Mobile Radio Communications

Size: px
Start display at page:

Download "Mobile Radio Communications"

Transcription

1 Course 3: Radio wave propagation Session 3, page 1

2 Propagation mechanisms free space propagation reflection diffraction scattering LARGE SCALE: average attenuation SMALL SCALE: short-term variations in position and time dx >> λ dt >> T S dx λ dt T S Session 3, page

3 Large-scale/small scale variations space PL (db) d Session 3, page 3

4 Large-scale/small scale variations time PL (db) t Session 3, page 4

5 Free-space propagation P: power G: antenna gain d: separation distance L: system loss P R ( d ) P G G = T T R λ ( ) 4π d L Session 3, page 5

6 Antenna gain isotropic: G=1 directional: G(θ) G = 4 π e λ A EIRP: effective isotropic radiated power (received power as if received from isotropic source) EIRP = G T P T Session 3, page 6

7 Free-space path loss PL =10log PT P R = 0 log 4π + λ ( d ) = PL + 0log 0 0log d d d 0 attenuation 0 db/dec dec Session 3, page 7

8 Ground wave reflection d 1 h t d h r E tot α α = cos c ct d d 1 ( ω t) + Γ cos( ω + θ ) Session 3, page 8

9 Ground wave reflection = d d 1 = d h t h r d + ( ) h + h d + ( h h ) t r d >> h t r θ 4πh t h r λd Session 3, page 9

10 Ground wave reflection θ α/d 1 α/d E E α = d α = d {( θ ) + θ } 1 cos sin α ( cos θ ) θ d ( ) Session 3, page 10

11 Ground wave reflection E α 4πht h d λd r P r = E Ae ( ht hr ) β 4 10π d A e G λ 4π Session 3, page 11

12 Two-ray path loss model PL = 40 log d ( 10logG t + 10logGr + 0log ht + 0log hr ) attenuation 40 db/dec dec Session 3, page 1

13 Path loss exponent Session 3, page 13

14 Log-distance path loss model P L = PL( d0) + 10n log d d 0 d: T-R distance d 0 : close-in reference distance n: path loss exponent ranging from 1 to 6 Session 3, page 14

15 Okumura path loss model L 50 = L + A ( f, d) G( h ) G( h ) F mu t r G AREA ht G ( ht ) = 0 log( ) 00 hr G ( hr ) = 10 log( ) 3 hr G ( hr ) = 0 log( ) 3 10m < h t < 1000m h r < 3m 3m < h t < 10m Session 3, page 15

16 Hata path loss model L = log 13.8 log h 50 c t r + ( log h t )log d + C( fc) f a( h ) Session 3, page 16

17 Coverage area actual coverage ideal cell boundary Session 3, page 17

18 Log-normal shadowing PL (db) d PL = PL(d) + Χ σ Session 3, page 18

19 Log-normal shadowing Χ = N ( 0, σ ) zero-mean Gaussion with standard dev. σ outdoor: σ=6-9db indoor: σ=-1db Session 3, page 19

20 Link budget P t P t : permitted transmit power P r : required receive power PL: path loss N: noise floor NF: noise factor FM: fading margin SNR: required signal-to-noise ratio PL FM SNR NF P R N Session 3, page 0

21 Multipath fading PL (db) d Session 3, page 1

22 Multipath effects d 1 d time delay fading in amplitude dispersion (echoes) Doppler spread speed angle of arrival Session 3, page

23 Delay space phenomenon Session 3, page 3

24 Fading d λ τ λ = c 1 f c θ π RF waves s = r 1 1 cosθ1 s = r cosθ s = r 3 3 cosθ3 Session 3, page 4

25 Time dispersion τ T S τ t t Session 3, page 5

26 Doppler spread d 1 d v λ ( φ ) cosφ f d = v 0 φ π v λ f d v λ Session 3, page 6

27 Doppler shifts Session 3, page 7

28 Doppler spread v (km/h) f c (MHz) f d (Hz) Session 3, page 8

29 Delay profile Session 3, page 9

30 Measured delay profile Session 3, page 30

31 Impulse response model time-variant, linear system: h( t, τ ) t: time variant due to motion (ms to s) τ: time dispersion (ns to µs) filter representation: y() t = x() t h( t, τ ) Session 3, page 31

32 FIR representation h b ( t, τ ) N 1 = i= 0 a i () t exp{ jθ () t } δ ( τ τ ) i i a 0,θ 0 a 1,θ 1 a,θ a 3,θ 3 a 4,θ 4 t 0 τ 0 τ 1 τ τ 3 τ 4 h b (t,τ) BW signal < 1/( τ) τ0: excess delay reference N τ: maximum excess delay Session 3, page 3

33 FIR representation t t h b (t,τ) t 1 h b (t 1,τ) t 0 τ 0 τ 1 τ τ 3 τ 4 h b (t 0,τ) Session 3, page 33

34 Power delay profile h b ( t, τ ) measured in local area spatial averaging (-6m) Session 3, page 34

35 Time dispersion parameters τ = k k P( τ ) τ k P( τ ) k k τ = k k P( τ ) τ k P( τ ) k k mean excess delay: τ rms delay spread: σ τ = τ (τ ) maximum excess delay: τ where P P max Session 3, page 35

36 Time dispersion example normalized RX power (db) σ τ τ = 55ns = 70ns τ excess delay (ns) Session 3, page 36

37 Time dispersion values environment f (MHz) σ τ urban suburban indoor µs ns 70-90ns Session 3, page 37

38 Coherence bandwidth h(t,τ) H(f) B c : f where frequencies become uncorrelated corr.>0.5 B c 1 5σ τ Session 3, page 38

39 Flat fading All frequency components fade identically. B s << B c T s >>σ τ S(f) H(f) f f Session 3, page 39

40 Frequency-selective fading Different frequency components fade differently. B s > B c T s <σ τ S(f) H(f) f f Session 3, page 40

