Communications II Lecture 4: Effects of Noise on AM. Professor Kin K. Leung EEE and Computing Departments Imperial College London Copyright reserved

Size: px
Start display at page:

Download "Communications II Lecture 4: Effects of Noise on AM. Professor Kin K. Leung EEE and Computing Departments Imperial College London Copyright reserved"

Transcription

1 Commuiaio II Leure 4: Effe of Noie o M Profeor Ki K. Leug EEE ad Compuig Deparme Imperial College Lodo Copyrigh reerved

2 Noie i alog Commuiaio Syem How do variou aalog modulaio heme perform i he preee of oie? Whih heme perform be? How a we meaure i performae?

3 We mu fid a way o quaify =o meaure he performae of a modulaio heme. We ue he igal-o-oie raio SNR a he oupu of he reeiver: SNR 0 average power of meage igal a he reeiver oupu average power of oie a he reeiver oupu P P S N 3

4 Model of a aalog ommuiaio yem 4

5 P T : The ramied power Limied by: equipme apabiliy, o, goverme reriio, ierferee wih oher hael, e The higher i i, he more he reeived power PS, he higher he SNR For a fair ompario bewee differe modulaio heme: P T hould be he ame for all We ue he baebad igal o oie raio SNR baebad o alibrae he SNR value we obai 5

6 Baebad Commuiaio Syem I doe o ue modulaio I i uiable for ramiio over wire The power i rami i ideial o he meage power: P T = P The reul arry over o bad-pa yem 6

7 7

8 verage igal=meage power: P =he area uder he riagular urve ume: ddiive, whie oie wih power peral deiy PSD=N 0 / verage oie power a he reeiver: P N = area uder he raigh lie = WN0/ = WN 0 8

9 SNR a he reeiver oupu: SNR baebad PT N W 0 Noe: ume o propagaio lo PT P S Improve he SNR by: a ireaig he ramied power P T, b reriig he meage badwidh W, makig he reeiver le oiy N 0. 9

10 REVISION: mpliude Modulaio Geeral form of a M igal: [ m ]of M : he ampliude of he arrier f : he arrier frequey m: he meage igal 10

11 Modulaio idex: m p m p : he peak ampliude of m, i.e., m p = max m 11

12 Sigal reovery 1 1 m p : ue a evelope deeor Oherwie: ue yhroou deeio=produ demodulaio=ohere deeio 1

13 Syhroou deeio Muliply he waveform a he reeiver wih a loal arrier of he ame frequey ad phae a he arrier ued a he ramier: Ue a LPF o reover m + / ad fially m Problem: he reeiver you eed a igal perfely yhroized wih he ramied arrier 13

14 REVISION mpliude Modulaio: Double-idebad uppreed arrier DSB-SC m of DSBS Sigal reovery: Wih yhroou deeio oly 14

15 15 Noie i DSB-SC The rue igal reeived i: i ]o [ i o o i o f f m f f f m f f x

16 For yhroou deeio: muliply wih oπf : y of m o x m [1 o4f i4f f Ue a LPF o keep ~ y m ] o f [1 o4f i4f ] 16

17 Sigal power a he reeiver oupu: P S E{ m } E{ m } P Power of he oie igal : P W N0df N W N 0 W 17

18 SNR a he reeiver oupu: SNR 0 P N W 0 To whih ramied power doe hi orrepod? P T E{ P m o f } 18

19 So SNR 0 PT N W 0 SNR DSBSC Compario wih SNR baebad PT N W 0 SNR DSBSC SNR baebad Coluio: a DSB-SC yem provide o SNR performae gai over a baebad yem. 19

20 0 Noie i adard M, Syhroou Deeio Pre-deeio igal: i ]o [ i o ]o [ ]o [ f f m f f f m f m x

21 Sigal Reovery: Muliply wih oπf : y [1 o4f [1 o4f ] m [1 ] o4f i4f ] LPF ~ y m 1

22 Sigal power a he reeiver oupu: P S E{ m } P Noie power: P N N 0 W

23 SNR a he reeiver oupu: SNR 0 P N W 0 SNR M Tramied power: P T P P SNR of a baebad igal wih he ame ramied power: P N W SNR baebad 0 3

24 Thu: SNR M SNR baebad P P Noe: P 1 P Coluio: he performae of adard M wih yhroou reovery i wore ha ha of a baebad yem. 4

25 Noie i adard M, Evelope Deeio Phaor diagram of he igal pree a a M reeiver E i : reeiver oupu= y 5

26 y evelope of x [ m ] 95 Equaio oo ompliaed Mu ue limiig ae o pu i i a form where oie ad meage are added 6

27 1 pproximaio: a Small Noie Cae [ m ] The The [ m ] y [ m ] 7

28 Thu SNR 0 P N W 0 SNR ev d i erm of baebad SNR: SNR Valid for mall oie oly! ev SNR baebad P P 8

29 9 ] [ m d pproximaio: b Large Noie Cae Iolae he mall quaiy i 95: 1 ] [ ] [ m m m m m y

30 30 where : he evelope of he oie E ] [ 1 ] [ 1 ] [ E m E m y

31 31 From he phaor diagram: = E oθ The: Ue : 1 for 1 1 x x x ]o [ 1 E m E y ]o [ ]o [ 1 m E E m E y

32 Noie i mulipliaive here! No erm proporioal o he meage! Reul: a hrehold effe, a below ome arrier power level very low, he performae of he deeor deeriorae very rapidly. 3

33 SSB modulaio Sigle lower idebad M: where mˆ mˆ SSB m of mˆ i f i he Hilber raform of m. i obaied by paig m hrough a liear filer wih rafer fuio jgf. mˆ ad m have he ame power P. The average power i P/4. 33

34 34 Noie i SSB Reeiver igal x = +. pply a bad-pa filer o he lower idebad. Uig ohere deeio: fer low-pa filerig, i4 ˆ o4 o f m f m m f x y m y

35 Sigal power P/4 Noie power for = ha for bad-pa oie = N 0 W SNR a oupu P 4N W SNR SSB 0 For a baebad yem wih he ame ramied power P/4 P 4N W SNR baebad 0 Coluio: SSB ahieve he ame SNR performae a DSB-SC ad he baebad model bu oly require half he bad-widh. 35

