Research on Efficient Turbo Frequency Domain Equalization in STBC-MIMO System
|
|
- Veronica Eaton
- 6 years ago
- Views:
Transcription
1 Research o Effcet urbo Freuecy Doma Eualzato SBC-MIMO System Wau uag Bejg echology ad Busess Uversty Bejg R. Cha Abstract. A effcet urbo Freuecy Doma Eualzato FDE based o symbol-wse mmum mea-suare error MMSE flterg s roosed for a ovel sace-tme bloc code SBC MIMO system. he trasmtter seds a searate data bloc va SBC usg two ateas er grou to get dversty ga. he recever ca effectvely utlze ter-atea terferece IAI ad ter-symbol terferece ISI followed by freuecy doma eualzato to rocess soft terferece cacellato SIC. After freuecy doma flterg e symbol Log-lelhood rato LLRs calculated from e oututs of eualzer s as e uts of e soft- soft-out SISO decoder. Smulato results show at our roosed scheme rovdes a furer substatal ga whle ot creasg comlexty at e recever. Keywords: MIMO; MMSE; sgle carrer; SBC; urbo FDE. Itroducto I s aer we develo a teratve detecto ad decodg algorm of SC-FDE based o [] for a ovel SBCMIMO wreless system. he recever ca effectvely utlze ter-atea terferece IAI ad ter-symbol terferece ISI followed by freuecy doma eualzato to rocess soft terferece cacellatosic ad symbol Log-lelhood rato LLR s calculated as e uts of e soft- softout SISO decoder usg e oututs of eualzer. So t ca realze teratve chael eualzato ad chael decodg at each terato. heory aalyss ad smulato results bo show at our roosed algorm ca mrove e system erformace remarably comared w geeral MIMO system. System Overvew Wau uag female master ayag ea rovce lecturer. Research drecto: Comuter Alcato etwor ecoomc maagemet. el: E-mal: huagw@.btbu.edu.c hs wor was suort by a grat from Research Fud for Youg Scholars I Bejg echology ad Busess Uversty.R.ChaO. QJJ0-6 Bejg hlosohy ad socal scece lag rojects.r.chao. JGB08 ad he Geeral rogram of Bejg Mucal Educato Commttee.R.ChaO.SM000008research result of stage. CCA 03 ASL Vol SERSC 03 07
2 roceedgs he d Iteratoal Coferece o Comuter ad Alcatos We defe at t x =... ; t = v v + ; v =0... s e t bloc sgal o e stream before sace-tme bloc codg. After trasferrg each sgal bloc to freuecy doma by -ot Fast Fourer rasform FF corresodg sgal s t t X x = s e t bloc sgal o trasmtted atea atea o e stream after beg ecoded accordg to SBC rcle. Where t t t t x = x x... x t t t t X = X... X X t t t t x = x... x x t t t+ t+ Bloc-wse SBC rcle s show as x = x x = x t t+ t+ t x = x x = x e At e recever after dscardg e C e tme doma sgals o t bloc ca be exressed as freuecy doma sgal s R atea for t r = Qad e corresodg receved t t t t.where t r = r r... r t t t t R = R R... R I order to exress clearly we defe t t+ t t+ Let E [ e e e ] x x x... x x = X = X X X X R = R R R R =... t t+ t t+... t t+ t t+... Q Q = where e deotes e ut vector at ca get freuecy doma sgal at e =0 - toe ca be wrtte as R = E F x+ Z = X + Z 5 Where = Q Q... Q Q Q Q... Q Q 3 Freuecy Doma MMSE urbo Eualzato We descrbe t t+ t t+ x= x x... x x x x... x = A. Soft ISI ad IAI Cacellato he estmate of e desred symbol ca be roduced by a freuecy doma MMSE flter after e ISI ad IAI cacellato e freuecy doma. We assume e symbol o e atea x =... ; =... s e desred
3 Research o Effcet urbo Freuecy Doma Eualzato SBC-MIMO System symbol. I e aer e exected IAI ad ISI for x ca be reseted as α α = E F x 6 β resectvely ˆ = E F xˆ β 7 ˆ xˆ = Where 0 x x x+ x 8 xˆ = 0 0 xˆ 00 0 ˆ ˆ ˆ xˆ ˆ = x ˆ x 0 x + x after SIC e sgal e freuecy doma at toe s wrtte as Y = R α β = E F xx x + Z ˆ ˆ ˆ ˆ = E F x x + Z Cosderg all freuecy toes e soft terferece cacellato model ca be exressed as follows Y = R = F x xˆ + Z α β 0 After soft terferece cacellato sgal to terferece lus ose rato SIR of sgal Y has bee mroved comared w orgal receved data. he freuecy doma MMSE eualzato s mlemeted whle soft terferece s gored terato zero sce ere s o ror formato. B. Effcet Freuecy Doma MMSE Flterg Symbol-wse MMSE crtero ca be wrtte as order to detect e desred symbol { } m x. E D Y x D = arg D { } E DY x Y = Accordg to e orogoalty rcle we have he 0 s substtuted to w a assumto at ere s o correlato betwee data symbols ad AWG. 0 { } D { } { } D F E xxˆ x xˆ F + E ZZ E x x xˆ F = 0 3 We assumed at e symbols are deedet ad e ror formato about e x desred symbol should ot be used e evaluato e we have 4 E x ˆ x x =Φ = 0 0 σ s 0 0 = σ s e { } = + { ˆ ˆ } { =Γ ϒϒ ϒ ϒϒ ϒ} E x x x x = dag 5 + where ϒ j = dag { ϒ j ϒ } j ϒ j j ad ϒ j m s varace of symbol xj 9 ad 09
