u, x, y, z, s s 0 p(u i u i, z i Q SC p(x i x i, z i, y i, u i p(y i, z i, s i x i, s i for all s 0 S, N ee the regio R SC as R SC co (R 0, R, R 2 : R 0 0, R 0, q Q SC R 2 0, R mi s 0 S I(X Z, Y U, s 0 q, R 0 + R 2 mi s 0 S I(U Z s 0 q For the geeral FSBC with feedback such that switches A, A 2 ad B are closed, the capacity regio is give by C SC fb lim RSC, (6 ad the limit exists The deitio of the limit of regios ca be foud, eg, i [5] Here, the auxiliary RV U represets the iformatio trasmitted to Rx 2 Whe feedback ad cooperatio are ot available from Rx 2 ad the chael is physically degraded we obtai the followig capacity regio: Theorem 2 (capacity regio for SC2: Let Q SC2 be the set of all distributios o (U X Y Z S that satisfy Q SC2 q(u, x, y, z, s s 0 : q(u, x, y, z, s s 0 p(u p(x i x i, y i, u p(y i, z i, s i x i, s i for all s 0 S ad U mi X Y, Z +, N, where p(y, z, s x, s is the physically degraded FSBC satisfyig Equatio (2 ee the regio R SC2 as R SC2 co (R 0, R, R 2 : R 0 0, R 0, q Q SC2 R 2 0, R mi s 0 S R 0 + R 2 mi s 0 S I(X Y U, s 0 q, I(U ; Z s 0 q For the geeral FSBC with feedback such that switch B is closed, the capacity regio is give by C SC2 fb lim RSC2, (7 ad the limit exists Sice the capacity of the broadcast chael depeds oly o the coditioal margials p(y x, s 0 ad p(z x, s 0 (see [2, Chapter 46], the capacity regio of the stochastically degraded FSBC for sceario SC2 is the same as the correspodig physically degraded FSBC: Corollary : For the stochastically degraded FSBC of efiitio 3, the capacity regio is give by Theorem 2 with the appropriate p(z y satisfyig (4 The ite-state Markov BC (FSMBC is deed by p(y, z, s x, s p(s s p(y, z x, s Whe the Markov chai is homogeeous, irreducible ad aperiodic the the FSMBC is a idecomposable chael,

4 deoted H-FSMBC For the H-FSMBC we have the followig corollary: Corollary 2: For the H-FSMBC of sceario SC, the capacity regio is give by Theorem where, i the deitio of R SC, the coditioig o s 0 ad s 0 are omitted from the mutual iformatio expressios 2 For the physically degraded H-FSMBC of sceario SC2, the capacity regio is obtaied from Theorem 2 where, i the deitio of R SC2, s 0 ad s 0 are omitted from the mutual iformatio expressios 3 For the stochastically degraded H-FSMBC of sceario SC2, the capacity regio is obtaied from Corollary where, i the deitio of R SC2, s 0 ad s 0 are omitted from the mutual iformatio expressios Proof outlie: Loosely speakig, the corollary is true sice for large eough the effect of the iitial state fades away Therefore, the maximum over all s 0 S equals the miimum Hece, for all iitial states the limits for are the same iscussio We make the followig observatios: We ote that i both scearios we actually have physically degraded situatios I SC the physical degradedess is due to the cooperatio lik (switch A ad the Markov chai X (Y, Z Z I SC2 the chael is physically degraded by deitio of the sceario Therefore, i the derivatio we eed to cosider oly the two private messages case as the commo message ca be icorporated by splittig the rate to Rx 2 ito private ad commo rates, as i [2, Theorem 464] 2 Sice i SC the chael is effectively a physically degraded broadcast chael, a superpositio codetree achieves capacity Iterpretig superpositio codig i terms of cloud ceters (U ad cloud elemets (X U we ote that i SC the cloud ceters are i fact codetrees o which cloud elemets, which are also codetrees, are superimposed 3 It was show i [7] that feedback does ot icrease the capacity of the physically degraded memoryless BC The ituitio is as follows: i the physically degraded memoryless BC, Y is a memoryless trasformatio of X ad Z is a memoryless trasformatio of Y Now, as feedback does ot help the memoryless poit-to-poit chael it caot help the cascade of two such chaels As explaied earlier, whe the chael has memory, feedback ca icrease its capacity Feedback also helps the stochastically degraded BC [0] Therefore, feedback ca icrease the capacity of the FSBC 4 I SC, both receivers decode the cloud ceter based o Z, as Z is available also at Rx through the cooperatio lik Rx ow proceeds to decode the cloud elemet However, sice the chael is ot physically degraded, after decodig the cloud ceter usig Z, Rx uses both (Z, Y rather tha oly Y to decode the cloud elemet This is because p(z x, y, s 0 p(z y, s 0 5 A superpositio codebook structure achieves capacity for both SC ad SC2 This itroduces a structural costrait whe optimizig the codebook for achievig the maximal rate pairs Note, however, that for SC we are ot able to establish a cardiality boud for the auxiliary RV 6 I SC2 feedback is available oly from Rx However, due to the physically degraded structure, this helps both receivers: degradedess costrais the sum-rate to be bouded by the sum-rate at Rx Whe this sum-rate is icreased, the for the same rate R it is possible to obtai a higher rate R 2 The maximum rate to Rx 2, though, remais the same IV PROOF OUTLINE FOR THEOREM (SC A Achievability The achievability proof cosists of the followig steps: Fix a collectio of probability distributios p(ui u i, z i, p(x i u i, y i, z i, x i Q, Q 2 Usig maximum-likelihood decodig at Rx 2 accordig to arg max p(z i z i, u i, s 0 m 2 S s 0 S we coclude that a positive error expoet for decodig M 2 ca be obtaied as log as R 2 mi s0 S I(U Z s 0 log 2 S I2 (Q, Q 2 As Rx receives Z through the cooperatio lik, the also for decodig M 2 at Rx the error expoet is positive Usig maximum-likelihood decodig at Rx accordig to arg max p(y i, z i x i, y i, z i, s 0 m S s 0 S we coclude that a positive error expoet for decodig M at Rx, give that M 2 was correctly decoded, ca be achieved as log as R mi s0 S I(X Y, Z U, s 0 log 2 S I (Q, Q 2 Next, for a xed ad some iteger b let 0 b Costruct distributios by takig the product of the basic distributio for a block of symbols b times: Q 2 (x 0 u 0, y 0, z 0 b ( p b Q (u 0 z 0 b ( p b x (b +i x (b +i (b +, y (b +i (b +, z (b +i (b +, u (b +i (b + u (b +i z (b +i (b +, u (b +i (b + Now, takig b large eough results i a average probability of error that is arbitrarily small, hece (I (Q, Q 2, I 2 (Q, Q 2 is achievable Fially, for λ > 0 dee C fb,sc (λ: Cfb,SC(λ F (λ, Q, Q 2 max Q (u z Q 2 (x u,z,y mi s 0 S F (λ, Q, Q 2, (8 I(U Z s 0 ( + λ +λ mi s 0 S I(X Z, Y U, s 0 log S We show that Cfb,SC (λ is sup-additive: C fb,sc (λ lim Cfb,SC (λ sup Cfb,SC (λ Therefore, the boudary of the achievable regio ca be writte as ( R 2 (R C fb,sc (λ λr (9 if 0 λ