36 Summary 36

Communication Systems Lecture 25. Dong In Kim School of Info/Comm Engineering Sungkyunkwan University

Communication Systems Lecture 25. Dong In Kim School of Info/Comm Engineering Sungkyunkwan University Commuiaio Sysems Leure 5 Dog I Kim Shool o Io/Comm Egieerig Sugkyukwa Uiversiy 1 Oulie Noise i Agle Modulaio Phase deviaio Large SNR Small SNR Oupu SNR PM FM Review o Agle Modulaio Geeral orm o agle modulaed

More information

Principles of Communications Lecture 12: Noise in Modulation Systems. Chih-Wei Liu 劉志尉 National Chiao Tung University

Principles of Communications Lecture 12: Noise in Modulation Systems. Chih-Wei Liu 劉志尉 National Chiao Tung University Priiples of Commuiatios Leture 1: Noise i Modulatio Systems Chih-Wei Liu 劉志尉 Natioal Chiao ug Uiversity wliu@twis.ee.tu.edu.tw Outlies Sigal-to-Noise Ratio Noise ad Phase Errors i Coheret Systems Noise

More information

Digital Modulation Schemes

Digital Modulation Schemes Digial Modulaio cheme Digial ramiio chai igal repreeaio ime domai Frequecy domai igal pace Liear modulaio cheme Ampliude hi Keyig (AK) Phae hi Keyig (PK) Combiaio (APK, QAM) Pule hapig Coiuou Phae Modulaio

More information

MODERN CONTROL SYSTEMS

MODERN CONTROL SYSTEMS MODERN CONTROL SYSTEMS Lecure 9, Sae Space Repreeaio Emam Fahy Deparme of Elecrical ad Corol Egieerig email: emfmz@aa.edu hp://www.aa.edu/cv.php?dip_ui=346&er=6855 Trafer Fucio Limiaio TF = O/P I/P ZIC

More information

Lecture contents Macroscopic Electrodynamics Propagation of EM Waves in dielectrics and metals

Lecture contents Macroscopic Electrodynamics Propagation of EM Waves in dielectrics and metals Leure oes Marosopi lerodyamis Propagaio of M Waves i dieleris ad meals NNS 58 M Leure #4 Maxwell quaios Maxwell equaios desribig he ouplig of eleri ad magei fields D q ev B D J [SI] [CGS] D 4 B D 4 J B

More information

6.302 Feedback Systems Recitation : Phase-locked Loops Prof. Joel L. Dawson

6.302 Feedback Systems Recitation : Phase-locked Loops Prof. Joel L. Dawson 6.32 Feedback Syem Phae-locked loop are a foundaional building block for analog circui deign, paricularly for communicaion circui. They provide a good example yem for hi cla becaue hey are an excellen

More information

TIME RESPONSE Introduction

TIME RESPONSE Introduction TIME RESPONSE Iroducio Time repoe of a corol yem i a udy o how he oupu variable chage whe a ypical e ipu igal i give o he yem. The commoly e ipu igal are hoe of ep fucio, impule fucio, ramp fucio ad iuoidal

More information

Review - Week 10. There are two types of errors one can make when performing significance tests:

Review - Week 10. There are two types of errors one can make when performing significance tests: Review - Week Read: Chaper -3 Review: There are wo ype of error oe ca make whe performig igificace e: Type I error The ull hypohei i rue, bu we miakely rejec i (Fale poiive) Type II error The ull hypohei

More information

Consider a Binary antipodal system which produces data of δ (t)

Consider a Binary antipodal system which produces data of δ (t) Modulaion Polem PSK: (inay Phae-hi keying) Conide a inay anipodal yem whih podue daa o δ ( o + δ ( o inay and epeively. Thi daa i paed o pule haping ile and he oupu o he pule haping ile i muliplied y o(

More information

ELG3175 Introduction to Communication Systems. Angle Modulation Continued

ELG3175 Introduction to Communication Systems. Angle Modulation Continued ELG3175 Iroduio o Couiaio Sye gle Modulaio Coiued Le araériique de igaux odulé e agle PM Sigal M Sigal Iaaeou phae i Iaaeou requey Maxiu phae deviaio D ax Maxiu requey deviaio D ax Power p p p x où 0 d

More information

ME 321 Kinematics and Dynamics of Machines S. Lambert Winter 2002

ME 321 Kinematics and Dynamics of Machines S. Lambert Winter 2002 ME 31 Kiemaic ad Dyamic o Machie S. Lamber Wier 6.. Forced Vibraio wih Dampig Coider ow he cae o orced vibraio wih dampig. Recall ha he goverig diereial equaio i: m && c& k F() ad ha we will aume ha he

More information

7-Speech Quality Assessment

7-Speech Quality Assessment 7-Speech Quality Aeet Quality Level Subjective Tet Objective Tet Itelligibility Naturale Quality Level Sythetic Quality Uder 4.8 kbp Couicatio Quality 4.8 to 3 kbp Toll Quality 3 to 64 kbp Broadcat Quality

More information

Economics 8723 Macroeconomic Theory Problem Set 3 Sketch of Solutions Professor Sanjay Chugh Spring 2017

Economics 8723 Macroeconomic Theory Problem Set 3 Sketch of Solutions Professor Sanjay Chugh Spring 2017 Deparme of Ecoomic The Ohio Sae Uiveriy Ecoomic 8723 Macroecoomic Theory Problem Se 3 Skech of Soluio Profeor Sajay Chugh Sprig 27 Taylor Saggered Nomial Price-Seig Model There are wo group of moopoliically-compeiive

More information

Stability. Outline Stability Sab Stability of Digital Systems. Stability for Continuous-time Systems. system is its stability:

Stability. Outline Stability Sab Stability of Digital Systems. Stability for Continuous-time Systems. system is its stability: Oulie Sabiliy Sab Sabiliy of Digial Syem Ieral Sabiliy Exeral Sabiliy Example Roo Locu v ime Repoe Fir Orer Seco Orer Sabiliy e Jury e Rouh Crierio Example Sabiliy A very impora propery of a yamic yem

More information

What is a Communications System?