41 Rayleigh fading Amplitude fading N i.i.d. components: resultant Normally (Gaussian) distributed Gaussian distribution on I and Q gives Rayleigh on envelope Q I Session 3, page 41

42 Rayleigh distribution p r r r) = exp ( r σ σ ( 0) p(r) /σ r = 1.533σ σ r = 0.49σ 0 σ σ 3σ 4σ r Session 3, page 4

43 Rician fading Amplitude fading One dominant component + N i.i.d. components: resultant Rician distributed Session 3, page 43

44 Rician distribution p r r + A Ar r) = exp 0 (, I A r σ σ σ ( 0) A: amplitude of dominant component I 0 : zero-order Bessel function Rician factor: K = A σ Session 3, page 44

45 Rician distribution K - : K : approaching Rayleigh approaching N(A,σ) p(r) K - K=3dB 0 σ σ 3σ 4σ r Session 3, page 45

46 h(τ) Modeling: flat fading a, φ t F f s(t) a exp(jφ) a: Rayleigh dist. φ: uniform (0,π] Session 3, page 46

47 Modeling: -ray fading h(τ) a 1, φ 1 a, φ 0 τ t F f s(t) Σ a 1 exp(jφ 1 ) τ a exp(jφ ) Session 3, page 47

48 Doppler spread d 1 d v λ ( φ ) cosφ f d = v 0 φ π v λ f d v λ Session 3, page 48

49 Doppler power spectrum S D ( f ) = f m 1 α f f m f c f m = v λ f c -f m f c f c +f m Session 3, page 49

50 Coherence time S D (f) h D (t) T c : t where samples become uncorrelated T c 1 f m Session 3, page 50

51 Slow and fast fading Fast fading: B s < B D T s >Τ c Slow fading: B s >> B D T s <<Τ c Session 3, page 51

52 Slow/fast - flat/freq freq-sel.. fading Flat / frequency selective fading: Echo pattern h(t,τ) Fast / slow fading: Motion effects h(t,τ) Session 3, page 5

53 T s Slow/fast - flat/freq freq-sel.. fading flat, slow fading flat, fast fading σ τ frequency selective, slow fading frequency selective, fast fading T c T s Session 3, page 53

54 B s Slow/fast - flat/freq freq-sel.. fading frequency selective, fast fading frequency selective, slow fading B c flat, fast fading flat, slow fading B D B s Session 3, page 54

55 Multipath time scales dt Amplitude fading: (i.e. dt=1ns) Time dispersion: (i.e. dt=1µs) dt dt Doppler spread: (i.e. dt=10ms) 1 f c d c 1 f d Session 3, page 55

56 FOR NEXT WEEK Read: Chapter 5: 5.1, 5. (not 5.., 5..3), 5.3 (not 5.3., 5.3.3) (not 5.7.8) Solve problems: Chapter 4: 4., 4.3, 4.5, 4.18, 4.1 Session 3, page 56

Lecture 2. Fading Channel

Lecture 2. Fading Channel 1 Lecture 2. Fading Channel Characteristics of Fading Channels Modeling of Fading Channels Discrete-time Input/Output Model 2 Radio Propagation in Free Space Speed: c = 299,792,458 m/s Isotropic Received

More information

ECE6604 PERSONAL & MOBILE COMMUNICATIONS. Week 3. Flat Fading Channels Envelope Distribution Autocorrelation of a Random Process

ECE6604 PERSONAL & MOBILE COMMUNICATIONS. Week 3. Flat Fading Channels Envelope Distribution Autocorrelation of a Random Process 1 ECE6604 PERSONAL & MOBILE COMMUNICATIONS Week 3 Flat Fading Channels Envelope Distribution Autocorrelation of a Random Process 2 Multipath-Fading Mechanism local scatterers mobile subscriber base station

More information

Multiple Antennas. Mats Bengtsson, Björn Ottersten. Channel characterization and modeling 1 September 8, Signal KTH Research Focus

Multiple Antennas. Mats Bengtsson, Björn Ottersten. Channel characterization and modeling 1 September 8, Signal KTH Research Focus Multiple Antennas Channel Characterization and Modeling Mats Bengtsson, Björn Ottersten Channel characterization and modeling 1 September 8, 2005 Signal Processing @ KTH Research Focus Channel modeling

More information

CHAPTER 14. Based on the info about the scattering function we know that the multipath spread is T m =1ms, and the Doppler spread is B d =0.2 Hz.

CHAPTER 14. Based on the info about the scattering function we know that the multipath spread is T m =1ms, and the Doppler spread is B d =0.2 Hz. CHAPTER 4 Problem 4. : Based on the info about the scattering function we know that the multipath spread is T m =ms, and the Doppler spread is B d =. Hz. (a) (i) T m = 3 sec (ii) B d =. Hz (iii) ( t) c

More information

Lecture 7: Wireless Channels and Diversity Advanced Digital Communications (EQ2410) 1

Lecture 7: Wireless Channels and Diversity Advanced Digital Communications (EQ2410) 1 Wireless : Wireless Advanced Digital Communications (EQ2410) 1 Thursday, Feb. 11, 2016 10:00-12:00, B24 1 Textbook: U. Madhow, Fundamentals of Digital Communications, 2008 1 / 15 Wireless Lecture 1-6 Equalization

More information

Fading Statistical description of the wireless channel

Fading Statistical description of the wireless channel Channel Modelling ETIM10 Lecture no: 3 Fading Statistical description of the wireless channel Fredrik Tufvesson Department of Electrical and Information Technology Lund University, Sweden Fredrik.Tufvesson@eit.lth.se

More information

EE6604 Personal & Mobile Communications. Week 5. Level Crossing Rate and Average Fade Duration. Statistical Channel Modeling.