5 B Coverse Theorem 3: If for some λ > 0, R 2 + λr > C fb,sc(λ + ɛ, the there exist iitial states s 0, s 0 S for which P ( e2 (s 0R 2 + λp ( + log S e (s 0R > ɛ ( + λ (0 The implicatio of (0, as explaied i [], is that for large eough the probability of error caot be made arbitrarily small, outside the regio whose boudary is give by (9 Proof: Recall that P ( e2 (s 0 ad P ( e (s 0 deote probabilities of error for iitial state s 0, whe the decoders are igorat of the iitial state From Fao's iequality we have that for iitial state s 0 H(M 2 Z, s 0 P ( e2 (s 0R 2 + (a H(M Z, Y, s 0 P ( e (s 0R + (b eote with s 0, the iitial state that maximizes H(M 2 Z, s 0 ad with s 0, the iitial state that maximizes H(M Z, Y, s 0 Now, ote that H(M2 s 0 H(M 2 Z, s 0 mi I(M 2; Z s 0 mi s 0 S s 0 S R 2 max H(M 2 Z, s 0, (2 s 0 S mi I(M ; Z, Y M 2, s 0 s 0 S We ext have I(M 2 ; Z s 0 R max s 0 S H(M Y, Z, M 2, s 0 R max s 0 S H(M Y, Z, s 0 (3 ( H(Zi Z i, s 0 H(Z i Z i, M 2, s 0 ( H(Zi Z i, s 0 H(Z i Z i, U i, s 0 I(U Z s 0 where U i (M 2, Z i, i, 2,, Note that U i (U i, Z i Y i ad also U i Z i, Y i, s 0 X i Z i, Y i, s 0 Y i, Z i Z i, Y i, s 0 We also have that I(M ; Y, Z M 2, s 0 ( H(Zi, Y i Z i, Y i, M 2, s 0 H(Z i, Y i Z i, Y i, M, M 2, s 0 ( H(Zi, Y i Z i, Y i, U i, s 0 H(Z i, Y i Z i, Y i, X i, U i, s 0 I(X Z, Y U, s 0 Combiig the above we have that for our choice of U : mi I(M 2; Z s 0 + λ mi I(M ; Z, Y M 2, s s 0 0 S s 0 S mi s 0 S I(U Z s 0 + λ mi s 0 S I(X Z, Y U, s 0 C fb,sc(λ + ( + λ log S C fb,sc(λ + ( + λ log S, (4 sice Cfb,SC (λ is obtaied by maximizig over all joit distributios Q (u z Q 2 (x u, z, y ad also because Cfb,SC (λ is sup-additive Pluggig (2 ad (3 ito (4 yields R 2 H(M 2 Z, s 0, + λ(r H(M Z, Y, s 0, C fb,sc(λ + ( + λ log S H(M 2 Z, s 0, + λh(m Z, Y, s 0, + ( + λ log S ( R 2 + λr C fb,sc(λ > ɛ Combied with Fao's iequalities (, we obtai (0, which meas that at least oe of the states s 0,, s 0, results i a probability of error (at the respective receiver that is bouded away from zero, completig the proof of the coverse V CONCLUSIONS We have derived the capacity regio of a two-user FSBC with feedback uder two differet assumptios about the ature of the feedback ad user cooperatio A importat property of the rst sceario is that the chael output at oe receiver is also available to the other receiver through cooperatio Whe this cooperatio is ot possible, the the rst user's receiver caot use iformatio about received sigal at the secod receiver to decode its ow message This implies that a superpositio codebook is ot ecessarily optimal, eve if the chael is physically degraded I our secod model, as decoder 2's chael output is ot available at the trasmitter ad the chael is physically degraded, a superpositio codebook i which the cloud ceters are geerated without feedback is optimal A importat property of feedback i multi-user scearios is that feedback from oe user ca help other users as well REFERENCES [] B McMilla The Basic Theorems of Iformatio Theory The Aals of Mathematical Statistics, Vol-24(2:96 29, 953 [2] R G Gallager Iformatio Theory ad Reliable Commuicatio Joh Wiley ad Sos Ic, 968 [3] T Holliday, A Goldsmith, ad P Gly O Etropy ad Lyapuov Expoets for Fiite State Chaels IEEE Tr o IT, IT-52(8, 2006 [4] H Permuter, P Cuff, B Va Roy, ad T Weissma Capacity of the Trapdoor Chael with Feedback Submitted to IEEE Tras o IT [5] H Permuter ad T Weissma Capacity Regio of the Fiite-State Multiple Access Chael with ad without Feedback Submitted to the IEEE Tras Iform Theory, 2007 [6] R abora ad A Goldsmith The Capacity of the egraded Fiite- State Broadcast Chael Accepted to the Iformatio Theory Workshop (ITW, 2008 [7] A El-Gamal The Feedback Capacity of egraded Broadcast Chaels IEEE Tras Iform Theory, IT-24(3:379 38, 978 [8] Y Steiberg Codig for the egraded Broadcast Chael With Radom Parameters, With Causal ad Nocausal Side Iformatio IEEE Tras Iform Theory, IT-5(8: , 2005 [9] A Wishtok ad Y Steiberg The Arbitrarily Varyig egraded Broadcast Chael with States Kow at the Ecoder Iteratioal Symp o Iform Theory (ISIT 2006, Seattle, WA, pp [0] L H Ozarow Codig ad Capacity for Additive White Gaussia Noise Multi-User Chaels with Feedback Ph dissertatio, Mass Ist Tech, Cambridge, MA, May 979 [] R G Gallager Capacity ad Codig for egraded Broadcast Chaels Problemy Peredachi Iformatsii, vol 0(3:3 4, 974 [2] T M Cover ad J Thomas Elemets of Iformatio Theory Joh Wiley ad Sos Ic, 99