4 roceedgs he d Iteratoal Coferece o Comuter ad Alcatos o e bass of ror formato from decoder. τ = dag ϒ ϒ ϒ σ ϒ + ϒ ad σ s e symbol eergy. { } s cov m m m x x ϒ = 6 j j j =Φ F F Γ F + σ I 7 Fally we obtaed D s Q C. Extrsc LLR Calculato After eualzato e estmate of tme doma symbol x ca be obtaed by IFF = = + = + xˆ D Y D F x xˆ Z D F e x xˆ xˆ D Z 8 I 8 we ca see at e frst term s e exected symbol multled by a factor e secod term s e resdual terferece from oer ateas ad symbols e rd term s AWG. As e terato cotue e ror formato becomes more ad more exact. So a assumto s made [9] at e outut of MMSE eualzer has udergoe a euvalet Gaussa chael φ λ x = x + 9 ˆ he e soft-ut soft-outut decoder ca utlze extrsc formato from eualzer whch s treated as e ror formato to calculate extrsc LLR accordg to e exectato ad varace of eualzed data symbol. As descrbed x ca be comuted [3] exectato µ = D F e 0 σ s wrtte as σ ˆ ˆ reew x µ x x µ σ s Varace he fucto of reew. s to reew = as a orgal modulated symbol. he extrsc LLR for e symbol ca be obtaed xˆ µ Le x = σ D. Low Comlexty Imlemetato As e eualzato s rocessed based o symbol-wse t s hard to mlemet due to e comlexty. Cosderg at e dagoal elemets of e freuecy doma Θ = F Γ 7 are costat w e same value F covarace matrx ω j = γ j j 3 = he off-dagoal elemets ca be gored because e dagoal elemets s larger a e off-dagoal elemets. herefore we aroxmate ω = γ 4 = s 0
5 Research o Effcet urbo Freuecy Doma Eualzato SBC-MIMO System After smlcato Θ = dag { ω I ω I νi ω I ω I } 6 + Accordgly eualzer coeffcets are gve by D =Φ F Θ + σ I 7 Q s 4 Smulato Results he BER erformace of our roosed turbo eualzato algorm for SBC- MIMO system s showed Fg.. It s obvous at our roosed teratve eualzer acheves sgfcat erformace comared w e tradtoal o-teratve oes esecally uder well chael codto. As e terato tmes creases e erformace of e roosed system s better but e teratve gas become comaratvely smaller esecally after 3 teratos. Fg.. BER erformace of our roosed eualzer for SBC-MIMO 5 Cocluso I s aer we roose a ovel urbo FDE based o symbol-wse detecto for sgle carrer SBC-MIMO system. he trasmtter ateas double to get dversty ga wout creasg recevg ateas. hs algorm ca effectvely utlze ter-atea terfereceiai ad ter-symbol terfereceiai followed by freuecy doma eualzato to rocess soft terferece cacellatosic. Smulato results have show at our roosed algorm acheves better BER erformace comared to bo e tradtoal o-teratve oes ad oes w geeral MIMO system. Refereces. Baoj L Zhfeg Rua ad Yogyu Chag Effcet urbo Freuecy Doma Eualzato Based o Symbol-Wse Detecto IEEE Iteratoal Coferece o Commcatos 08 to be ublshed.. A. Dejoghe ad L. Vadedore urbo-eualzato for multlevel modulato: a effcet low-comlexty scheme IEEE ICC 0 May C M. uchler ad J. ageauer Lear tme ad freuecy doma turbo eualzato IEEE VC 0 May C453
Robust Adaptive Volterra Filter under Maximum Correntropy Criteria in Impulsive Environments
Robust Adatve Volterra Flter uder Maxmum Corretroy Crtera Imulsve Evromets Weyua Wag Haqua Zhao Badog Che Abstract: As a robust adatato crtero the maxmum corretroy crtero (MCC) has gaed creased atteto
More informationDr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur
Aalyss of Varace ad Desg of Exermets-I MODULE II LECTURE - GENERAL LINEAR HYPOTHESIS AND ANALYSIS OF VARIANCE Dr Shalabh Deartmet of Mathematcs ad Statstcs Ida Isttute of Techology Kaur Tukey s rocedure
More informationA Robust Total Least Mean Square Algorithm For Nonlinear Adaptive Filter
A Robust otal east Mea Square Algorthm For Nolear Adaptve Flter Ruxua We School of Electroc ad Iformato Egeerg X'a Jaotog Uversty X'a 70049, P.R. Cha rxwe@chare.com Chogzhao Ha, azhe u School of Electroc
More informationRecursive linear estimation for discrete time systems in the presence of different multiplicative observation noises
Recursve lear estmato for dscrete tme systems the resece of dfferet multlcatve observato oses C. Sáchez Gozález,*,.M. García Muñoz Deartameto de Métodos Cuattatvos ara la Ecoomía y la Emresa, Facultad
More informationDiversity for Wireless Communications
Dverst for Wreless Commucatos For AWG caels, te probablt of error decas epoetall wt SR c.f. Q. fucto P e Q SR SR e For fadg caels, te deca s muc slower. For Raleg-fadg caels cael coeffcet s comple Gaussa=>ampltude
More informationCOV. Violation of constant variance of ε i s but they are still independent. The error term (ε) is said to be heteroscedastic.