EE6604 Personal & Mobile Communications. Week 5. Level Crossing Rate and Average Fade Duration. Statistical Channel Modeling. EE6604 Personal & Mobile Communications Week 5 Level Crossing Rate and Average Fade Duration Statistical Channel Modeling Fading Simulators 1 Level Crossing Rate and Average Fade Duration The level crossing

More information

s o (t) = S(f)H(f; t)e j2πft df,

s o (t) = S(f)H(f; t)e j2πft df, Sample Problems for Midterm. The sample problems for the fourth and fifth quizzes as well as Example on Slide 8-37 and Example on Slides 8-39 4) will also be a key part of the second midterm.. For a causal)

More information

Ranging detection algorithm for indoor UWB channels

Ranging detection algorithm for indoor UWB channels Ranging detection algorithm for indoor UWB channels Choi Look LAW and Chi XU Positioning and Wireless Technology Centre Nanyang Technological University 1. Measurement Campaign Objectives Obtain a database

More information

ECE6604 PERSONAL & MOBILE COMMUNICATIONS. Week 4. Envelope Correlation Space-time Correlation

ECE6604 PERSONAL & MOBILE COMMUNICATIONS. Week 4. Envelope Correlation Space-time Correlation ECE6604 PERSONAL & MOBILE COMMUNICATIONS Week 4 Envelope Correlation Space-time Correlation 1 Autocorrelation of a Bandpass Random Process Consider again the received band-pass random process r(t) = g

More information

Communication Systems Lecture 21, 22. Dong In Kim School of Information & Comm. Eng. Sungkyunkwan University

Communication Systems Lecture 21, 22. Dong In Kim School of Information & Comm. Eng. Sungkyunkwan University Communication Systems Lecture 1, Dong In Kim School of Information & Comm. Eng. Sungkyunkwan University 1 Outline Linear Systems with WSS Inputs Noise White noise, Gaussian noise, White Gaussian noise

More information

III. Spherical Waves and Radiation

III. Spherical Waves and Radiation III. Spherical Waves and Radiation Antennas radiate spherical waves into free space Receiving antennas, reciprocity, path gain and path loss Noise as a limit to reception Ray model for antennas above a

More information

Emmanouel T. Michailidis Athanasios G. Kanatas

Emmanouel T. Michailidis Athanasios G. Kanatas Emmanouel T. Michailidis (emichail@unipi.gr) George Efthymoglou (gefthymo@unipi.gr) gr) Athanasios G. Kanatas (kanatas@unipi.gr) University of Piraeus Department of Digital Systems Wireless Communications

More information

On Coding for Orthogonal Frequency Division Multiplexing Systems

On Coding for Orthogonal Frequency Division Multiplexing Systems On Coding for Orthogonal Frequency Division Multiplexing Systems Alan Clark Department of Electrical and Computer Engineering A thesis presented for the degree of Doctor of Philosophy University of Canterbury

More information

Perfect Modeling and Simulation of Measured Spatio-Temporal Wireless Channels

Perfect Modeling and Simulation of Measured Spatio-Temporal Wireless Channels Perfect Modeling and Simulation of Measured Spatio-Temporal Wireless Channels Matthias Pätzold Agder University College Faculty of Engineering and Science N-4876 Grimstad, Norway matthiaspaetzold@hiano

More information

Stochastic Models for System Simulations

Stochastic Models for System Simulations Lecture 4: Stochastic Models for System Simulations Contents: Radio channel simulation Stochastic modelling COST 7 model CODIT model Turin-Suzuki-Hashemi model Saleh-Valenzuela model Stochastic Models

More information

APPENDIX 1 NEYMAN PEARSON CRITERIA

APPENDIX 1 NEYMAN PEARSON CRITERIA 54 APPENDIX NEYMAN PEARSON CRITERIA The design approaches for detectors directly follow the theory of hypothesis testing. The primary approaches to hypothesis testing problem are the classical approach

More information

Communication Theory Summary of Important Definitions and Results

Communication Theory Summary of Important Definitions and Results Signal and system theory Convolution of signals x(t) h(t) = y(t): Fourier Transform: Communication Theory Summary of Important Definitions and Results X(ω) = X(ω) = y(t) = X(ω) = j x(t) e jωt dt, 0 Properties

More information

Reverberation Impulse Response Analysis

Reverberation Impulse Response Analysis MUS424: Signal Processing Techniques for Digital Audio Effects Handout #18 Jonathan Abel, David Berners April 27, 24 Lecture #9: April 27, 24 Lecture Notes 7 Reverberation Impulse Response Analysis 1 CCRMA

More information

Tutorial (4) Problem (1):

Tutorial (4) Problem (1): Problem (1): Tutorial (4) Consider the WLAN supported within the campus of the GUC. Assume that an access point is located in the second floor. The transmitter power at the access point is 5 dbm. Free

More information

Unbiased Power Prediction of Rayleigh Fading Channels

Unbiased Power Prediction of Rayleigh Fading Channels Unbiased Power Prediction of Rayleigh Fading Channels Torbjörn Ekman UniK PO Box 70, N-2027 Kjeller, Norway Email: torbjorn.ekman@signal.uu.se Mikael Sternad and Anders Ahlén Signals and Systems, Uppsala

More information

GISM Global Ionospheric Scintillation Model

GISM Global Ionospheric Scintillation Model GISM Global Ionospheric Scintillation Model http://www.ieea.fr/en/gism-web-interface.html Y. Béniguel, IEEA Béniguel Y., P. Hamel, A Global Ionosphere Scintillation Propagation Model for Equatorial Regions,

More information

Maximum Achievable Diversity for MIMO-OFDM Systems with Arbitrary. Spatial Correlation

Maximum Achievable Diversity for MIMO-OFDM Systems with Arbitrary. Spatial Correlation Maximum Achievable Diversity for MIMO-OFDM Systems with Arbitrary Spatial Correlation Ahmed K Sadek, Weifeng Su, and K J Ray Liu Department of Electrical and Computer Engineering, and Institute for Systems