The Capacity Region of the Degraded Finite-State Broadcast Channel

The Capacity Region of the Degraded Finite-State Broadcast Channel The Capacity Regio of the Degraded Fiite-State Broadcast Chael Ro Dabora ad Adrea Goldsmith Dept. of Electrical Egieerig, Staford Uiversity, Staford, CA Abstract We cosider the discrete, time-varyig broadcast

More information

The Capacity Region of the. Degraded Finite-State Broadcast Channel

The Capacity Region of the. Degraded Finite-State Broadcast Channel Submitted to the IEEE Trasactios o Iformatio Theory, 2008. The Capacity Regio of the Degraded Fiite-State Broadcast Chael Ro Dabora ad Adrea Goldsmith Wireless Systems Lab Departmet of Electrical Egieerig

More information

Lecture 11: Channel Coding Theorem: Converse Part

Lecture 11: Channel Coding Theorem: Converse Part EE376A/STATS376A Iformatio Theory Lecture - 02/3/208 Lecture : Chael Codig Theorem: Coverse Part Lecturer: Tsachy Weissma Scribe: Erdem Bıyık I this lecture, we will cotiue our discussio o chael codig

More information

Information Theory Tutorial Communication over Channels with memory. Chi Zhang Department of Electrical Engineering University of Notre Dame

Information Theory Tutorial Communication over Channels with memory. Chi Zhang Department of Electrical Engineering University of Notre Dame Iformatio Theory Tutorial Commuicatio over Chaels with memory Chi Zhag Departmet of Electrical Egieerig Uiversity of Notre Dame Abstract A geeral capacity formula C = sup I(; Y ), which is correct for

More information

Lecture 27. Capacity of additive Gaussian noise channel and the sphere packing bound

Lecture 27. Capacity of additive Gaussian noise channel and the sphere packing bound Lecture 7 Ageda for the lecture Gaussia chael with average power costraits Capacity of additive Gaussia oise chael ad the sphere packig boud 7. Additive Gaussia oise chael Up to this poit, we have bee

More information

Asymptotic Coupling and Its Applications in Information Theory

Asymptotic Coupling and Its Applications in Information Theory Asymptotic Couplig ad Its Applicatios i Iformatio Theory Vicet Y. F. Ta Joit Work with Lei Yu Departmet of Electrical ad Computer Egieerig, Departmet of Mathematics, Natioal Uiversity of Sigapore IMS-APRM

More information

Symmetric Two-User Gaussian Interference Channel with Common Messages

Symmetric Two-User Gaussian Interference Channel with Common Messages Symmetric Two-User Gaussia Iterferece Chael with Commo Messages Qua Geg CSL ad Dept. of ECE UIUC, IL 680 Email: geg5@illiois.edu Tie Liu Dept. of Electrical ad Computer Egieerig Texas A&M Uiversity, TX

More information

SUCCESSIVE INTERFERENCE CANCELLATION DECODING FOR THE K -USER CYCLIC INTERFERENCE CHANNEL

SUCCESSIVE INTERFERENCE CANCELLATION DECODING FOR THE K -USER CYCLIC INTERFERENCE CHANNEL Joural of Theoretical ad Applied Iformatio Techology 31 st December 212 Vol 46 No2 25-212 JATIT & LLS All rights reserved ISSN: 1992-8645 wwwatitorg E-ISSN: 1817-3195 SCCESSIVE INTERFERENCE CANCELLATION

More information

Overview of Gaussian MIMO (Vector) BC

Overview of Gaussian MIMO (Vector) BC Overview of Gaussia MIMO (Vector) BC Gwamo Ku Adaptive Sigal Processig ad Iformatio Theory Research Group Nov. 30, 2012 Outlie / Capacity Regio of Gaussia MIMO BC System Structure Kow Capacity Regios -

More information

A Partial Decode-Forward Scheme For A Network with N relays

A Partial Decode-Forward Scheme For A Network with N relays A Partial Decode-Forward Scheme For A etwork with relays Yao Tag ECE Departmet, McGill Uiversity Motreal, QC, Caada Email: yaotag2@mailmcgillca Mai Vu ECE Departmet, Tufts Uiversity Medford, MA, USA Email:

More information

A New Achievability Scheme for the Relay Channel

A New Achievability Scheme for the Relay Channel A New Achievability Scheme for the Relay Chael Wei Kag Seur Ulukus Departmet of Electrical ad Computer Egieerig Uiversity of Marylad, College Park, MD 20742 wkag@umd.edu ulukus@umd.edu October 4, 2007

More information

Lecture 15: Strong, Conditional, & Joint Typicality

Lecture 15: Strong, Conditional, & Joint Typicality EE376A/STATS376A Iformatio Theory Lecture 15-02/27/2018 Lecture 15: Strog, Coditioal, & Joit Typicality Lecturer: Tsachy Weissma Scribe: Nimit Sohoi, William McCloskey, Halwest Mohammad I this lecture,

More information

Cooperative Communication Fundamentals & Coding Techniques

Cooperative Communication Fundamentals & Coding Techniques 3 th ICACT Tutorial Cooperative commuicatio fudametals & codig techiques Cooperative Commuicatio Fudametals & Codig Techiques 0..4 Electroics ad Telecommuicatio Research Istitute Kiug Jug 3 th ICACT Tutorial

More information

Lecture 7: October 18, 2017

Lecture 7: October 18, 2017 Iformatio ad Codig Theory Autum 207 Lecturer: Madhur Tulsiai Lecture 7: October 8, 207 Biary hypothesis testig I this lecture, we apply the tools developed i the past few lectures to uderstad the problem

More information

Entropy and Ergodic Theory Lecture 5: Joint typicality and conditional AEP

Entropy and Ergodic Theory Lecture 5: Joint typicality and conditional AEP Etropy ad Ergodic Theory Lecture 5: Joit typicality ad coditioal AEP 1 Notatio: from RVs back to distributios Let (Ω, F, P) be a probability space, ad let X ad Y be A- ad B-valued discrete RVs, respectively.