c Pogsa Porchawseskul, Faculty of Ecoomcs, Chulalogkor Uversty olato of costat varace of s but they are stll depedet. C,, he error term s sad to be heteroscedastc. c Pogsa Porchawseskul, Faculty of Ecoomcs,
More informationSTRONG CONSISTENCY FOR SIMPLE LINEAR EV MODEL WITH v/ -MIXING
Joural of tatstcs: Advaces Theory ad Alcatos Volume 5, Number, 6, Pages 3- Avalable at htt://scetfcadvaces.co. DOI: htt://d.do.org/.864/jsata_7678 TRONG CONITENCY FOR IMPLE LINEAR EV MODEL WITH v/ -MIXING
More informationPERFORMANCE EVALUATION OF C-BLAST MIMO SYSTEMS USING MMSE DETECTION ALGORITHM
53 EFOACE EVALUATIO OF C-BLAST IO SYSTES USIG SE DETECTIO ALGOITH urhayat * * hyscs Departemet, FIA, Surabaya State Uversty Jl. Kettag Surabaya 603 d@grad.ts.ac.d ABSTACT C-BLAST system s detecto algorthm
More informationTwo Fuzzy Probability Measures
Two Fuzzy robablty Measures Zdeěk Karíšek Isttute of Mathematcs Faculty of Mechacal Egeerg Bro Uversty of Techology Techcká 2 66 69 Bro Czech Reublc e-mal: karsek@umfmevutbrcz Karel Slavíček System dmstrato
More informationA LFM Interference Suppression Scheme Based on FRFT and Subspace Projection
teratoal Joural of Emergg Egeerg esearch ad Techology Volume 3, ssue 6, Jue 15, PP 157-16 SS 349-4395 (Prt) & SS 349-449 (Ole) A LF terferece Suppresso Scheme Based o FFT ad Subspace Projecto Xg ZOU 1
More information2. Independence and Bernoulli Trials
. Ideedece ad Beroull Trals Ideedece: Evets ad B are deedet f B B. - It s easy to show that, B deedet mles, B;, B are all deedet ars. For examle, ad so that B or B B B B B φ,.e., ad B are deedet evets.,
More information2SLS Estimates ECON In this case, begin with the assumption that E[ i
SLS Estmates ECON 3033 Bll Evas Fall 05 Two-Stage Least Squares (SLS Cosder a stadard lear bvarate regresso model y 0 x. I ths case, beg wth the assumto that E[ x] 0 whch meas that OLS estmates of wll
More informationBasics of Information Theory: Markku Juntti. Basic concepts and tools 1 Introduction 2 Entropy, relative entropy and mutual information
: Maru Jutt Overvew he propertes of adlmted Gaussa chaels are further studed, parallel Gaussa chaels ad Gaussa chaels wth feedac are solved. Source he materal s maly ased o Sectos.4.6 of the course oo
More informationTESTS BASED ON MAXIMUM LIKELIHOOD
ESE 5 Toy E. Smth. The Basc Example. TESTS BASED ON MAXIMUM LIKELIHOOD To llustrate the propertes of maxmum lkelhood estmates ad tests, we cosder the smplest possble case of estmatg the mea of the ormal
More informationSTK3100 and STK4100 Autumn 2017
SK3 ad SK4 Autum 7 Geeralzed lear models Part III Covers the followg materal from chaters 4 ad 5: Sectos 4..5, 4.3.5, 4.3.6, 4.4., 4.4., ad 4.4.3 Sectos 5.., 5.., ad 5.5. Ørulf Borga Deartmet of Mathematcs
More informationEconometric Methods. Review of Estimation
Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators
More informationConvergence of the Desroziers scheme and its relation to the lag innovation diagnostic
Covergece of the Desrozers scheme ad ts relato to the lag ovato dagostc chard Méard Evromet Caada, Ar Qualty esearch Dvso World Weather Ope Scece Coferece Motreal, August 9, 04 o t t O x x x y x y Oservato
More informationENGI 4421 Joint Probability Distributions Page Joint Probability Distributions [Navidi sections 2.5 and 2.6; Devore sections
ENGI 441 Jot Probablty Dstrbutos Page 7-01 Jot Probablty Dstrbutos [Navd sectos.5 ad.6; Devore sectos 5.1-5.] The jot probablty mass fucto of two dscrete radom quattes, s, P ad p x y x y The margal probablty