More information

Analog Electronics 2 ICS905

Analog Electronics 2 ICS905 Analog Electronics 2 ICS905 G. Rodriguez-Guisantes Dépt. COMELEC http://perso.telecom-paristech.fr/ rodrigez/ens/cycle_master/ November 2016 2/ 67 Schedule Radio channel characteristics ; Analysis and

More information

MATHEMATICAL TOOLS FOR DIGITAL TRANSMISSION ANALYSIS

MATHEMATICAL TOOLS FOR DIGITAL TRANSMISSION ANALYSIS ch03.qxd 1/9/03 09:14 AM Page 35 CHAPTER 3 MATHEMATICAL TOOLS FOR DIGITAL TRANSMISSION ANALYSIS 3.1 INTRODUCTION The study of digital wireless transmission is in large measure the study of (a) the conversion

More information

Overview of Technical Approaches

Overview of Technical Approaches Overview of Technical Approaches Robust receivers Edit / excise / blank - frequency / time domain - might lose/corrupt astronomy signal Cancel / subtract / null - identify / characterize / subtract - frequency

More information

2016 Spring: The Final Exam of Digital Communications

2016 Spring: The Final Exam of Digital Communications 2016 Spring: The Final Exam of Digital Communications The total number of points is 131. 1. Image of Transmitter Transmitter L 1 θ v 1 As shown in the figure above, a car is receiving a signal from a remote

More information

Copyright license. Exchanging Information with the Stars. The goal. Some challenges

Copyright license. Exchanging Information with the Stars. The goal. Some challenges Copyright license Exchanging Information with the Stars David G Messerschmitt Department of Electrical Engineering and Computer Sciences University of California at Berkeley messer@eecs.berkeley.edu Talk

More information

ON THE SIMULATIONS OF CORRELATED NAKAGAMI-M FADING CHANNELS USING SUM OF-SINUSOIDS METHOD

ON THE SIMULATIONS OF CORRELATED NAKAGAMI-M FADING CHANNELS USING SUM OF-SINUSOIDS METHOD ON THE SIMULATIONS OF CORRELATED NAKAGAMI-M FADING CHANNELS USING SUM OF-SINUSOIDS METHOD A Thesis Presented to the Faculty of Graduate School of University of Missouri-Columbia In Partial Fulfillment

More information

ELEG 3124 SYSTEMS AND SIGNALS Ch. 5 Fourier Transform

ELEG 3124 SYSTEMS AND SIGNALS Ch. 5 Fourier Transform Department of Electrical Engineering University of Arkansas ELEG 3124 SYSTEMS AND SIGNALS Ch. 5 Fourier Transform Dr. Jingxian Wu wuj@uark.edu OUTLINE 2 Introduction Fourier Transform Properties of Fourier

More information

Wireless Communications

Wireless Communications NETW701 Wireless Communications Dr. Wassim Alexan Winter 2018 Lecture 2 NETW705 Mobile Communication Networks Dr. Wassim Alexan Winter 2018 Lecture 2 Wassim Alexan 2 Reflection When a radio wave propagating

More information

A Simple Stochastic Channel Simulator for Car-to-Car Communication at 24 GHz

A Simple Stochastic Channel Simulator for Car-to-Car Communication at 24 GHz A Simple Stochastic Channel Simulator for Car-to-Car Communication at 24 GHz Micha Linde, Communications Engineering Lab (CEL), Karlsruhe Institute of Technology (KIT), Germany micha.linde@student.kit.edu

More information

Now identified: Noise sources Amplifier components and basic design How to achieve best signal to noise?

Now identified: Noise sources Amplifier components and basic design How to achieve best signal to noise? Signal processing Now identified: Noise sources Amplifier components and basic design How to achieve best signal to noise? Possible constraints power consumption ability to provide power & extract heat,

More information

CS6956: Wireless and Mobile Networks Lecture Notes: 2/4/2015

CS6956: Wireless and Mobile Networks Lecture Notes: 2/4/2015 CS6956: Wireless and Mobile Networks Lecture Notes: 2/4/2015 [Most of the material for this lecture has been taken from the Wireless Communications & Networks book by Stallings (2 nd edition).] Effective

More information

EE5713 : Advanced Digital Communications

EE5713 : Advanced Digital Communications EE5713 : Advanced Digital Communications Week 12, 13: Inter Symbol Interference (ISI) Nyquist Criteria for ISI Pulse Shaping and Raised-Cosine Filter Eye Pattern Equalization (On Board) 20-May-15 Muhammad

More information

SIGNAL PROCESSING FOR COMMUNICATIONS

SIGNAL PROCESSING FOR COMMUNICATIONS SIGNAL PROCESSING FOR COMMUNICATIONS ii Delft University of Technology Faculty of Electrical Engineering, Mathematics, and Computer Science Circuits and Systems SIGNAL PROCESSING FOR COMMUNICATIONS ET4

More information

ENSC327 Communications Systems 2: Fourier Representations. Jie Liang School of Engineering Science Simon Fraser University

ENSC327 Communications Systems 2: Fourier Representations. Jie Liang School of Engineering Science Simon Fraser University ENSC327 Communications Systems 2: Fourier Representations Jie Liang School of Engineering Science Simon Fraser University 1 Outline Chap 2.1 2.5: Signal Classifications Fourier Transform Dirac Delta Function

More information

On the MIMO Channel Capacity Predicted by Kronecker and Müller Models

On the MIMO Channel Capacity Predicted by Kronecker and Müller Models 1 On the MIMO Channel Capacity Predicted by Kronecker and Müller Models Müge Karaman Çolakoğlu and Mehmet Şafak Abstract This paper presents a comparison between the outage capacity of MIMO channels predicted

More information

Solution to Homework 1

Solution to Homework 1 Solution to Homework 1 1. Exercise 2.4 in Tse and Viswanath. 1. a) With the given information we can comopute the Doppler shift of the first and second path f 1 fv c cos θ 1, f 2 fv c cos θ 2 as well as

More information

Revision of Lecture 4

Revision of Lecture 4 Revision of Lecture 4 We have completed studying digital sources from information theory viewpoint We have learnt all fundamental principles for source coding, provided by information theory Practical

More information

Diversity Combining Techniques

Diversity Combining Techniques Diversity Combining Techniques When the required signal is a combination of several plane waves (multipath), the total signal amplitude may experience deep fades (Rayleigh fading), over time or space.