What is a Communications System? Wha is a ommuiaios Sysem? Aual Real Life Messae Real Life Messae Replia Ipu Sial Oupu Sial Ipu rasduer Oupu rasduer Eleroi Sial rasmier rasmied Sial hael Reeived Sial Reeiver Eleroi Sial Noise ad Disorio

More information

State-Space Model. In general, the dynamic equations of a lumped-parameter continuous system may be represented by

State-Space Model. In general, the dynamic equations of a lumped-parameter continuous system may be represented by Sae-Space Model I geeral, he dyaic equaio of a luped-paraeer coiuou ye ay be repreeed by x & f x, u, y g x, u, ae equaio oupu equaio where f ad g are oliear vecor-valued fucio Uig a liearized echique,

More information

BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS

BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS Opimal ear Forecasig Alhough we have o meioed hem explicily so far i he course, here are geeral saisical priciples for derivig he bes liear forecas, ad

More information

u(t) Figure 1. Open loop control system

u(t) Figure 1. Open loop control system Open loop conrol v cloed loop feedbac conrol The nex wo figure preen he rucure of open loop and feedbac conrol yem Figure how an open loop conrol yem whoe funcion i o caue he oupu y o follow he reference

More information

t = s D Overview of Tests Two-Sample t-test: Independent Samples Independent Samples t-test Difference between Means in a Two-sample Experiment

t = s D Overview of Tests Two-Sample t-test: Independent Samples Independent Samples t-test Difference between Means in a Two-sample Experiment Overview of Te Two-Sample -Te: Idepede Sample Chaper 4 z-te Oe Sample -Te Relaed Sample -Te Idepede Sample -Te Compare oe ample o a populaio Compare wo ample Differece bewee Mea i a Two-ample Experime

More information

Ruled surfaces are one of the most important topics of differential geometry. The

Ruled surfaces are one of the most important topics of differential geometry. The CONSTANT ANGLE RULED SURFACES IN EUCLIDEAN SPACES Yuuf YAYLI Ere ZIPLAR Deparme of Mahemaic Faculy of Sciece Uieriy of Aara Tadoğa Aara Turey yayli@cieceaaraedur Deparme of Mahemaic Faculy of Sciece Uieriy

More information

ECE-314 Fall 2012 Review Questions

ECE-314 Fall 2012 Review Questions ECE-34 Fall 0 Review Quesios. A liear ime-ivaria sysem has he ipu-oupu characerisics show i he firs row of he diagram below. Deermie he oupu for he ipu show o he secod row of he diagram. Jusify your aswer.

More information

Chapter 10. Laser Oscillation : Gain and Threshold

Chapter 10. Laser Oscillation : Gain and Threshold Chaper 0. aser Osillaio : Gai ad hreshold Deailed desripio of laser osillaio 0. Gai Cosider a quasi-moohromai plae wave of frequey propaai i he + direio ; u A he rae a whih

More information

Sample Final Exam (finals03) Covering Chapters 1-9 of Fundamentals of Signals & Systems

Sample Final Exam (finals03) Covering Chapters 1-9 of Fundamentals of Signals & Systems Sample Final Exam Covering Chaper 9 (final04) Sample Final Exam (final03) Covering Chaper 9 of Fundamenal of Signal & Syem Problem (0 mar) Conider he caual opamp circui iniially a re depiced below. I LI

More information

The Signal, Variable System, and Transformation: A Personal Perspective

The Signal, Variable System, and Transformation: A Personal Perspective The Sgal Varable Syem ad Traformao: A Peroal Perpecve Sherv Erfa 35 Eex Hall Faculy of Egeerg Oule Of he Talk Iroduco Mahemacal Repreeao of yem Operaor Calculu Traformao Obervao O Laplace Traform SSB A

More information

Single Phase Line Frequency Uncontrolled Rectifiers

Single Phase Line Frequency Uncontrolled Rectifiers Single Phae Line Frequency Unconrolle Recifier Kevin Gaughan 24-Nov-03 Single Phae Unconrolle Recifier 1 Topic Baic operaion an Waveform (nucive Loa) Power Facor Calculaion Supply curren Harmonic an Th

More information

Section 8 Convolution and Deconvolution

Section 8 Convolution and Deconvolution APPLICATIONS IN SIGNAL PROCESSING Secio 8 Covoluio ad Decovoluio This docume illusraes several echiques for carryig ou covoluio ad decovoluio i Mahcad. There are several operaors available for hese fucios:

More information

Downlink Transmit Diversity For Broadband Singlecarrier Distributed Antenna Network

Downlink Transmit Diversity For Broadband Singlecarrier Distributed Antenna Network Dowi rami Diveriy For Broadbad Sigearrier Diribued Aea ewor iroi MASUDA Kazui AKDA ad Fumiyui ADACI Dep. of eria ad Commuiaio gieerig, Graduae Soo of gieerig, oou Uiveriy --5 Aza-Aoba, Aramai, Aoba-u,

More information

Ideal Amplifier/Attenuator. Memoryless. where k is some real constant. Integrator. System with memory

Ideal Amplifier/Attenuator. Memoryless. where k is some real constant. Integrator. System with memory Liear Time-Ivaria Sysems (LTI Sysems) Oulie Basic Sysem Properies Memoryless ad sysems wih memory (saic or dyamic) Causal ad o-causal sysems (Causaliy) Liear ad o-liear sysems (Lieariy) Sable ad o-sable

More information

King Fahd University of Petroleum & Minerals Computer Engineering g Dept

King Fahd University of Petroleum & Minerals Computer Engineering g Dept Kig Fahd Uiversiy of Peroleum & Mierals Compuer Egieerig g Dep COE 4 Daa ad Compuer Commuicaios erm Dr. shraf S. Hasa Mahmoud Rm -4 Ex. 74 Email: ashraf@kfupm.edu.sa 9/8/ Dr. shraf S. Hasa Mahmoud Lecure

More information

8.6 Order-Recursive LS s[n]

8.6 Order-Recursive LS s[n] 8.6 Order-Recurive LS [] Motivate ti idea wit Curve Fittig Give data: 0,,,..., - [0], [],..., [-] Wat to fit a polyomial to data.., but wic oe i te rigt model?! Cotat! Quadratic! Liear! Cubic, Etc. ry

More information

Application of Combined Fourier Series Transform (Sampling Theorem)

Application of Combined Fourier Series Transform (Sampling Theorem) Applicaion o Combined Fourier Serie ranorm Sampling heorem x X[] m m Sampling Frequency Deparmen o Elecrical and Compuer Engineering Deparmen o Elecrical and Compuer Engineering X[] x We need Fourier Serie

More information

Let. x y. denote a bivariate time series with zero mean.