More informationResearch Article A New Derivation and Recursive Algorithm Based on Wronskian Matrix for Vandermonde Inverse Matrix
Mathematcal Problems Egeerg Volume 05 Artcle ID 94757 7 pages http://ddoorg/055/05/94757 Research Artcle A New Dervato ad Recursve Algorthm Based o Wroska Matr for Vadermode Iverse Matr Qu Zhou Xja Zhag
More informationECONOMETRIC THEORY. MODULE VIII Lecture - 26 Heteroskedasticity
ECONOMETRIC THEORY MODULE VIII Lecture - 6 Heteroskedastcty Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur . Breusch Paga test Ths test ca be appled whe the replcated data
More informationTokyo Institute of Technology Tokyo Institute of Technology
Outle ult-aget Search usg oroo Partto ad oroo D eermet Revew Itroducto Decreasg desty fucto Stablty Cocluso Fujta Lab, Det. of Cotrol ad System Egeerg, FL07--: July 09,007 Davd Ask ork rogress:. Smulato
More informationX X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then
Secto 5 Vectors of Radom Varables Whe workg wth several radom varables,,..., to arrage them vector form x, t s ofte coveet We ca the make use of matrx algebra to help us orgaze ad mapulate large umbers
More informationComparison of Dual to Ratio-Cum-Product Estimators of Population Mean
Research Joural of Mathematcal ad Statstcal Sceces ISS 30 6047 Vol. 1(), 5-1, ovember (013) Res. J. Mathematcal ad Statstcal Sc. Comparso of Dual to Rato-Cum-Product Estmators of Populato Mea Abstract
More information2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America
SOLUTION OF SYSTEMS OF SIMULTANEOUS LINEAR EQUATIONS Gauss-Sedel Method 006 Jame Traha, Autar Kaw, Kev Mart Uversty of South Florda Uted States of Amerca kaw@eg.usf.edu Itroducto Ths worksheet demostrates
More informationModified Cosine Similarity Measure between Intuitionistic Fuzzy Sets
Modfed ose mlarty Measure betwee Itutostc Fuzzy ets hao-mg wag ad M-he Yag,* Deartmet of led Mathematcs, hese ulture Uversty, Tae, Tawa Deartmet of led Mathematcs, hug Yua hrsta Uversty, hug-l, Tawa msyag@math.cycu.edu.tw
More informationTransforms that are commonly used are separable
Trasforms s Trasforms that are commoly used are separable Eamples: Two-dmesoal DFT DCT DST adamard We ca the use -D trasforms computg the D separable trasforms: Take -D trasform of the rows > rows ( )
More informationSimulation Output Analysis
Smulato Output Aalyss Summary Examples Parameter Estmato Sample Mea ad Varace Pot ad Iterval Estmato ermatg ad o-ermatg Smulato Mea Square Errors Example: Sgle Server Queueg System x(t) S 4 S 4 S 3 S 5
More informationIS 709/809: Computational Methods in IS Research. Simple Markovian Queueing Model
IS 79/89: Comutatoal Methods IS Research Smle Marova Queueg Model Nrmalya Roy Deartmet of Iformato Systems Uversty of Marylad Baltmore Couty www.umbc.edu Queueg Theory Software QtsPlus software The software
More informationUnimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods
Malaysa Umodalty Joural Tests of Mathematcal for Global Optmzato Sceces (): of 05 Sgle - 5 Varable (007) Fuctos Usg Statstcal Methods Umodalty Tests for Global Optmzato of Sgle Varable Fuctos Usg Statstcal
More informationLinear Regression Linear Regression with Shrinkage. Some slides are due to Tommi Jaakkola, MIT AI Lab
Lear Regresso Lear Regresso th Shrkage Some sldes are due to Tomm Jaakkola, MIT AI Lab Itroducto The goal of regresso s to make quattatve real valued predctos o the bass of a vector of features or attrbutes.