More information

Direct-Sequence Spread-Spectrum

Direct-Sequence Spread-Spectrum Chapter 3 Direct-Sequence Spread-Spectrum In this chapter we consider direct-sequence spread-spectrum systems. Unlike frequency-hopping, a direct-sequence signal occupies the entire bandwidth continuously.

More information

Project: IEEE P Working Group for Wireless Personal Area Networks N

Project: IEEE P Working Group for Wireless Personal Area Networks N Project: IEEE P82.15 Working Group for Wireless Personal Area Networks N (WPANs( WPANs) Title: [LOS office channel model based on TSV model] Date Submitted: [September, 26] Source: [Hirokazu Sawada, Yozo

More information

Channel model. Free space propagation

Channel model. Free space propagation //06 Channel model Free spae rado propagaton Terrestral propagaton - refleton, dffraton, satterng arge-sale fadng Empral models Small-sale fadng Nose and nterferene Wreless Systems 06 Free spae propagaton

More information

BROADBAND MIMO SONAR SYSTEM: A THEORETICAL AND EXPERIMENTAL APPROACH

BROADBAND MIMO SONAR SYSTEM: A THEORETICAL AND EXPERIMENTAL APPROACH BROADBAND MIMO SONAR SYSTM: A THORTICAL AND XPRIMNTAL APPROACH Yan Pailhas a, Yvan Petillot a, Chris Capus a, Keith Brown a a Oceans Systems Lab., School of PS, Heriot Watt University, dinburgh, Scotland,

More information

Passive Sonar Detection Performance Prediction of a Moving Source in an Uncertain Environment

Passive Sonar Detection Performance Prediction of a Moving Source in an Uncertain Environment Acoustical Society of America Meeting Fall 2005 Passive Sonar Detection Performance Prediction of a Moving Source in an Uncertain Environment Vivek Varadarajan and Jeffrey Krolik Duke University Department

More information

Digital Band-pass Modulation PROF. MICHAEL TSAI 2011/11/10

Digital Band-pass Modulation PROF. MICHAEL TSAI 2011/11/10 Digital Band-pass Modulation PROF. MICHAEL TSAI 211/11/1 Band-pass Signal Representation a t g t General form: 2πf c t + φ t g t = a t cos 2πf c t + φ t Envelope Phase Envelope is always non-negative,

More information

A Wideband Space-Time MIMO Channel Simulator Based on the Geometrical One-Ring Model

A Wideband Space-Time MIMO Channel Simulator Based on the Geometrical One-Ring Model A Wideband Space-Time MIMO Channel Simulator Based on the Geometrical One-Ring Model Matthias Pätzold and Bjørn Olav Hogstad Faculty of Engineering and Science Agder University College 4898 Grimstad, Norway

More information

Square Root Raised Cosine Filter

Square Root Raised Cosine Filter Wireless Information Transmission System Lab. Square Root Raised Cosine Filter Institute of Communications Engineering National Sun Yat-sen University Introduction We consider the problem of signal design

More information

CHARACTERIZATION AND ANALYSIS OF DOUBLY DISPERSIVE MIMO CHANNELS. Gerald Matz

CHARACTERIZATION AND ANALYSIS OF DOUBLY DISPERSIVE MIMO CHANNELS. Gerald Matz CHARACTERIZATION AND ANALYSIS OF DOUBLY DISPERSIVE MIMO CHANNELS Gerald Matz Institute of Communications and Radio-Frequency Engineering, Vienna University of Technology Gusshausstrasse 2/389, A-14 Vienna,

More information

SOLUTIONS for Homework #2. 1. The given wave function can be normalized to the total probability equal to 1, ψ(x) = Ne λ x.

SOLUTIONS for Homework #2. 1. The given wave function can be normalized to the total probability equal to 1, ψ(x) = Ne λ x. SOLUTIONS for Homework #. The given wave function can be normalized to the total probability equal to, ψ(x) = Ne λ x. () To get we choose dx ψ(x) = N dx e λx =, () 0 N = λ. (3) This state corresponds to

More information

Review of Doppler Spread The response to exp[2πift] is ĥ(f, t) exp[2πift]. ĥ(f, t) = β j exp[ 2πifτ j (t)] = exp[2πid j t 2πifτ o j ]

Review of Doppler Spread The response to exp[2πift] is ĥ(f, t) exp[2πift]. ĥ(f, t) = β j exp[ 2πifτ j (t)] = exp[2πid j t 2πifτ o j ] Review of Doppler Spread The response to exp[2πift] is ĥ(f, t) exp[2πift]. ĥ(f, t) = β exp[ 2πifτ (t)] = exp[2πid t 2πifτ o ] Define D = max D min D ; The fading at f is ĥ(f, t) = 1 T coh = 2D exp[2πi(d

More information

ETA Observations of Crab Pulsar Giant Pulses

ETA Observations of Crab Pulsar Giant Pulses ETA Observations of Crab Pulsar Giant Pulses John Simonetti,, Dept of Physics, Virginia Tech October 7, 2005 Pulsars Crab Pulsar Crab Giant Pulses Observing Pulses --- Propagation Effects Summary Pulsars

More information

Basic System. Basic System. Sonosite 180. Acuson Sequoia. Echo occurs at t=2z/c where c is approximately 1500 m/s or 1.5 mm/µs