More information

Are Slepian-Wolf Rates Necessary for Distributed Parameter Estimation?

Are Slepian-Wolf Rates Necessary for Distributed Parameter Estimation? Are Slepia-Wolf Rates Necessary for Distributed Parameter Estimatio? Mostafa El Gamal ad Lifeg Lai Departmet of Electrical ad Computer Egieerig Worcester Polytechic Istitute {melgamal, llai}@wpi.edu arxiv:1508.02765v2

More information

Lecture 7: Channel coding theorem for discrete-time continuous memoryless channel

Lecture 7: Channel coding theorem for discrete-time continuous memoryless channel Lecture 7: Chael codig theorem for discrete-time cotiuous memoryless chael Lectured by Dr. Saif K. Mohammed Scribed by Mirsad Čirkić Iformatio Theory for Wireless Commuicatio ITWC Sprig 202 Let us first

More information

lim za n n = z lim a n n.

lim za n n = z lim a n n. Lecture 6 Sequeces ad Series Defiitio 1 By a sequece i a set A, we mea a mappig f : N A. It is customary to deote a sequece f by {s } where, s := f(). A sequece {z } of (complex) umbers is said to be coverget

More information

EECS564 Estimation, Filtering, and Detection Hwk 2 Solns. Winter p θ (z) = (2θz + 1 θ), 0 z 1

EECS564 Estimation, Filtering, and Detection Hwk 2 Solns. Winter p θ (z) = (2θz + 1 θ), 0 z 1 EECS564 Estimatio, Filterig, ad Detectio Hwk 2 Sols. Witer 25 4. Let Z be a sigle observatio havig desity fuctio where. p (z) = (2z + ), z (a) Assumig that is a oradom parameter, fid ad plot the maximum

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 19 11/17/2008 LAWS OF LARGE NUMBERS II THE STRONG LAW OF LARGE NUMBERS

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 19 11/17/2008 LAWS OF LARGE NUMBERS II THE STRONG LAW OF LARGE NUMBERS MASSACHUSTTS INSTITUT OF TCHNOLOGY 6.436J/5.085J Fall 2008 Lecture 9 /7/2008 LAWS OF LARG NUMBRS II Cotets. The strog law of large umbers 2. The Cheroff boud TH STRONG LAW OF LARG NUMBRS While the weak

More information

Chapter 3. Strong convergence. 3.1 Definition of almost sure convergence

Chapter 3. Strong convergence. 3.1 Definition of almost sure convergence Chapter 3 Strog covergece As poited out i the Chapter 2, there are multiple ways to defie the otio of covergece of a sequece of radom variables. That chapter defied covergece i probability, covergece i

More information

Lecture 14: Graph Entropy

Lecture 14: Graph Entropy 15-859: Iformatio Theory ad Applicatios i TCS Sprig 2013 Lecture 14: Graph Etropy March 19, 2013 Lecturer: Mahdi Cheraghchi Scribe: Euiwoog Lee 1 Recap Bergma s boud o the permaet Shearer s Lemma Number

More information

Multiterminal source coding with complementary delivery

Multiterminal source coding with complementary delivery Iteratioal Symposium o Iformatio Theory ad its Applicatios, ISITA2006 Seoul, Korea, October 29 November 1, 2006 Multitermial source codig with complemetary delivery Akisato Kimura ad Tomohiko Uyematsu

More information

Hybrid Coding for Gaussian Broadcast Channels with Gaussian Sources

Hybrid Coding for Gaussian Broadcast Channels with Gaussian Sources Hybrid Codig for Gaussia Broadcast Chaels with Gaussia Sources Rajiv Soudararaja Departmet of Electrical & Computer Egieerig Uiversity of Texas at Austi Austi, TX 7871, USA Email: rajivs@mailutexasedu

More information

Convergence of random variables. (telegram style notes) P.J.C. Spreij

Convergence of random variables. (telegram style notes) P.J.C. Spreij Covergece of radom variables (telegram style otes).j.c. Spreij this versio: September 6, 2005 Itroductio As we kow, radom variables are by defiitio measurable fuctios o some uderlyig measurable space

More information

The Fading Number of Multiple-Input Multiple-Output Fading Channels with Memory

The Fading Number of Multiple-Input Multiple-Output Fading Channels with Memory The Fadig Number of Multiple-Iput Multiple-Output Fadig Chaels with Memory Stefa M. Moser Departmet of Commuicatio Egieerig Natioal Chiao Tug Uiversity NCTU Hsichu, Taiwa Email: stefa.moser@ieee.org Abstract

More information

Journal of Multivariate Analysis. Superefficient estimation of the marginals by exploiting knowledge on the copula

Journal of Multivariate Analysis. Superefficient estimation of the marginals by exploiting knowledge on the copula Joural of Multivariate Aalysis 102 (2011) 1315 1319 Cotets lists available at ScieceDirect Joural of Multivariate Aalysis joural homepage: www.elsevier.com/locate/jmva Superefficiet estimatio of the margials

More information

Entropies & Information Theory

Entropies & Information Theory Etropies & Iformatio Theory LECTURE I Nilajaa Datta Uiversity of Cambridge,U.K. For more details: see lecture otes (Lecture 1- Lecture 5) o http://www.qi.damtp.cam.ac.uk/ode/223 Quatum Iformatio Theory

More information

The Maximum-Likelihood Decoding Performance of Error-Correcting Codes

The Maximum-Likelihood Decoding Performance of Error-Correcting Codes The Maximum-Lielihood Decodig Performace of Error-Correctig Codes Hery D. Pfister ECE Departmet Texas A&M Uiversity August 27th, 2007 (rev. 0) November 2st, 203 (rev. ) Performace of Codes. Notatio X,

More information

On Random Line Segments in the Unit Square

On Random Line Segments in the Unit Square O Radom Lie Segmets i the Uit Square Thomas A. Courtade Departmet of Electrical Egieerig Uiversity of Califoria Los Ageles, Califoria 90095 Email: tacourta@ee.ucla.edu I. INTRODUCTION Let Q = [0, 1] [0,

More information

IET Commun., 2009, Vol. 3, Iss. 1, pp doi: /iet-com: & The Institution of Engineering and Technology 2009

IET Commun., 2009, Vol. 3, Iss. 1, pp doi: /iet-com: & The Institution of Engineering and Technology 2009 Published i IET Commuicatios Received o 4th Jue 28 Revised o 3th July 28 doi: 1.149/iet-com:28373 ISSN 1751-8628 Symmetric relayig based o partial decodig ad the capacity of a class of relay etworks L.