More informationECE 559: Wireless Communication Project Report Diversity Multiplexing Tradeoff in MIMO Channels with partial CSIT. Hoa Pham
ECE 559: Wreless Commucato Project Report Dversty Multplexg Tradeoff MIMO Chaels wth partal CSIT Hoa Pham. Summary I ths project, I have studed the performace ga of MIMO systems. There are two types of
More informationChannel Polarization and Polar Codes; Capacity Achieving
Chael Polarzato ad Polar Codes; Capacty chevg Peyma Hesam Tutoral of Iformato Theory Course Uversty of otre Dame December, 9, 009 bstract: ew proposed method for costructg codes that acheves the symmetrc
More informationSTK3100 and STK4100 Autumn 2018
SK3 ad SK4 Autum 8 Geeralzed lear models Part III Covers the followg materal from chaters 4 ad 5: Cofdece tervals by vertg tests Cosder a model wth a sgle arameter β We may obta a ( α% cofdece terval for
More informationA Method for Damping Estimation Based On Least Square Fit
Amerca Joural of Egeerg Research (AJER) 5 Amerca Joural of Egeerg Research (AJER) e-issn: 3-847 p-issn : 3-936 Volume-4, Issue-7, pp-5-9 www.ajer.org Research Paper Ope Access A Method for Dampg Estmato
More informationThe Mathematical Appendix
The Mathematcal Appedx Defto A: If ( Λ, Ω, where ( λ λ λ whch the probablty dstrbutos,,..., Defto A. uppose that ( Λ,,..., s a expermet type, the σ-algebra o λ λ λ are defed s deoted by ( (,,...,, σ Ω.
More informationQuantum Plain and Carry Look-Ahead Adders
Quatum Pla ad Carry Look-Ahead Adders Ka-We Cheg u8984@cc.kfust.edu.tw Che-Cheg Tseg tcc@ccms.kfust.edu.tw Deartmet of Comuter ad Commucato Egeerg, Natoal Kaohsug Frst Uversty of Scece ad Techology, Yechao,
More informationSeveral Theorems for the Trace of Self-conjugate Quaternion Matrix
Moder Aled Scece Setember, 008 Several Theorems for the Trace of Self-cojugate Quatero Matrx Qglog Hu Deartmet of Egeerg Techology Xchag College Xchag, Schua, 6503, Cha E-mal: shjecho@6com Lm Zou(Corresodg
More informationAnalysis of Variance with Weibull Data
Aalyss of Varace wth Webull Data Lahaa Watthaacheewaul Abstract I statstcal data aalyss by aalyss of varace, the usual basc assumptos are that the model s addtve ad the errors are radomly, depedetly, ad
More informationReliability evaluation of distribution network based on improved non. sequential Monte Carlo method
3rd Iteratoal Coferece o Mecatrocs, Robotcs ad Automato (ICMRA 205) Relablty evaluato of dstrbuto etwork based o mproved o sequetal Mote Carlo metod Je Zu, a, Cao L, b, Aog Tag, c Scool of Automato, Wua
More informationå 1 13 Practice Final Examination Solutions - = CS109 Dec 5, 2018
Chrs Pech Fal Practce CS09 Dec 5, 08 Practce Fal Examato Solutos. Aswer: 4/5 8/7. There are multle ways to obta ths aswer; here are two: The frst commo method s to sum over all ossbltes for the rak of
More informationContinuous Random Variables: Conditioning, Expectation and Independence
Cotuous Radom Varables: Codtog, xectato ad Ideedece Berl Che Deartmet o Comuter cece & Iormato geerg atoal Tawa ormal Uverst Reerece: - D.. Bertsekas, J.. Tstskls, Itroducto to robablt, ectos 3.4-3.5 Codtog
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted
More informationMultiple Linear Regression Analysis
LINEA EGESSION ANALYSIS MODULE III Lecture - 4 Multple Lear egresso Aalyss Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur Cofdece terval estmato The cofdece tervals multple
More information4. Standard Regression Model and Spatial Dependence Tests
4. Stadard Regresso Model ad Spatal Depedece Tests Stadard regresso aalss fals the presece of spatal effects. I case of spatal depedeces ad/or spatal heterogeet a stadard regresso model wll be msspecfed.