Let. x y. denote a bivariate time series with zero mean. Linear Filer Le x y : T denoe a bivariae ime erie wih zero mean. Suppoe ha he ime erie {y : T} i conruced a follow: y a x The ime erie {y : T} i aid o be conruced from {x : T} by mean of a Linear Filer.

More information

Interplex modulation for navigation systems at the L1 band

Interplex modulation for navigation systems at the L1 band Ierplex modulaio or avigaio yem a he L bad Emilie Rebeyrol, Chriophe Maabiau, Lioel Rie, Jea-Lu Iler, Mihel Bouque, Marie-Laure Bouhere To ie hi verio: Emilie Rebeyrol, Chriophe Maabiau, Lioel Rie, Jea-Lu

More information

The Eigen Function of Linear Systems

The Eigen Function of Linear Systems 1/25/211 The Eige Fucio of Liear Sysems.doc 1/7 The Eige Fucio of Liear Sysems Recall ha ha we ca express (expad) a ime-limied sigal wih a weighed summaio of basis fucios: v ( ) a ψ ( ) = where v ( ) =

More information

Sampling. AD Conversion (Additional Material) Sampling: Band limited signal. Sampling. Sampling function (sampling comb) III(x) Shah.

Sampling. AD Conversion (Additional Material) Sampling: Band limited signal. Sampling. Sampling function (sampling comb) III(x) Shah. AD Coversio (Addiioal Maerial Samplig Samplig Properies of real ADCs wo Sep Flash ADC Pipelie ADC Iegraig ADCs: Sigle Slope, Dual Slope DA Coverer Samplig fucio (samplig comb III(x Shah III III ( x = δ

More information

Modified Decomposition Method for Solution of Fractional Partial Differential Equations of Two-Sided

Modified Decomposition Method for Solution of Fractional Partial Differential Equations of Two-Sided Arile Ieraioal Joral of Moder Mahemaial Siee 4: 3-36 Ieraioal Joral of Moder Mahemaial Siee Joral homepage:www.modersieifipre.om/joral/ijmm.ap ISSN: 66-86X Florida USA Modified Deompoiio Mehod for Solio

More information

Solutions Manual 4.1. nonlinear. 4.2 The Fourier Series is: and the fundamental frequency is ω 2π

Solutions Manual 4.1. nonlinear. 4.2 The Fourier Series is: and the fundamental frequency is ω 2π Soluios Maual. (a) (b) (c) (d) (e) (f) (g) liear oliear liear liear oliear oliear liear. The Fourier Series is: F () 5si( ) ad he fudameal frequecy is ω f ----- H z.3 Sice V rms V ad f 6Hz, he Fourier

More information

EGR 544 Communication Theory

EGR 544 Communication Theory EGR 544 Commuicaio heory 7. Represeaio of Digially Modulaed Sigals II Z. Aliyazicioglu Elecrical ad Compuer Egieerig Deparme Cal Poly Pomoa Represeaio of Digial Modulaio wih Memory Liear Digial Modulaio

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 31 Signal & Syem Prof. Mark Fowler Noe Se #27 C-T Syem: Laplace Tranform Power Tool for yem analyi Reading Aignmen: Secion 6.1 6.3 of Kamen and Heck 1/18 Coure Flow Diagram The arrow here how concepual

More information

ECE 350 Matlab-Based Project #3

ECE 350 Matlab-Based Project #3 ECE 350 Malab-Based Projec #3 Due Dae: Nov. 26, 2008 Read he aached Malab uorial ad read he help files abou fucio i, subs, sem, bar, sum, aa2. he wrie a sigle Malab M file o complee he followig ask for

More information

Big O Notation for Time Complexity of Algorithms

Big O Notation for Time Complexity of Algorithms BRONX COMMUNITY COLLEGE of he Ciy Uiversiy of New York DEPARTMENT OF MATHEMATICS AND COMPUTER SCIENCE CSI 33 Secio E01 Hadou 1 Fall 2014 Sepember 3, 2014 Big O Noaio for Time Complexiy of Algorihms Time

More information

Electrical Engineering Department Network Lab.

Electrical Engineering Department Network Lab. Par:- Elecrical Egieerig Deparme Nework Lab. Deermiaio of differe parameers of -por eworks ad verificaio of heir ierrelaio ships. Objecive: - To deermie Y, ad ABD parameers of sigle ad cascaded wo Por

More information

S n. = n. Sum of first n terms of an A. P is

S n. = n. Sum of first n terms of an A. P is PROGREION I his secio we discuss hree impora series amely ) Arihmeic Progressio (A.P), ) Geomeric Progressio (G.P), ad 3) Harmoic Progressio (H.P) Which are very widely used i biological scieces ad humaiies.

More information

K3 p K2 p Kp 0 p 2 p 3 p

K3 p K2 p Kp 0 p 2 p 3 p Mah 80-00 Mo Ar 0 Chaer 9 Fourier Series ad alicaios o differeial equaios (ad arial differeial equaios) 9.-9. Fourier series defiiio ad covergece. The idea of Fourier series is relaed o he liear algebra

More information

Fourier transform. Continuous-time Fourier transform (CTFT) ω ω

Fourier transform. Continuous-time Fourier transform (CTFT) ω ω Fourier rasform Coiuous-ime Fourier rasform (CTFT P. Deoe ( he Fourier rasform of he sigal x(. Deermie he followig values, wihou compuig (. a (0 b ( d c ( si d ( d d e iverse Fourier rasform for Re { (

More information

13.4 Scalar Kalman Filter

13.4 Scalar Kalman Filter 13.4 Scalar Kalma Filter Data Model o derive the Kalma filter we eed the data model: a 1 + u < State quatio > + w < Obervatio quatio > Aumptio 1. u i zero mea Gauia, White, u } σ. w i zero mea Gauia, White,