More information

Lecture 19: Convergence

Lecture 19: Convergence Lecture 19: Covergece Asymptotic approach I statistical aalysis or iferece, a key to the success of fidig a good procedure is beig able to fid some momets ad/or distributios of various statistics. I may

More information

Increasing timing capacity using packet coloring

Increasing timing capacity using packet coloring 003 Coferece o Iformatio Scieces ad Systems, The Johs Hopkis Uiversity, March 4, 003 Icreasig timig capacity usig packet colorig Xi Liu ad R Srikat[] Coordiated Sciece Laboratory Uiversity of Illiois e-mail:

More information

Generalized Semi- Markov Processes (GSMP)

Generalized Semi- Markov Processes (GSMP) Geeralized Semi- Markov Processes (GSMP) Summary Some Defiitios Markov ad Semi-Markov Processes The Poisso Process Properties of the Poisso Process Iterarrival times Memoryless property ad the residual

More information

Binary Fading Interference Channel with No CSIT

Binary Fading Interference Channel with No CSIT Biary Fadig Iterferece Chael with No CSIT Alireza Vahid, Mohammad Ali Maddah-Ali, A. Salma Avestimehr, ad Ya Zhu arxiv:405.003v3 [cs.it] 4 Mar 07 Abstract We study the capacity regio of the two-user Biary

More information

arxiv: v1 [cs.it] 13 Jul 2012

arxiv: v1 [cs.it] 13 Jul 2012 O the Sum Capacity of the Discrete Memoryless Iterferece Chael with Oe-Sided Weak Iterferece ad Mixed Iterferece Fagfag Zhu ad Biao Che Syracuse Uiversity Departmet of EECS Syracuse, NY 3244 Email: fazhu{biche}@syr.edu

More information

Entropy Rates and Asymptotic Equipartition

Entropy Rates and Asymptotic Equipartition Chapter 29 Etropy Rates ad Asymptotic Equipartitio Sectio 29. itroduces the etropy rate the asymptotic etropy per time-step of a stochastic process ad shows that it is well-defied; ad similarly for iformatio,

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 3 9/11/2013. Large deviations Theory. Cramér s Theorem

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 3 9/11/2013. Large deviations Theory. Cramér s Theorem MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/5.070J Fall 203 Lecture 3 9//203 Large deviatios Theory. Cramér s Theorem Cotet.. Cramér s Theorem. 2. Rate fuctio ad properties. 3. Chage of measure techique.

More information

Application to Random Graphs

Application to Random Graphs A Applicatio to Radom Graphs Brachig processes have a umber of iterestig ad importat applicatios. We shall cosider oe of the most famous of them, the Erdős-Réyi radom graph theory. 1 Defiitio A.1. Let

More information

Resilient Source Coding

Resilient Source Coding Resiliet Source Codig Maël Le Treust Laboratoire des Sigaux et Systèmes CNRS - Supélec - Uiv. Paris Sud 11 91191, Gif-sur-Yvette, Frace Email: mael.letreust@lss.supelec.fr Samso Lasaulce Laboratoire des

More information

Advanced Stochastic Processes.

Advanced Stochastic Processes. Advaced Stochastic Processes. David Gamarik LECTURE 2 Radom variables ad measurable fuctios. Strog Law of Large Numbers (SLLN). Scary stuff cotiued... Outlie of Lecture Radom variables ad measurable fuctios.

More information

Problem Set 4 Due Oct, 12

Problem Set 4 Due Oct, 12 EE226: Radom Processes i Systems Lecturer: Jea C. Walrad Problem Set 4 Due Oct, 12 Fall 06 GSI: Assae Gueye This problem set essetially reviews detectio theory ad hypothesis testig ad some basic otios

More information

Product measures, Tonelli s and Fubini s theorems For use in MAT3400/4400, autumn 2014 Nadia S. Larsen. Version of 13 October 2014.

Product measures, Tonelli s and Fubini s theorems For use in MAT3400/4400, autumn 2014 Nadia S. Larsen. Version of 13 October 2014. Product measures, Toelli s ad Fubii s theorems For use i MAT3400/4400, autum 2014 Nadia S. Larse Versio of 13 October 2014. 1. Costructio of the product measure The purpose of these otes is to preset the

More information

Capacity of Compound State-Dependent Channels with States Known at the Transmitter

Capacity of Compound State-Dependent Channels with States Known at the Transmitter Capacity of Compoud State-Depedet Chaels with States Kow at the Trasmitter Pablo Piataida Departmet of Telecommuicatios SUPELEC Plateau de Moulo, 992 Gif-sur-Yvette, Frace Email: pablo.piataida@supelec.fr

More information

Beurling Integers: Part 2

Beurling Integers: Part 2 Beurlig Itegers: Part 2 Isomorphisms Devi Platt July 11, 2015 1 Prime Factorizatio Sequeces I the last article we itroduced the Beurlig geeralized itegers, which ca be represeted as a sequece of real umbers

More information

The Three-Terminal Interactive. Lossy Source Coding Problem

The Three-Terminal Interactive. Lossy Source Coding Problem The Three-Termial Iteractive 1 Lossy Source Codig Problem Leoardo Rey Vega, Pablo Piataida ad Alfred O. Hero III arxiv:1502.01359v3 cs.it] 18 Ja 2016 Abstract The three-ode multitermial lossy source codig

More information

UC Berkeley CS 170: Efficient Algorithms and Intractable Problems Handout 17 Lecturer: David Wagner April 3, Notes 17 for CS 170

UC Berkeley CS 170: Efficient Algorithms and Intractable Problems Handout 17 Lecturer: David Wagner April 3, Notes 17 for CS 170 UC Berkeley CS 170: Efficiet Algorithms ad Itractable Problems Hadout 17 Lecturer: David Wager April 3, 2003 Notes 17 for CS 170 1 The Lempel-Ziv algorithm There is a sese i which the Huffma codig was

More information

THE interference channel problem describes a setup where multiple pairs of transmitters and receivers share a communication

THE interference channel problem describes a setup where multiple pairs of transmitters and receivers share a communication Trade-off betwee Commuicatio ad Cooperatio i the Iterferece Chael Farhad Shirai EECS Departmet Uiversity of Michiga A Arbor,USA Email: fshirai@umich.edu S. Sadeep Pradha EECS Departmet Uiversity of Michiga