More informationρ < 1 be five real numbers. The
Lecture o BST 63: Statstcal Theory I Ku Zhag, /0/006 Revew for the prevous lecture Deftos: covarace, correlato Examples: How to calculate covarace ad correlato Theorems: propertes of correlato ad covarace
More information9.1 Introduction to the probit and logit models
EC3000 Ecoometrcs Lecture 9 Probt & Logt Aalss 9. Itroducto to the probt ad logt models 9. The logt model 9.3 The probt model Appedx 9. Itroducto to the probt ad logt models These models are used regressos
More information{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:
Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed
More informationMultiple Choice Test. Chapter Adequacy of Models for Regression
Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to
More informationError probability and error stream properties in channel with slow Rician fading
Error probablty ad error stream propertes chael wth slow Rca fadg Paper Krystya M. Noga Abstract I a rado commucato chael wave parameters fluctuate radomly. The sgal evelope udergoes deep fades. Whe bary
More informationAn Efficient Selective Receiver for Multiple-Input Multiple-Output Scheme
> REPLACE I LINE WI YOUR PAPER IDENIFICAION NUMBER (DOUBLE-CLICK ERE O EDI) < A Effcet electve Recever for Multple-Iput Multple-Output ceme Lu Lu, tudet Member, IEEE, ad Myoug-eob Lm, Member, IEEE Abstract
More informationEntropy, Relative Entropy and Mutual Information
Etro Relatve Etro ad Mutual Iformato rof. Ja-Lg Wu Deartmet of Comuter Scece ad Iformato Egeerg Natoal Tawa Uverst Defto: The Etro of a dscrete radom varable s defed b : base : 0 0 0 as bts 0 : addg terms
More informationLecture 3 Probability review (cont d)
STATS 00: Itroducto to Statstcal Iferece Autum 06 Lecture 3 Probablty revew (cot d) 3. Jot dstrbutos If radom varables X,..., X k are depedet, the ther dstrbuto may be specfed by specfyg the dvdual dstrbuto
More informationε. Therefore, the estimate
Suggested Aswers, Problem Set 3 ECON 333 Da Hugerma. Ths s ot a very good dea. We kow from the secod FOC problem b) that ( ) SSE / = y x x = ( ) Whch ca be reduced to read y x x = ε x = ( ) The OLS model
More informationResearch on SVM Prediction Model Based on Chaos Theory
Advaced Scece ad Techology Letters Vol.3 (SoftTech 06, pp.59-63 http://dx.do.org/0.457/astl.06.3.3 Research o SVM Predcto Model Based o Chaos Theory Sog Lagog, Wu Hux, Zhag Zezhog 3, College of Iformato
More informationSignal,autocorrelation -0.6
Sgal,autocorrelato Phase ose p/.9.3.7. -.5 5 5 5 Tme Sgal,autocorrelato Phase ose p/.5..7.3 -. -.5 5 5 5 Tme Sgal,autocorrelato. Phase ose p/.9.3.7. -.5 5 5 5 Tme Sgal,autocorrelato. Phase ose p/.8..6.
More informationThird handout: On the Gini Index
Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The
More informationSimple Linear Regression
Statstcal Methods I (EST 75) Page 139 Smple Lear Regresso Smple regresso applcatos are used to ft a model descrbg a lear relatoshp betwee two varables. The aspects of least squares regresso ad correlato
More informationBayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information
Malaysa Joural of Mathematcal Sceces (): 97- (9) Bayes Estmator for Expoetal Dstrbuto wth Exteso of Jeffery Pror Iformato Hadeel Salm Al-Kutub ad Noor Akma Ibrahm Isttute for Mathematcal Research, Uverst
More informationChapter 11 Systematic Sampling
Chapter stematc amplg The sstematc samplg techue s operatoall more coveet tha the smple radom samplg. It also esures at the same tme that each ut has eual probablt of cluso the sample. I ths method of
More informationEstimation of Stress- Strength Reliability model using finite mixture of exponential distributions
Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur
More informationDiagnosing Problems of Distribution-Free Multivariate Control Chart
Advaced Materals Research Ole: 4-6-5 ISSN: 66-8985, Vols. 97-973, 6-66 do:.48/www.scetfc.et/amr.97-973.6 4 ras ech Publcatos, Swtzerlad Dagosg Problems of Dstrbuto-Free Multvarate Cotrol Chart Wel Sh,
More informationAn Indian Journal FULL PAPER ABSTRACT KEYWORDS. Trade Science Inc. Research on scheme evaluation method of automation mechatronic systems
[ype text] [ype text] [ype text] ISSN : 0974-7435 Volume 0 Issue 6 Boechology 204 Ida Joural FULL PPER BIJ, 0(6, 204 [927-9275] Research o scheme evaluato method of automato mechatroc systems BSRC Che
More informationTHE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5
THE ROYAL STATISTICAL SOCIETY 06 EAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5 The Socety s provdg these solutos to assst cadtes preparg for the examatos 07. The solutos are teded as learg ads ad should
More informationLecture Notes Forecasting the process of estimating or predicting unknown situations
Lecture Notes. Ecoomc Forecastg. Forecastg the process of estmatg or predctg ukow stuatos Eample usuall ecoomsts predct future ecoomc varables Forecastg apples to a varet of data () tme seres data predctg
More informationCHAPTER 6. d. With success = observation greater than 10, x = # of successes = 4, and
CHAPTR 6 Secto 6.. a. We use the samle mea, to estmate the oulato mea µ. Σ 9.80 µ 8.407 7 ~ 7. b. We use the samle meda, 7 (the mddle observato whe arraged ascedg order. c. We use the samle stadard devato,
More informationA Family of Non-Self Maps Satisfying i -Contractive Condition and Having Unique Common Fixed Point in Metrically Convex Spaces *
Advaces Pure Matheatcs 0 80-84 htt://dxdoorg/0436/a04036 Publshed Ole July 0 (htt://wwwscrporg/oural/a) A Faly of No-Self Mas Satsfyg -Cotractve Codto ad Havg Uque Coo Fxed Pot Metrcally Covex Saces *
More informationChannel Models with Memory. Channel Models with Memory. Channel Models with Memory. Channel Models with Memory
Chael Models wth Memory Chael Models wth Memory Hayder radha Electrcal ad Comuter Egeerg Mchga State Uversty I may ractcal etworkg scearos (cludg the Iteret ad wreless etworks), the uderlyg chaels are
More informationLecture Note to Rice Chapter 8
ECON 430 HG revsed Nov 06 Lecture Note to Rce Chapter 8 Radom matrces Let Y, =,,, m, =,,, be radom varables (r.v. s). The matrx Y Y Y Y Y Y Y Y Y Y = m m m s called a radom matrx ( wth a ot m-dmesoal dstrbuto,
More informationChapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II
CEE49b Chapter - Free Vbrato of Mult-Degree-of-Freedom Systems - II We ca obta a approxmate soluto to the fudametal atural frequecy through a approxmate formula developed usg eergy prcples by Lord Raylegh
More informationCOMBINED-TYPE FAMILY OF ESTIMATORS OF POPULATION MEAN IN STRATIFIED RANDOM SAMPLING UNDER NON-RESPONSE
Joural of elablty ad tatstcal tudes; I (Prt): 974-84, (Ole):9-5666 Vol. 5, Issue (): 33-4 COMBIED-YPE FAMILY OF EIMAO OF POPULAIO MEA I AIFIED ADOM AMPLIG UDE O-EPOE Maoj K. Chaudhary, V. K. gh ad. K.