Basic System. Basic System. Sonosite 180. Acuson Sequoia. Echo occurs at t=2z/c where c is approximately 1500 m/s or 1.5 mm/µs Bioengineering 280A Principles of Biomedical Imaging Fall Quarter 2007 Ultrasound Lecture Sonosite 80 From Suetens 2002 Acuson Sequoia Basic System Basic System Echo occurs at t=2/c where c is approximately

More information

Comparative Performance of Three DSSS/Rake Modems Over Mobile UWB Dense Multipath Channels

Comparative Performance of Three DSSS/Rake Modems Over Mobile UWB Dense Multipath Channels MTR 6B5 MITRE TECHNICAL REPORT Comparative Performance of Three DSSS/Rake Modems Over Mobile UWB Dense Multipath Channels June 5 Phillip A. Bello Sponsor: Contract No.: FA871-5-C-1 Dept. No.: E53 Project

More information

NAME: ht () 1 2π. Hj0 ( ) dω Find the value of BW for the system having the following impulse response.

NAME: ht () 1 2π. Hj0 ( ) dω Find the value of BW for the system having the following impulse response. University of California at Berkeley Department of Electrical Engineering and Computer Sciences Professor J. M. Kahn, EECS 120, Fall 1998 Final Examination, Wednesday, December 16, 1998, 5-8 pm NAME: 1.

More information

Modeling Environmental Effects on Directionality in Wireless Networks

Modeling Environmental Effects on Directionality in Wireless Networks Modeling Environmental Effects on Directionality in Wireless Networks Eric Anderson, Caleb Phillips, Douglas Sicker, and Dirk Grunwald eric.anderson@colorado.edu University of Colorado Department of Computer

More information

Estimation of the Capacity of Multipath Infrared Channels

Estimation of the Capacity of Multipath Infrared Channels Estimation of the Capacity of Multipath Infrared Channels Jeffrey B. Carruthers Department of Electrical and Computer Engineering Boston University jbc@bu.edu Sachin Padma Department of Electrical and

More information

A. Dong, N. Garcia, A.M. Haimovich

A. Dong, N. Garcia, A.M. Haimovich A. Dong, N. Garcia, A.M. Haimovich 2 Goal: Localization (geolocation) of unknown RF emitters in multipath environments Challenges: Conventional methods such as TDOA based on line-of-sight (LOS) Non-line-of-sight

More information

Effective Rate Analysis of MISO Systems over α-µ Fading Channels

Effective Rate Analysis of MISO Systems over α-µ Fading Channels Effective Rate Analysis of MISO Systems over α-µ Fading Channels Jiayi Zhang 1,2, Linglong Dai 1, Zhaocheng Wang 1 Derrick Wing Kwan Ng 2,3 and Wolfgang H. Gerstacker 2 1 Tsinghua National Laboratory for

More information

Chapter 2 Underwater Acoustic Channel Models

Chapter 2 Underwater Acoustic Channel Models Chapter 2 Underwater Acoustic Channel Models In this chapter, we introduce two prevailing UWA channel models, namely, the empirical UWA channel model and the statistical time-varying UWA channel model,

More information

Chapter 3 Problem Solutions

Chapter 3 Problem Solutions Chate Poblem Solutions Poblem A Equation (5) gives P P G G log h log h L 4 log d t t t sys Substituting gives P log 6 log log 4 log 875 m B The wavelength is given by 8 The fee-sace ath loss is then 9

More information

Project: IEEE P Working Group for Wireless Personal Area Networks N

Project: IEEE P Working Group for Wireless Personal Area Networks N Project: IEEE P802.15 Working Group for Wireless Personal Area Networks N (WPANs( WPANs) Title: [IBM Measured Data Analysis Revised] Date Submitted: [May 2006] Source: [Zhiguo Lai, University of Massachusetts

More information

Attenuation and dispersion

Attenuation and dispersion Attenuation and dispersion! Mechanisms: Absorption (inelastic); Scattering (elastic).! Mathematical descriptions! Measurement! Frequency dependence! Dispersion, its relation to attenuation Reading: Sheriff

More information

EXPLOITING TRANSMIT CHANNEL SIDE INFORMATION IN MIMO WIRELESS SYSTEMS

EXPLOITING TRANSMIT CHANNEL SIDE INFORMATION IN MIMO WIRELESS SYSTEMS EXPLOITING TRANSMIT CHANNEL SIDE INFORMATION IN MIMO WIRELESS SYSTEMS A DISSERTATION SUBMITTED TO THE DEPARTMENT OF ELECTRICAL ENGINEERING AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN

More information

Lecture 6: Modeling of MIMO Channels Theoretical Foundations of Wireless Communications 1

Lecture 6: Modeling of MIMO Channels Theoretical Foundations of Wireless Communications 1 Fading : Theoretical Foundations of Wireless Communications 1 Thursday, May 3, 2018 9:30-12:00, Conference Room SIP 1 Textbook: D. Tse and P. Viswanath, Fundamentals of Wireless Communication 1 / 23 Overview

More information

How long before I regain my signal?