More information

Dynamics for Proactive Defense through Self-hardening in the Presence or Absence of Anti Malicious Software

Dynamics for Proactive Defense through Self-hardening in the Presence or Absence of Anti Malicious Software 00 Ieraioal Joural of Compuer Appliaio 0975 8887) Volume No. 4 Dyami for roaie Defee hrough Self-hardeig i he reee or Abee of Ai Maliiou Sofare Hemraj Saii Aia rofeor, Deparme of IT, Oria Egieerig College,

More information

Corrupt the signal waveform Degrade the performance of communication systems

Corrupt the signal waveform Degrade the performance of communication systems Nie Nie : rd luui pwer i ye Crrup he igl wver Degrde he perre uii ye ure Nie: rd wderig ree eler i reir herl ie, rd lw hrge i eidur jui h ie, e. ddiive ie Zer-e Whie Gui-diribued Nie, pwer perl deiy /

More information

6/10/2014. Definition. Time series Data. Time series Graph. Components of time series. Time series Seasonal. Time series Trend

6/10/2014. Definition. Time series Data. Time series Graph. Components of time series. Time series Seasonal. Time series Trend 6//4 Defiiio Time series Daa A ime series Measures he same pheomeo a equal iervals of ime Time series Graph Compoes of ime series 5 5 5-5 7 Q 7 Q 7 Q 3 7 Q 4 8 Q 8 Q 8 Q 3 8 Q 4 9 Q 9 Q 9 Q 3 9 Q 4 Q Q

More information

Math 213b (Spring 2005) Yum-Tong Siu 1. Explicit Formula for Logarithmic Derivative of Riemann Zeta Function

Math 213b (Spring 2005) Yum-Tong Siu 1. Explicit Formula for Logarithmic Derivative of Riemann Zeta Function Math 3b Sprig 005 Yum-og Siu Expliit Formula for Logarithmi Derivative of Riema Zeta Futio he expliit formula for the logarithmi derivative of the Riema zeta futio i the appliatio to it of the Perro formula

More information

arxiv: v1 [math.nt] 13 Dec 2010

arxiv: v1 [math.nt] 13 Dec 2010 WZ-PROOFS OF DIVERGENT RAMANUJAN-TYPE SERIES arxiv:0.68v [mah.nt] Dec 00 JESÚS GUILLERA Abrac. We prove ome diverge Ramauja-ype erie for /π /π applyig a Bare-iegral raegy of he WZ-mehod.. Wilf-Zeilberger

More information

Chapter 8: Response of Linear Systems to Random Inputs

Chapter 8: Response of Linear Systems to Random Inputs Caper 8: epone of Linear yem o anom Inpu 8- Inroucion 8- nalyi in e ime Domain 8- Mean an Variance Value of yem Oupu 8-4 uocorrelaion Funcion of yem Oupu 8-5 Crocorrelaion beeen Inpu an Oupu 8-6 ample

More information

Orthogonal Code-Multiplexed Frequency-Domain Single-Carrier Spread Spectrum

Orthogonal Code-Multiplexed Frequency-Domain Single-Carrier Spread Spectrum 一般社団法人電子情報通信学会 ISI OF LOIS, IFOAIO A OIAIO GIS 信学技報 II ehia eor Orhogoa oe-iexe Freey-omai Sige-arrier Srea Serm Amar BOOKAJAY a Fmiyi AAI earme of ommiaio gieerig, Graae Shoo of gieerig, oho iveriy 6-6-5

More information

Design of Controller for Robot Position Control

Design of Controller for Robot Position Control eign of Conroller for Robo oiion Conrol Two imporan goal of conrol: 1. Reference inpu racking: The oupu mu follow he reference inpu rajecory a quickly a poible. Se-poin racking: Tracking when he reference

More information

High-Speed Serial Interface Circuits and Systems. Lect. 4 Phase-Locked Loop (PLL) Type 1 (Chap. 8 in Razavi)

High-Speed Serial Interface Circuits and Systems. Lect. 4 Phase-Locked Loop (PLL) Type 1 (Chap. 8 in Razavi) High-Speed Serial Iterface Circuit ad Sytem Lect. 4 Phae-Locked Loop (PLL) Type 1 (Chap. 8 i Razavi) PLL Phae lockig loop A (egative-feedback) cotrol ytem that geerate a output igal whoe phae (ad frequecy)

More information

CHAPTER 2 Quadratic diophantine equations with two unknowns

CHAPTER 2 Quadratic diophantine equations with two unknowns CHAPTER - QUADRATIC DIOPHANTINE EQUATIONS WITH TWO UNKNOWNS 3 CHAPTER Quadraic diophaie equaio wih wo ukow Thi chaper coi of hree ecio. I ecio (A), o rivial iegral oluio of he biar quadraic diophaie equaio

More information

6.003 Homework #13 Solutions

6.003 Homework #13 Solutions 6.003 Homework #3 Soluions Problems. Transformaion Consider he following ransformaion from x() o y(): x() w () w () w 3 () + y() p() cos() where p() = δ( k). Deermine an expression for y() when x() = sin(/)/().

More information

SLOW INCREASING FUNCTIONS AND THEIR APPLICATIONS TO SOME PROBLEMS IN NUMBER THEORY

SLOW INCREASING FUNCTIONS AND THEIR APPLICATIONS TO SOME PROBLEMS IN NUMBER THEORY VOL. 8, NO. 7, JULY 03 ISSN 89-6608 ARPN Jourl of Egieerig d Applied Sciece 006-03 Ai Reerch Publihig Nework (ARPN). All righ reerved. www.rpjourl.com SLOW INCREASING FUNCTIONS AND THEIR APPLICATIONS TO

More information

Stanford University EE 102B: Signal Processing and Linear Systems II Spring , Professor Andrea Goldsmith EE102B Course Reader

Stanford University EE 102B: Signal Processing and Linear Systems II Spring , Professor Andrea Goldsmith EE102B Course Reader Staford Uiverity EE B: Sigal Proeig ad Liear Sytem II Sprig 7-8, Profeor Adrea Goldmith EEB Coure Reader By Profeor Joeph Kah Staford Uiverity EE B: Sigal Proeig ad Liear Sytem II Profeor Joeph M Kah Table