More informationLecture 07: Poles and Zeros
Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto
More informationECE 595, Section 10 Numerical Simulations Lecture 19: FEM for Electronic Transport. Prof. Peter Bermel February 22, 2013
ECE 595, Secto 0 Numercal Smulatos Lecture 9: FEM for Electroc Trasport Prof. Peter Bermel February, 03 Outle Recap from Wedesday Physcs-based devce modelg Electroc trasport theory FEM electroc trasport
More informationAnalysis of Lagrange Interpolation Formula
P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal
More informationCODING & MODULATION Prof. Ing. Anton Čižmár, PhD.
CODING & MODULATION Prof. Ig. Ato Čžmár, PhD. also from Dgtal Commucatos 4th Ed., J. G. Proaks, McGraw-Hll It. Ed. 00 CONTENT. PROBABILITY. STOCHASTIC PROCESSES Probablty ad Stochastc Processes The theory
More informationIntroducing Sieve of Eratosthenes as a Theorem
ISSN(Ole 9-8 ISSN (Prt - Iteratoal Joural of Iovatve Research Scece Egeerg ad echolog (A Hgh Imact Factor & UGC Aroved Joural Webste wwwrsetcom Vol Issue 9 Setember Itroducg Seve of Eratosthees as a heorem
More informationbest estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best
Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg
More informationSignificance Testing in Exact Logistic Multiple Regression
BULLETIN of the MALAYSIAN MATHEMATICAL SCIENCES SOCIETY Bull. Malays. Math. Sc. Soc. () 7 (4), 7 15 Sgfcace Testg Exact Logstc Multle Regresso MEZBAHUR RAHMAN AND SHUVRO CHAKROBARTTY Mesota State Uversty,
More informationLINEAR EQUALIZERS & NONLINEAR EQUALIZERS. Prepared by Deepa.T, Asst.Prof. /TCE
LINEAR EQUALIZERS & NONLINEAR EQUALIZERS Prepared by Deepa.T, Asst.Prof. /TCE Eqalzers The goal of eqalzers s to elmate tersymbol terferece (ISI) ad the addtve ose as mch as possble. Itersymbol terferece(isi)
More informationSummary of the lecture in Biostatistics
Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the
More informationTHE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Series A, OF THE ROMANIAN ACADEMY Volume 9, Number 3/2008, pp
THE PUBLISHIN HOUSE PROCEEDINS OF THE ROMANIAN ACADEMY, Seres A, OF THE ROMANIAN ACADEMY Volume 9, Number 3/8, THE UNITS IN Stela Corelu ANDRONESCU Uversty of Pteşt, Deartmet of Mathematcs, Târgu Vale
More informationA Note on Ratio Estimators in two Stage Sampling
Iteratoal Joural of Scetfc ad Research Publcatos, Volume, Issue, December 0 ISS 0- A ote o Rato Estmators two Stage Samplg Stashu Shekhar Mshra Lecturer Statstcs, Trdet Academy of Creatve Techology (TACT),
More informationTHE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA
THE ROYAL STATISTICAL SOCIETY 3 EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA PAPER I STATISTICAL THEORY & METHODS The Socety provdes these solutos to assst caddates preparg for the examatos future years ad
More informationTraining Sample Model: Given n observations, [[( Yi, x i the sample model can be expressed as (1) where, zero and variance σ
Stat 74 Estmato for Geeral Lear Model Prof. Goel Broad Outle Geeral Lear Model (GLM): Trag Samle Model: Gve observatos, [[( Y, x ), x = ( x,, xr )], =,,, the samle model ca be exressed as Y = µ ( x, x,,
More informationMinimizing Total Completion Time in a Flow-shop Scheduling Problems with a Single Server
Joural of Aled Mathematcs & Boformatcs vol. o.3 0 33-38 SSN: 79-660 (rt) 79-6939 (ole) Sceress Ltd 0 Mmzg Total omleto Tme a Flow-sho Schedulg Problems wth a Sgle Server Sh lg ad heg xue-guag Abstract
More informationManipulator Dynamics. Amirkabir University of Technology Computer Engineering & Information Technology Department
Mapulator Dyamcs mrkabr Uversty of echology omputer Egeerg formato echology Departmet troducto obot arm dyamcs deals wth the mathematcal formulatos of the equatos of robot arm moto. hey are useful as:
More informationSolution of General Dual Fuzzy Linear Systems. Using ABS Algorithm
Appled Mathematcal Sceces, Vol 6, 0, o 4, 63-7 Soluto of Geeral Dual Fuzzy Lear Systems Usg ABS Algorthm M A Farborz Aragh * ad M M ossezadeh Departmet of Mathematcs, Islamc Azad Uversty Cetral ehra Brach,
More informationProbability and Statistics. What is probability? What is statistics?