How long before I regain my signal? How long before I regain my signal? Tingting Lu, Pei Liu and Shivendra S. Panwar Polytechnic School of Engineering New York University Brooklyn, New York Email: tl984@nyu.edu, peiliu@gmail.com, panwar@catt.poly.edu

More information

Outline. 1 Path-Loss, Large-Scale-Fading, Small-Scale Fading. 2 Simplified Models. 3 Multipath Fading Channel

Outline. 1 Path-Loss, Large-Scale-Fading, Small-Scale Fading. 2 Simplified Models. 3 Multipath Fading Channel Outline Digital Communiations Leture 11 Wireless Channels Pierluigi SALVO OSSI Department of Industrial and Information Engineering Seond University of Naples Via oma 29, 81031 Aversa CE), Italy homepage:

More information

Chapter 2 Propagation Modeling

Chapter 2 Propagation Modeling Chapter 2 Propagation Modeling The design of spectrally efficient wireless communication systems requires a thorough understanding of the radio propagation channel. The characteristics of the radio channel

More information

Estimation of Performance Loss Due to Delay in Channel Feedback in MIMO Systems

Estimation of Performance Loss Due to Delay in Channel Feedback in MIMO Systems MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Estimation of Performance Loss Due to Delay in Channel Feedback in MIMO Systems Jianxuan Du Ye Li Daqing Gu Andreas F. Molisch Jinyun Zhang

More information

A VECTOR CHANNEL MODEL WITH STOCHASTIC FADING SIMULATION

A VECTOR CHANNEL MODEL WITH STOCHASTIC FADING SIMULATION A VECTOR CHANNEL MODEL WITH STOCHASTIC FADING SIMULATION Jens Jelitto, Matthias Stege, Michael Löhning, Marcus Bronzel, Gerhard Fettweis Mobile Communications Systems Chair, Dresden University of Technology

More information

Lecture 6: Modeling of MIMO Channels Theoretical Foundations of Wireless Communications 1. Overview. CommTh/EES/KTH

Lecture 6: Modeling of MIMO Channels Theoretical Foundations of Wireless Communications 1. Overview. CommTh/EES/KTH : Theoretical Foundations of Wireless Communications 1 Wednesday, May 11, 2016 9:00-12:00, Conference Room SIP 1 Textbook: D. Tse and P. Viswanath, Fundamentals of Wireless Communication 1 / 1 Overview

More information

Signal Loss. A1 A L[Neper] = ln or L[dB] = 20log 1. Proportional loss of signal amplitude with increasing propagation distance: = α d

Signal Loss. A1 A L[Neper] = ln or L[dB] = 20log 1. Proportional loss of signal amplitude with increasing propagation distance: = α d Part 6 ATTENUATION Signal Loss Loss of signal amplitude: A1 A L[Neper] = ln or L[dB] = 0log 1 A A A 1 is the amplitude without loss A is the amplitude with loss Proportional loss of signal amplitude with

More information

Virtual Array Processing for Active Radar and Sonar Sensing

Virtual Array Processing for Active Radar and Sonar Sensing SCHARF AND PEZESHKI: VIRTUAL ARRAY PROCESSING FOR ACTIVE SENSING Virtual Array Processing for Active Radar and Sonar Sensing Louis L. Scharf and Ali Pezeshki Abstract In this paper, we describe how an

More information

Bioengineering 280A Principles of Biomedical Imaging. Fall Quarter 2006 Ultrasound Lecture 1

Bioengineering 280A Principles of Biomedical Imaging. Fall Quarter 2006 Ultrasound Lecture 1 Bioengineering 280A Principles of Biomedical Imaging Fall Quarter 2006 Ultrasound Lecture 1 From Suetens 2002 1 Basic System Echo occurs at t=2z/c where c is approximately 1500 m/s or 1.5 mm/µs Macovski

More information

Dielectric Waveguides and Optical Fibers. 高錕 Charles Kao

Dielectric Waveguides and Optical Fibers. 高錕 Charles Kao Dielectric Waveguides and Optical Fibers 高錕 Charles Kao 1 Planar Dielectric Slab Waveguide Symmetric Planar Slab Waveguide n 1 area : core, n 2 area : cladding a light ray can undergo TIR at the n 1 /n

More information

Free Space Optical (FSO) Communications. Towards the Speeds of Wireline Networks

Free Space Optical (FSO) Communications. Towards the Speeds of Wireline Networks Free Space Optical (FSO) Communications Towards the Speeds of Wireline Networks FSO Basic Principle Connects using narrow beams two optical wireless transceivers in line-of-sight. Light is transmitted

More information

2.3. Large Scale Channel Modeling Shadowing

2.3. Large Scale Channel Modeling Shadowing c B. Chen 2.3. Large Scale Channel Modeling Shadowing Reading assignment 4.9. Multipath Channel Because of the mobility and complex environment, there are two types of channel variations: small scale variation

More information

The Physics of Doppler Ultrasound. HET408 Medical Imaging

The Physics of Doppler Ultrasound. HET408 Medical Imaging The Physics of Doppler Ultrasound HET408 Medical Imaging 1 The Doppler Principle The basis of Doppler ultrasonography is the fact that reflected/scattered ultrasonic waves from a moving interface will

More information

1. Propagation Mechanisms

1. Propagation Mechanisms Contents: 1. Propagation Mechanisms The main propagation mechanisms Point sources in free-space Complex representation of waves Polarization Electric field pattern Antenna characteristics Free-space propagation

More information

Weibull-Gamma composite distribution: An alternative multipath/shadowing fading model

Weibull-Gamma composite distribution: An alternative multipath/shadowing fading model Weibull-Gamma composite distribution: An alternative multipath/shadowing fading model Petros S. Bithas Institute for Space Applications and Remote Sensing, National Observatory of Athens, Metaxa & Vas.