More information

Chapter 6 Infinite Series

Chapter 6 Infinite Series Chapter 6 Ifiite Series I the previous chapter we cosidered itegrals which were improper i the sese that the iterval of itegratio was ubouded. I this chapter we are goig to discuss a topic which is somewhat

More information

On the Capacity of Symmetric Gaussian Interference Channels with Feedback

On the Capacity of Symmetric Gaussian Interference Channels with Feedback O the Capacity of Symmetric Gaussia Iterferece Chaels with Feedback La V Truog Iformatio Techology Specializatio Departmet ITS FPT Uiversity, Haoi, Vietam E-mail: latv@fpteduv Hirosuke Yamamoto Dept of

More information

Finite Block-Length Gains in Distributed Source Coding

Finite Block-Length Gains in Distributed Source Coding Decoder Fiite Block-Legth Gais i Distributed Source Codig Farhad Shirai EECS Departmet Uiversity of Michiga A Arbor,USA Email: fshirai@umichedu S Sadeep Pradha EECS Departmet Uiversity of Michiga A Arbor,USA

More information

CEE 522 Autumn Uncertainty Concepts for Geotechnical Engineering

CEE 522 Autumn Uncertainty Concepts for Geotechnical Engineering CEE 5 Autum 005 Ucertaity Cocepts for Geotechical Egieerig Basic Termiology Set A set is a collectio of (mutually exclusive) objects or evets. The sample space is the (collectively exhaustive) collectio

More information

Properties and Hypothesis Testing

Properties and Hypothesis Testing Chapter 3 Properties ad Hypothesis Testig 3.1 Types of data The regressio techiques developed i previous chapters ca be applied to three differet kids of data. 1. Cross-sectioal data. 2. Time series data.

More information

Lecture 10: Universal coding and prediction

Lecture 10: Universal coding and prediction 0-704: Iformatio Processig ad Learig Sprig 0 Lecture 0: Uiversal codig ad predictio Lecturer: Aarti Sigh Scribes: Georg M. Goerg Disclaimer: These otes have ot bee subjected to the usual scrutiy reserved

More information

Non-Asymptotic Achievable Rates for Gaussian Energy-Harvesting Channels: Best-Effort and Save-and-Transmit

Non-Asymptotic Achievable Rates for Gaussian Energy-Harvesting Channels: Best-Effort and Save-and-Transmit No-Asymptotic Achievable Rates for Gaussia Eergy-Harvestig Chaels: Best-Effort ad Save-ad-Trasmit Silas L. Fog, Jig Yag, ad Ayli Yeer arxiv:805.089v [cs.it] 30 May 08 Abstract A additive white Gaussia

More information

The multiplicative structure of finite field and a construction of LRC

The multiplicative structure of finite field and a construction of LRC IERG6120 Codig for Distributed Storage Systems Lecture 8-06/10/2016 The multiplicative structure of fiite field ad a costructio of LRC Lecturer: Keeth Shum Scribe: Zhouyi Hu Notatios: We use the otatio

More information

5958 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 56, NO. 12, DECEMBER 2010

5958 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 56, NO. 12, DECEMBER 2010 5958 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 56, NO. 12, DECEMBER 2010 Capacity Theorems for Discrete, Finite-State Broadcast Channels With Feedback and Unidirectional Receiver Cooperation Ron Dabora

More information

1 Review and Overview

1 Review and Overview DRAFT a fial versio will be posted shortly CS229T/STATS231: Statistical Learig Theory Lecturer: Tegyu Ma Lecture #3 Scribe: Migda Qiao October 1, 2013 1 Review ad Overview I the first half of this course,

More information

(A sequence also can be thought of as the list of function values attained for a function f :ℵ X, where f (n) = x n for n 1.) x 1 x N +k x N +4 x 3

(A sequence also can be thought of as the list of function values attained for a function f :ℵ X, where f (n) = x n for n 1.) x 1 x N +k x N +4 x 3 MATH 337 Sequeces Dr. Neal, WKU Let X be a metric space with distace fuctio d. We shall defie the geeral cocept of sequece ad limit i a metric space, the apply the results i particular to some special

More information

Inequalities for Entropies of Sets of Subsets of Random Variables

Inequalities for Entropies of Sets of Subsets of Random Variables Iequalities for Etropies of Sets of Subsets of Radom Variables Chao Tia AT&T Labs-Research Florham Par, NJ 0792, USA. tia@research.att.com Abstract Ha s iequality o the etropy rates of subsets of radom

More information

Information Theory and Statistics Lecture 4: Lempel-Ziv code

Information Theory and Statistics Lecture 4: Lempel-Ziv code Iformatio Theory ad Statistics Lecture 4: Lempel-Ziv code Łukasz Dębowski ldebowsk@ipipa.waw.pl Ph. D. Programme 203/204 Etropy rate is the limitig compressio rate Theorem For a statioary process (X i)

More information

62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 +

62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + 62. Power series Defiitio 16. (Power series) Give a sequece {c }, the series c x = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + is called a power series i the variable x. The umbers c are called the coefficiets of

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 21 11/27/2013

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 21 11/27/2013 MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 21 11/27/2013 Fuctioal Law of Large Numbers. Costructio of the Wieer Measure Cotet. 1. Additioal techical results o weak covergece

More information

An Introduction to Randomized Algorithms

An Introduction to Randomized Algorithms A Itroductio to Radomized Algorithms The focus of this lecture is to study a radomized algorithm for quick sort, aalyze it usig probabilistic recurrece relatios, ad also provide more geeral tools for aalysis

More information

Lecture 3 : Random variables and their distributions

Lecture 3 : Random variables and their distributions Lecture 3 : Radom variables ad their distributios 3.1 Radom variables Let (Ω, F) ad (S, S) be two measurable spaces. A map X : Ω S is measurable or a radom variable (deoted r.v.) if X 1 (A) {ω : X(ω) A}

More information

On Multipath Fading Channels at High SNR

On Multipath Fading Channels at High SNR O Multipath Fadig Chaels at High SNR Tobias Koch Amos Lapidoth ETH Zurich, Switzerlad Email: toch, lapidoth}@isi.ee.ethz.ch ISIT 008, Toroto, Caada, July 6 -, 008 Abstract This paper studies the capacity

More information

A Note on Matrix Rigidity

A Note on Matrix Rigidity A Note o Matrix Rigidity Joel Friedma Departmet of Computer Sciece Priceto Uiversity Priceto, NJ 08544 Jue 25, 1990 Revised October 25, 1991 Abstract I this paper we give a explicit costructio of matrices

More information

ACO Comprehensive Exam 9 October 2007 Student code A. 1. Graph Theory

ACO Comprehensive Exam 9 October 2007 Student code A. 1. Graph Theory 1. Graph Theory Prove that there exist o simple plaar triagulatio T ad two distict adjacet vertices x, y V (T ) such that x ad y are the oly vertices of T of odd degree. Do ot use the Four-Color Theorem.