More information

PIECEWISE N TH ORDER ADOMIAN POLYNOMIAL STIFF DIFFERENTIAL EQUATION SOLVER 13

PIECEWISE N TH ORDER ADOMIAN POLYNOMIAL STIFF DIFFERENTIAL EQUATION SOLVER 13 Abrac PIECEWISE N TH ORDER ADOMIAN POLYNOMIAL A piecewie h order Adomia polyomial olver for iiial value differeial equaio capable of olvig highly iff problem i preeed here. Thi powerful echique which employ

More information

REFERENCES. Department of Electrical, Electronic and Computer Engineering University of Pretoria 126

REFERENCES. Department of Electrical, Electronic and Computer Engineering University of Pretoria 126 RFRN [] T.. Rappapor, Wireless ommuiaios - Priiples ad Praie, eod diio, Preie-Hall, [] hp://www.gsmworld.om [3]. M. Alamoui, "A simple rasmier diversiy sheme for wireless ommuiaios," J. ele. Area ommu.,

More information

Math 2414 Homework Set 7 Solutions 10 Points

Math 2414 Homework Set 7 Solutions 10 Points Mah Homework Se 7 Soluios 0 Pois #. ( ps) Firs verify ha we ca use he iegral es. The erms are clearly posiive (he epoeial is always posiive ad + is posiive if >, which i is i his case). For decreasig we

More information

Hadamard matrices from the Multiplication Table of the Finite Fields

Hadamard matrices from the Multiplication Table of the Finite Fields adamard marice from he Muliplicaio Table of he Fiie Field 신민호 송홍엽 노종선 * Iroducio adamard mari biary m-equece New Corucio Coe Theorem. Corucio wih caoical bai Theorem. Corucio wih ay bai Remark adamard

More information

12 Getting Started With Fourier Analysis

12 Getting Started With Fourier Analysis Commuicaios Egieerig MSc - Prelimiary Readig Geig Sared Wih Fourier Aalysis Fourier aalysis is cocered wih he represeaio of sigals i erms of he sums of sie, cosie or complex oscillaio waveforms. We ll

More information

Adaptive Multiplexing Order Selection For Single-carrier MIMO Transmission

Adaptive Multiplexing Order Selection For Single-carrier MIMO Transmission Adaive Mulilexig Ode Seleio Fo Sigle-aie MIMO Tamiio Ryo AGAOKA Shiya KUMAGAI Teuya YAMAMOTO ad Fumiyui AACI e. of Commuiaio Egieeig Gaduae Shool of Egieeig Tohou Uiveiy 6-6-5 Aza-Aoba Aamai Aoba-u Sedai

More information

A TAUBERIAN THEOREM FOR THE WEIGHTED MEAN METHOD OF SUMMABILITY

A TAUBERIAN THEOREM FOR THE WEIGHTED MEAN METHOD OF SUMMABILITY U.P.B. Sci. Bull., Series A, Vol. 78, Iss. 2, 206 ISSN 223-7027 A TAUBERIAN THEOREM FOR THE WEIGHTED MEAN METHOD OF SUMMABILITY İbrahim Çaak I his paper we obai a Tauberia codiio i erms of he weighed classical

More information

Introduction to Hypothesis Testing

Introduction to Hypothesis Testing Noe for Seember, Iroducio o Hyohei Teig Scieific Mehod. Sae a reearch hyohei or oe a queio.. Gaher daa or evidece (obervaioal or eerimeal) o awer he queio. 3. Summarize daa ad e he hyohei. 4. Draw a cocluio.

More information

MITPress NewMath.cls LAT E X Book Style Size: 7x9 September 27, :04am. Contents

MITPress NewMath.cls LAT E X Book Style Size: 7x9 September 27, :04am. Contents Coes 1 Temporal filers 1 1.1 Modelig sequeces 1 1.2 Temporal filers 3 1.2.1 Temporal Gaussia 5 1.2.2 Temporal derivaives 6 1.2.3 Spaioemporal Gabor filers 8 1.3 Velociy-ued filers 9 Bibliography 13 1

More information

M-ary Detection Problem. Lecture Notes 2: Detection Theory. Example 1: Additve White Gaussian Noise

M-ary Detection Problem. Lecture Notes 2: Detection Theory. Example 1: Additve White Gaussian Noise Hi ue Hi ue -ay Deecio Pole Coide he ole of decidig which of hyohei i ue aed o oevig a ado vaiale (veco). he efoace cieia we coide i he aveage eo oailiy. ha i he oailiy of decidig ayhig ece hyohei H whe

More information

A PROPOSED HANGUP FREE AND SELF-NOISE REDUCTION METHOD FOR DIGITAL SYMBOL SYNCHRONISER IN MFSK SYSTEMS

A PROPOSED HANGUP FREE AND SELF-NOISE REDUCTION METHOD FOR DIGITAL SYMBOL SYNCHRONISER IN MFSK SYSTEMS A PROPOSE HANGUP FREE AN SELF-NOISE REUCTION METHO FOR IGITAL SYMBOL SYNCHRONISER IN MFSK SYSTEMS C.. LEE ad M. ARNELL Iiue of Iegraed Iformaio Syem School of Elecroic ad Elecrical Egieerig, The Uiveriy

More information

Development of Kalman Filter and Analogs Schemes to Improve Numerical Weather Predictions

Development of Kalman Filter and Analogs Schemes to Improve Numerical Weather Predictions Developme of Kalma Filer ad Aalogs Schemes o Improve Numerical Weaher Predicios Luca Delle Moache *, Aimé Fourier, Yubao Liu, Gregory Roux, ad Thomas Warer (NCAR) Thomas Nipe, ad Rolad Sull (UBC) Wid Eergy

More information

MATH 507a ASSIGNMENT 4 SOLUTIONS FALL 2018 Prof. Alexander. g (x) dx = g(b) g(0) = g(b),