More information

Physikalische Chemie IV (Magnetische Resonanz) HS Solution Set 2. Hand out: Hand in:

Physikalische Chemie IV (Magnetische Resonanz) HS Solution Set 2. Hand out: Hand in: Solution Set Hand out:.. Hand in:.. Repetition. The magnetization moves adiabatically during the application of an r.f. pulse if it is always aligned along the effective field axis. This behaviour is observed

More information

Improved Detected Data Processing for Decision-Directed Tracking of MIMO Channels

Improved Detected Data Processing for Decision-Directed Tracking of MIMO Channels Improved Detected Data Processing for Decision-Directed Tracking of MIMO Channels Emna Eitel and Joachim Speidel Institute of Telecommunications, University of Stuttgart, Germany Abstract This paper addresses

More information

Lecture 9. PMTs and Laser Noise. Lecture 9. Photon Counting. Photomultiplier Tubes (PMTs) Laser Phase Noise. Relative Intensity

Lecture 9. PMTs and Laser Noise. Lecture 9. Photon Counting. Photomultiplier Tubes (PMTs) Laser Phase Noise. Relative Intensity s and Laser Phase Phase Density ECE 185 Lasers and Modulators Lab - Spring 2018 1 Detectors Continuous Output Internal Photoelectron Flux Thermal Filtered External Current w(t) Sensor i(t) External System

More information

Pulsar Scintillation & Secondary Spectra The view from the Orthodoxy. Jean-Pierre Macquart

Pulsar Scintillation & Secondary Spectra The view from the Orthodoxy. Jean-Pierre Macquart Pulsar Scintillation & Secondary Spectra The view from the Orthodoxy Jean-Pierre Macquart Interstellar Scintillation: Executive Summary Radiation is scattered between a source S and observer O Inhomogeneous

More information

An Analysis of Errors in RFID SAW-Tag Systems with Pulse Position Coding

An Analysis of Errors in RFID SAW-Tag Systems with Pulse Position Coding An Analysis of Errors in RFID SAW-Tag Systems with Pulse Position Coding YURIY S. SHMALIY, GUSTAVO CERDA-VILLAFAÑA, OSCAR IBARRA-MANZANO Guanajuato University, Department of Electronics Salamanca, 36855

More information

Methods for Path loss Prediction

Methods for Path loss Prediction School of Mathematics and Systems Engineering Reports from MSI - Rapporter från MSI Methods for Path loss Prediction Cem Akkaşlı October 9 MSI Report 967 Växjö University ISSN 65-647 SE-35 95 VÄXJÖ ISRN

More information

PRACTICE FINAL EXAM SOLUTION Jing Liang 12/06/2006

PRACTICE FINAL EXAM SOLUTION Jing Liang 12/06/2006 PRACICE FIAL EXAM SOLUIO Jing Liang /6/6 PROBLEM (a) (b) (c) (d) (e) he propagation ignal experience power variation due to the contructive and detructive addition of multi-path ignal component, and therefore

More information

Searching for Ultra-High Energy νs with ANITA

Searching for Ultra-High Energy νs with ANITA Searching for Ultra-High Energy νs with ANITA Ben Strutt on behalf of the ANITA collaboration Department of Physics and Astronomy University of California, Los Angeles August 28, 2018 Ben Strutt (UCLA)

More information

EE303: Communication Systems

EE303: Communication Systems EE303: Communication Systems Professor A. Manikas Chair of Communications and Array Processing Imperial College London Introductory Concepts Prof. A. Manikas (Imperial College) EE303: Introductory Concepts

More information

Mobile Communications (KECE425) Lecture Note Prof. Young-Chai Ko

Mobile Communications (KECE425) Lecture Note Prof. Young-Chai Ko Mobile Communications (KECE425) Lecture Note 20 5-19-2014 Prof Young-Chai Ko Summary Complexity issues of diversity systems ADC and Nyquist sampling theorem Transmit diversity Channel is known at the transmitter

More information

EE401: Advanced Communication Theory

EE401: Advanced Communication Theory EE401: Advanced Communication Theory Professor A. Manikas Chair of Communications and Array Processing Imperial College London Multi-Antenna Wireless Communications Part-B: SIMO, MISO and MIMO Prof. A.

More information

Parametric Characterization and Estimation of Dispersive Multi-Path Components with SAGE in Radio Propagation Channel

Parametric Characterization and Estimation of Dispersive Multi-Path Components with SAGE in Radio Propagation Channel Parametric Characterization and Estimation of Dispersive Multi-Path Components with SAGE in Radio Propagation Channel SIGNAL AND INFORMATION PROCESSING IN COMMUNICATIONS SYSTEMS DEPARTMENT OF COMMUNICATION

More information

CHAPTER 12 PROBLEMS AND EXERCISES

CHAPTER 12 PROBLEMS AND EXERCISES CHAPTER 1 PROBLEMS AND EXERCISES Problem 1: Consider a PWAS transducer of length l 7 mm, width b 1.65 mm, thickness t 0. mm, and material properties as given in Table 1.. The PWAS is bonded at one end

More information

Compensating for attenuation by inverse Q filtering. Carlos A. Montaña Dr. Gary F. Margrave

Compensating for attenuation by inverse Q filtering. Carlos A. Montaña Dr. Gary F. Margrave Compensating for attenuation by inverse Q filtering Carlos A. Montaña Dr. Gary F. Margrave Motivation Assess and compare the different methods of applying inverse Q filter Use Q filter as a reference to

More information

Impact of channel statistics and correlation on underwater acoustic communication systems

Impact of channel statistics and correlation on underwater acoustic communication systems Scholars' Mine Masters Theses Student Research & Creative Works Fall 211 Impact of channel statistics and correlation on underwater acoustic communication systems Jesse Scott Cross Follow this and additional

More information

, the time-correlation function

, the time-correlation function 270 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 47, NO. 1, FEBRUARY 1998 On the Correlation Scattering Functions of the WSSUS Channel for Mobile Communications John S. Sadowsky, Member, IEEE, Venceslav

More information

A Brief Introduction to Medical Imaging. Outline

A Brief Introduction to Medical Imaging. Outline A Brief Introduction to Medical Imaging Outline General Goals Linear Imaging Systems An Example, The Pin Hole Camera Radiations and Their Interactions with Matter Coherent vs. Incoherent Imaging Length

More information

Rate-Optimum Beamforming Transmission in MIMO Rician Fading Channels

Rate-Optimum Beamforming Transmission in MIMO Rician Fading Channels Rate-Optimum Beamforming Transmission in MIMO Rician Fading Channels Dimitrios E. Kontaxis National and Kapodistrian University of Athens Department of Informatics and telecommunications Abstract In this

More information