More information

Optimally Sparse SVMs

Optimally Sparse SVMs A. Proof of Lemma 3. We here prove a lower boud o the umber of support vectors to achieve geeralizatio bouds of the form which we cosider. Importatly, this result holds ot oly for liear classifiers, but

More information

Chapter 4. Fourier Series

Chapter 4. Fourier Series Chapter 4. Fourier Series At this poit we are ready to ow cosider the caoical equatios. Cosider, for eample the heat equatio u t = u, < (4.) subject to u(, ) = si, u(, t) = u(, t) =. (4.) Here,

More information

On Routing-Optimal Network for Multiple Unicasts

On Routing-Optimal Network for Multiple Unicasts O Routig-Optimal Network for Multiple Uicasts Chu Meg, Athia Markopoulou Abstract I this paper, we cosider etworks with multiple uicast sessios. Geerally, o-liear etwork codig is eeded to achieve the whole

More information

Information-based Feature Selection

Information-based Feature Selection Iformatio-based Feature Selectio Farza Faria, Abbas Kazeroui, Afshi Babveyh Email: {faria,abbask,afshib}@staford.edu 1 Itroductio Feature selectio is a topic of great iterest i applicatios dealig with

More information

Inseparability of the Multiple Access Wiretap Channel

Inseparability of the Multiple Access Wiretap Channel Iseparability of the Multiple Access Wiretap Chael Jiawei Xie Seur Ulukus Departmet of Electrical ad Computer Egieerig Uiversity of Marylad, College Park, MD 074 xiejw@umd.edu ulukus@umd.edu Abstract We

More information

Random Models. Tusheng Zhang. February 14, 2013

Random Models. Tusheng Zhang. February 14, 2013 Radom Models Tusheg Zhag February 14, 013 1 Radom Walks Let me describe the model. Radom walks are used to describe the motio of a movig particle (object). Suppose that a particle (object) moves alog the

More information

Homework Set #3 - Solutions

Homework Set #3 - Solutions EE 15 - Applicatios of Covex Optimizatio i Sigal Processig ad Commuicatios Dr. Adre Tkaceko JPL Third Term 11-1 Homework Set #3 - Solutios 1. a) Note that x is closer to x tha to x l i the Euclidea orm

More information

CS284A: Representations and Algorithms in Molecular Biology

CS284A: Representations and Algorithms in Molecular Biology CS284A: Represetatios ad Algorithms i Molecular Biology Scribe Notes o Lectures 3 & 4: Motif Discovery via Eumeratio & Motif Represetatio Usig Positio Weight Matrix Joshua Gervi Based o presetatios by

More information

On Evaluating the Rate-Distortion Function of Sources with Feed-Forward and the Capacity of Channels with Feedback.

On Evaluating the Rate-Distortion Function of Sources with Feed-Forward and the Capacity of Channels with Feedback. O Evaluatig the Rate-Distortio Fuctio of Sources with Feed-Forward ad the Capacity of Chaels with Feedback. Ramji Vekataramaa ad S. Sadeep Pradha Departmet of EECS, Uiversity of Michiga, A Arbor, MI 4805

More information

Multiterminal Source Coding with an Entropy-Based Distortion Measure

Multiterminal Source Coding with an Entropy-Based Distortion Measure 20 IEEE Iteratioal Symposium o Iformatio Theory Proceedigs Multitermial Source Codig with a Etropy-Based Distortio Measure Thomas A. Courtade ad Richard D. Wesel Departmet of Electrical Egieerig Uiversity

More information

First Year Quantitative Comp Exam Spring, Part I - 203A. f X (x) = 0 otherwise

First Year Quantitative Comp Exam Spring, Part I - 203A. f X (x) = 0 otherwise First Year Quatitative Comp Exam Sprig, 2012 Istructio: There are three parts. Aswer every questio i every part. Questio I-1 Part I - 203A A radom variable X is distributed with the margial desity: >

More information

On Successive Refinement for the Wyner-Ziv Problem with Partially Cooperating Decoders

On Successive Refinement for the Wyner-Ziv Problem with Partially Cooperating Decoders ISIT 2008, Toroto, Caada, July 6-11, 2008 O Successive Refiemet for the Wyer-Ziv Problem with Partially Cooperatig Decoders Shraga I. Bross ad Tsachy Weissma, School of Egieerig, Bar-Ila Uiversity, Ramat-Ga

More information

Sequences. Notation. Convergence of a Sequence

Sequences. Notation. Convergence of a Sequence Sequeces A sequece is essetially just a list. Defiitio (Sequece of Real Numbers). A sequece of real umbers is a fuctio Z (, ) R for some real umber. Do t let the descriptio of the domai cofuse you; it

More information

The Poisson Process *

The Poisson Process * OpeStax-CNX module: m11255 1 The Poisso Process * Do Johso This work is produced by OpeStax-CNX ad licesed uder the Creative Commos Attributio Licese 1.0 Some sigals have o waveform. Cosider the measuremet

More information

BIRKHOFF ERGODIC THEOREM

BIRKHOFF ERGODIC THEOREM BIRKHOFF ERGODIC THEOREM Abstract. We will give a proof of the poitwise ergodic theorem, which was first proved by Birkhoff. May improvemets have bee made sice Birkhoff s orgial proof. The versio we give

More information

Shannon s noiseless coding theorem

Shannon s noiseless coding theorem 18.310 lecture otes May 4, 2015 Shao s oiseless codig theorem Lecturer: Michel Goemas I these otes we discuss Shao s oiseless codig theorem, which is oe of the foudig results of the field of iformatio