MATH 507a ASSIGNMENT 4 SOLUTIONS FALL 2018 Prof. Alexander. g (x) dx = g(b) g(0) = g(b), MATH 57a ASSIGNMENT 4 SOLUTIONS FALL 28 Prof. Alexader (2.3.8)(a) Le g(x) = x/( + x) for x. The g (x) = /( + x) 2 is decreasig, so for a, b, g(a + b) g(a) = a+b a g (x) dx b so g(a + b) g(a) + g(b). Sice

More information

The Purpose of this talk The generation of the high-frequency resonant FEL wave by means of it s low-frequency wave as a pomp wave

The Purpose of this talk The generation of the high-frequency resonant FEL wave by means of it s low-frequency wave as a pomp wave The Purpoe of hi alk The generaion of he high-frequency reonan FEL wave y mean of i low-frequency wave a a pomp wave A free elecron laer ha wo reonan frequencie wih : λ 1, = ( 1 ± β β ) λ w In a waveguide:

More information

ELEG5693 Wireless Communications Propagation and Noise Part II

ELEG5693 Wireless Communications Propagation and Noise Part II Deparme of Elecrical Egieerig Uiversiy of Arkasas ELEG5693 Wireless Commuicaios Propagaio ad Noise Par II Dr. Jigxia Wu wuj@uark.edu OUTLINE Wireless chael Pah loss Shadowig Small scale fadig Simulaio

More information

David Randall. ( )e ikx. k = u x,t. u( x,t)e ikx dx L. x L /2. Recall that the proof of (1) and (2) involves use of the orthogonality condition.

David Randall. ( )e ikx. k = u x,t. u( x,t)e ikx dx L. x L /2. Recall that the proof of (1) and (2) involves use of the orthogonality condition. ! Revised April 21, 2010 1:27 P! 1 Fourier Series David Radall Assume ha u( x,) is real ad iegrable If he domai is periodic, wih period L, we ca express u( x,) exacly by a Fourier series expasio: ( ) =

More information

A Theoretical Model of a Voltage Controlled Oscillator

A Theoretical Model of a Voltage Controlled Oscillator A Theoreical Model of a Volage Conrolled Ocillaor Yenming Chen Advior: Dr. Rober Scholz Communicaion Science Iniue Univeriy of Souhern California UWB Workhop, April 11-1, 6 Inroducion Moivaion The volage

More information

Lesson 5. Chapter 7. Wiener Filters. Bengt Mandersson. r x k We assume uncorrelated noise v(n). LTH. September 2010

Lesson 5. Chapter 7. Wiener Filters. Bengt Mandersson. r x k We assume uncorrelated noise v(n). LTH. September 2010 Optimal Sigal Poceig Leo 5 Chapte 7 Wiee Filte I thi chapte we will ue the model how below. The igal ito the eceive i ( ( iga. Nomally, thi igal i ditubed by additive white oie v(. The ifomatio i i (.

More information

SUMMATION OF INFINITE SERIES REVISITED

SUMMATION OF INFINITE SERIES REVISITED SUMMATION OF INFINITE SERIES REVISITED I several aricles over he las decade o his web page we have show how o sum cerai iiie series icludig he geomeric series. We wa here o eed his discussio o he geeral

More information

Last time: Completed solution to the optimum linear filter in real-time operation

Last time: Completed solution to the optimum linear filter in real-time operation 6.3 tochatic Etimatio ad Cotrol, Fall 4 ecture at time: Completed olutio to the oimum liear filter i real-time operatio emi-free cofiguratio: t D( p) F( p) i( p) dte dp e π F( ) F( ) ( ) F( p) ( p) 4444443

More information

6.003 Homework #5 Solutions

6.003 Homework #5 Solutions 6. Homework #5 Soluios Problems. DT covoluio Le y represe he DT sigal ha resuls whe f is covolved wih g, i.e., y[] = (f g)[] which is someimes wrie as y[] = f[] g[]. Deermie closed-form expressios for

More information

1. Solve by the method of undetermined coefficients and by the method of variation of parameters. (4)

1. Solve by the method of undetermined coefficients and by the method of variation of parameters. (4) 7 Differeial equaios Review Solve by he mehod of udeermied coefficies ad by he mehod of variaio of parameers (4) y y = si Soluio; we firs solve he homogeeous equaio (4) y y = 4 The correspodig characerisic

More information

Time Dependent Queuing

Time Dependent Queuing Time Depede Queuig Mark S. Daski Deparme of IE/MS, Norhweser Uiversiy Evaso, IL 628 Sprig, 26 Oulie Will look a M/M/s sysem Numerically iegraio of Chapma- Kolmogorov equaios Iroducio o Time Depede Queue

More information

EE 330 Lecture 40. Digital Circuits. Propagation Delay With Multiple Levels of Logic Overdrive

EE 330 Lecture 40. Digital Circuits. Propagation Delay With Multiple Levels of Logic Overdrive EE 330 Lecure 0 Digial ircuis Propagaion Delay Wih Muliple Levels of Logic Overdrive Review from Las Time Propagaion Delay in Saic MOS Family F Propagaion hrough k levels of logic + + + + HL HLn LH(n-1)

More information

Digital Signal Processing, Fall 2010

Digital Signal Processing, Fall 2010 Digital Sigal Proeig, Fall 2 Leture 3: Samplig ad reotrutio, traform aalyi of LTI ytem tem Zheg-ua Ta Departmet of Eletroi Sytem Aalborg Uiverity, Demar t@e.aau.d Coure at a glae MM Direte-time igal ad

More information

Some Improved Estimators for Population Variance Using Two Auxiliary Variables in Double Sampling

Some Improved Estimators for Population Variance Using Two Auxiliary Variables in Double Sampling Vplav Kumar gh Rajeh gh Deparme of ac Baara Hdu Uver Varaa-00 Ida Flore maradache Uver of ew Meco Gallup UA ome Improved Emaor for Populao Varace Ug Two Aular Varable Double amplg Publhed : Rajeh gh Flore

More information

Interpolation and Pulse Shaping

Interpolation and Pulse Shaping EE345S Real-Time Digial Signal Proceing Lab Spring 2006 Inerpolaion and Pule Shaping Prof. Brian L. Evan Dep. of Elecrical and Compuer Engineering The Univeriy of Texa a Auin Lecure 7 Dicree-o-Coninuou