robablt ad Statstcs What s robablt? What s statstcs? robablt ad Statstcs robablt Formall defed usg a set of aoms Seeks to determe the lkelhood that a gve evet or observato or measuremet wll or has haeed
More informationLINEAR REGRESSION ANALYSIS
LINEAR REGRESSION ANALYSIS MODULE V Lecture - Correctg Model Iadequaces Through Trasformato ad Weghtg Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur Aalytcal methods for
More informationCarbonyl Groups. University of Chemical Technology, Beijing , PR China;
Electroc Supplemetary Materal (ESI) for Physcal Chemstry Chemcal Physcs Ths joural s The Ower Socetes 0 Supportg Iformato A Theoretcal Study of Structure-Solublty Correlatos of Carbo Doxde Polymers Cotag
More informationLecture Notes 2. The ability to manipulate matrices is critical in economics.
Lecture Notes. Revew of Matrces he ablt to mapulate matrces s crtcal ecoomcs.. Matr a rectagular arra of umbers, parameters, or varables placed rows ad colums. Matrces are assocated wth lear equatos. lemets
More informationLecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model
Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The
More informationEntropy ISSN by MDPI
Etropy 2003, 5, 233-238 Etropy ISSN 1099-4300 2003 by MDPI www.mdp.org/etropy O the Measure Etropy of Addtve Cellular Automata Hasa Aı Arts ad Sceces Faculty, Departmet of Mathematcs, Harra Uversty; 63100,
More informationChapter 8. Inferences about More Than Two Population Central Values
Chapter 8. Ifereces about More Tha Two Populato Cetral Values Case tudy: Effect of Tmg of the Treatmet of Port-We tas wth Lasers ) To vestgate whether treatmet at a youg age would yeld better results tha
More informationChapter 2 General Linear Hypothesis and Analysis of Variance
Chater Geeral Lear Hyothess ad Aalyss of Varace Regresso model for the geeral lear hyothess Let Y, Y,..., Y be a seuece of deedet radom varables assocated wth resoses. The we ca wrte t as EY ( ) = β x,
More informationIntroduction to local (nonparametric) density estimation. methods
Itroducto to local (oparametrc) desty estmato methods A slecture by Yu Lu for ECE 66 Sprg 014 1. Itroducto Ths slecture troduces two local desty estmato methods whch are Parze desty estmato ad k-earest
More informationOptimization Design and Analysis of Systematic LT codes over AWGN Channel
Optmzato Desg ad Aalyss of Systematc LT codes over AWGN Chael Shegka Xu, Dazhua Xu, Xaofe Zhag ad Haq Shao College of Electroc ad Iformato Egeerg Najg Uversty of Aeroautcs ad Astroautcs Emal: xudazhua@uaa.edu.c
More informationFunctions of Random Variables
Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,
More informationChapter 10 Two Stage Sampling (Subsampling)
Chapter 0 To tage amplg (usamplg) I cluster samplg, all the elemets the selected clusters are surveyed oreover, the effcecy cluster samplg depeds o sze of the cluster As the sze creases, the effcecy decreases
More informationArithmetic Mean and Geometric Mean
Acta Mathematca Ntresa Vol, No, p 43 48 ISSN 453-6083 Arthmetc Mea ad Geometrc Mea Mare Varga a * Peter Mchalča b a Departmet of Mathematcs, Faculty of Natural Sceces, Costate the Phlosopher Uversty Ntra,
More informationIdea is to sample from a different distribution that picks points in important regions of the sample space. Want ( ) ( ) ( ) E f X = f x g x dx
Importace Samplg Used for a umber of purposes: Varace reducto Allows for dffcult dstrbutos to be sampled from. Sestvty aalyss Reusg samples to reduce computatoal burde. Idea s to sample from a dfferet
More information