More information

Lecture 6: Source coding, Typicality, and Noisy channels and capacity

Lecture 6: Source coding, Typicality, and Noisy channels and capacity 15-859: Iformatio Theory ad Applicatios i TCS CMU: Sprig 2013 Lecture 6: Source codig, Typicality, ad Noisy chaels ad capacity Jauary 31, 2013 Lecturer: Mahdi Cheraghchi Scribe: Togbo Huag 1 Recap Uiversal

More information

Distribution of Random Samples & Limit theorems

Distribution of Random Samples & Limit theorems STAT/MATH 395 A - PROBABILITY II UW Witer Quarter 2017 Néhémy Lim Distributio of Radom Samples & Limit theorems 1 Distributio of i.i.d. Samples Motivatig example. Assume that the goal of a study is to

More information

Polynomial identity testing and global minimum cut

Polynomial identity testing and global minimum cut CHAPTER 6 Polyomial idetity testig ad global miimum cut I this lecture we will cosider two further problems that ca be solved usig probabilistic algorithms. I the first half, we will cosider the problem

More information

Infinite Sequences and Series

Infinite Sequences and Series Chapter 6 Ifiite Sequeces ad Series 6.1 Ifiite Sequeces 6.1.1 Elemetary Cocepts Simply speakig, a sequece is a ordered list of umbers writte: {a 1, a 2, a 3,...a, a +1,...} where the elemets a i represet

More information

Random Walks on Discrete and Continuous Circles. by Jeffrey S. Rosenthal School of Mathematics, University of Minnesota, Minneapolis, MN, U.S.A.

Random Walks on Discrete and Continuous Circles. by Jeffrey S. Rosenthal School of Mathematics, University of Minnesota, Minneapolis, MN, U.S.A. Radom Walks o Discrete ad Cotiuous Circles by Jeffrey S. Rosethal School of Mathematics, Uiversity of Miesota, Mieapolis, MN, U.S.A. 55455 (Appeared i Joural of Applied Probability 30 (1993), 780 789.)

More information

Sequences A sequence of numbers is a function whose domain is the positive integers. We can see that the sequence

Sequences A sequence of numbers is a function whose domain is the positive integers. We can see that the sequence Sequeces A sequece of umbers is a fuctio whose domai is the positive itegers. We ca see that the sequece 1, 1, 2, 2, 3, 3,... is a fuctio from the positive itegers whe we write the first sequece elemet

More information

Capacity Regions of Two-User Broadcast Erasure Channels with Feedback and Hidden Memory

Capacity Regions of Two-User Broadcast Erasure Channels with Feedback and Hidden Memory Capacity Regios of Two-User Broadcast Erasure Chaels with Feedback ad Hidde Memory Michael Heidlmaier, Shiri Saeedi Bidokhti Istitute for Commuicatios Egieerig, Techische Uiversität Müche, Muich, Germay

More information

Machine Learning Theory Tübingen University, WS 2016/2017 Lecture 12

Machine Learning Theory Tübingen University, WS 2016/2017 Lecture 12 Machie Learig Theory Tübige Uiversity, WS 06/07 Lecture Tolstikhi Ilya Abstract I this lecture we derive risk bouds for kerel methods. We will start by showig that Soft Margi kerel SVM correspods to miimizig

More information

Summary and Discussion on Simultaneous Analysis of Lasso and Dantzig Selector

Summary and Discussion on Simultaneous Analysis of Lasso and Dantzig Selector Summary ad Discussio o Simultaeous Aalysis of Lasso ad Datzig Selector STAT732, Sprig 28 Duzhe Wag May 4, 28 Abstract This is a discussio o the work i Bickel, Ritov ad Tsybakov (29). We begi with a short

More information

R. van Zyl 1, A.J. van der Merwe 2. Quintiles International, University of the Free State

R. van Zyl 1, A.J. van der Merwe 2. Quintiles International, University of the Free State Bayesia Cotrol Charts for the Two-parameter Expoetial Distributio if the Locatio Parameter Ca Take o Ay Value Betwee Mius Iity ad Plus Iity R. va Zyl, A.J. va der Merwe 2 Quitiles Iteratioal, ruaavz@gmail.com

More information

Fundamental Theorem of Algebra. Yvonne Lai March 2010

Fundamental Theorem of Algebra. Yvonne Lai March 2010 Fudametal Theorem of Algebra Yvoe Lai March 010 We prove the Fudametal Theorem of Algebra: Fudametal Theorem of Algebra. Let f be a o-costat polyomial with real coefficiets. The f has at least oe complex

More information

Lecture 2. The Lovász Local Lemma

Lecture 2. The Lovász Local Lemma Staford Uiversity Sprig 208 Math 233A: No-costructive methods i combiatorics Istructor: Ja Vodrák Lecture date: Jauary 0, 208 Origial scribe: Apoorva Khare Lecture 2. The Lovász Local Lemma 2. Itroductio

More information

If a subset E of R contains no open interval, is it of zero measure? For instance, is the set of irrationals in [0, 1] is of measure zero?

If a subset E of R contains no open interval, is it of zero measure? For instance, is the set of irrationals in [0, 1] is of measure zero? 2 Lebesgue Measure I Chapter 1 we defied the cocept of a set of measure zero, ad we have observed that every coutable set is of measure zero. Here are some atural questios: If a subset E of R cotais a

More information

5.1 A mutual information bound based on metric entropy

5.1 A mutual information bound based on metric entropy Chapter 5 Global Fao Method I this chapter, we exted the techiques of Chapter 2.4 o Fao s method the local Fao method) to a more global costructio. I particular, we show that, rather tha costructig a local

More information

Resampling Methods. X (1/2), i.e., Pr (X i m) = 1/2. We order the data: X (1) X (2) X (n). Define the sample median: ( n.

Resampling Methods. X (1/2), i.e., Pr (X i m) = 1/2. We order the data: X (1) X (2) X (n). Define the sample median: ( n. Jauary 1, 2019 Resamplig Methods Motivatio We have so may estimators with the property θ θ d N 0, σ 2 We ca also write θ a N θ, σ 2 /, where a meas approximately distributed as Oce we have a cosistet estimator

More information

Discrete-Time Systems, LTI Systems, and Discrete-Time Convolution

Discrete-Time Systems, LTI Systems, and Discrete-Time Convolution EEL5: Discrete-Time Sigals ad Systems. Itroductio I this set of otes, we begi our mathematical treatmet of discrete-time s. As show i Figure, a discrete-time operates or trasforms some iput sequece x [

More information