More information

Professor: Mihnea UDREA DIGITAL SIGNAL PROCESSING. Grading: Web: MOODLE. 1. Introduction. General information

Professor: Mihnea UDREA DIGITAL SIGNAL PROCESSING. Grading: Web:   MOODLE. 1. Introduction. General information Geeral iformatio DIGITL SIGL PROCESSIG Profeor: ihea UDRE B29 mihea@comm.pub.ro Gradig: Laboratory: 5% Proect: 5% Tet: 2% ial exam : 5% Coure quiz: ±% Web: www.electroica.pub.ro OODLE 2 alog igal proceig

More information

AN IMPROVED SIMULATION MODEL OF RAYLEIGH FADING CHANNELS

AN IMPROVED SIMULATION MODEL OF RAYLEIGH FADING CHANNELS IJAL 5: (06) 3-39 Deember 06 ISSN: 394-58 Available a h://ieifiavae.o.i DOI: h://x.oi.org/0.864/ijamml_700740 AN IPROVED SIULATION ODEL OF RAYLEIGH FADING CHANNELS Cuijua Guo, Zhe Yag a Zhigag Wu Shool

More information

Notes 03 largely plagiarized by %khc

Notes 03 largely plagiarized by %khc 1 1 Discree-Time Covoluio Noes 03 largely plagiarized by %khc Le s begi our discussio of covoluio i discree-ime, sice life is somewha easier i ha domai. We sar wih a sigal x[] ha will be he ipu io our

More information

1 Notes on Little s Law (l = λw)

1 Notes on Little s Law (l = λw) Copyrigh c 26 by Karl Sigma Noes o Lile s Law (l λw) We cosider here a famous ad very useful law i queueig heory called Lile s Law, also kow as l λw, which assers ha he ime average umber of cusomers i

More information

EE 330 Lecture 41. Digital Circuits. Propagation Delay With Multiple Levels of Logic Overdrive

EE 330 Lecture 41. Digital Circuits. Propagation Delay With Multiple Levels of Logic Overdrive EE 330 Lecure 41 Digial ircuis Propagaion Delay Wih Muliple Levels of Logic Overdrive Review from Las Time The Reference Inverer Reference Inverer V DD R =R PD PU = IN= 4OX WMIN LMIN V IN M 2 M 1 L VTn.2VDD

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 31 Signals & Sysems Prof. Mar Fowler Noe Se #1 C-T Signals: Circuis wih Periodic Sources 1/1 Solving Circuis wih Periodic Sources FS maes i easy o find he response of an RLC circui o a periodic source!

More information

Probability Density Functions of Envelope and Phase of the Sum of a PSK Modulated Carrier and Narrowband Gaussian Noise

Probability Density Functions of Envelope and Phase of the Sum of a PSK Modulated Carrier and Narrowband Gaussian Noise robabiliy Desiy Fuios of Eveloe a hase of he Su of a SK oulae Carrier a Narrowba Gaussia Noise Auhor: Lohar Frieerihs AUDENS Teleouiaios Cosulig Dae: 6.6. wih orreios a eesios of 8.7.8 Coes age. Soe. Uoulae

More information

Pure Math 30: Explained!

Pure Math 30: Explained! ure Mah : Explaied! www.puremah.com 6 Logarihms Lesso ar Basic Expoeial Applicaios Expoeial Growh & Decay: Siuaios followig his ype of chage ca be modeled usig he formula: (b) A = Fuure Amou A o = iial

More information

Theoretical Design for Double Chirped Mirrors in Femtosecond Pulse Lasers

Theoretical Design for Double Chirped Mirrors in Femtosecond Pulse Lasers Ieraioal Joural of Phyic ad Applicaio. ISSN 0974-303 Volume 3, Number 3 (0), pp. 9-4 Ieraioal Reearch Publicaio Houe hp://www.irphoue.com Theoreical Deig for Double Chirped Mirror i Femoecod Pule Laer

More information

Algorithmic Discrete Mathematics 6. Exercise Sheet

Algorithmic Discrete Mathematics 6. Exercise Sheet Algorihmic Dicree Mahemaic. Exercie Shee Deparmen of Mahemaic SS 0 PD Dr. Ulf Lorenz 7. and 8. Juni 0 Dipl.-Mah. David Meffer Verion of June, 0 Groupwork Exercie G (Heap-Sor) Ue Heap-Sor wih a min-heap

More information

Introduction to AC Power, RMS RMS. ECE 2210 AC Power p1. Use RMS in power calculations. AC Power P =? DC Power P =. V I = R =. I 2 R. V p.

Introduction to AC Power, RMS RMS. ECE 2210 AC Power p1. Use RMS in power calculations. AC Power P =? DC Power P =. V I = R =. I 2 R. V p. ECE MS I DC Power P I = Inroducion o AC Power, MS I AC Power P =? A Solp //9, // // correced p4 '4 v( ) = p cos( ω ) v( ) p( ) Couldn' we define an "effecive" volage ha would allow us o use he same relaionships

More information

B Signals and Systems I Solutions to Midterm Test 2. xt ()

B Signals and Systems I Solutions to Midterm Test 2. xt () 34-33B Signals and Sysems I Soluions o Miderm es 34-33B Signals and Sysems I Soluions o Miderm es ednesday Marh 7, 7:PM-9:PM Examiner: Prof. Benoi Boule Deparmen of Elerial and Compuer Engineering MGill

More information

Exercise: Show that. Remarks: (i) Fc(l) is not continuous at l=c. (ii) In general, we have. yn ¾¾. Solution:

Exercise: Show that. Remarks: (i) Fc(l) is not continuous at l=c. (ii) In general, we have. yn ¾¾. Solution: Exercie: Show ha Soluio: y ¾ y ¾¾ L c Þ y ¾¾ p c. ¾ L c Þ F y (l Fc (l I[c,(l "l¹c Þ P( y c

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 30 Signal & Syem Prof. ark Fowler oe Se #34 C-T Tranfer Funcion and Frequency Repone /4 Finding he Tranfer Funcion from Differenial Eq. Recall: we found a DT yem Tranfer Funcion Hz y aking he ZT of

More information