Line Codes and Pulse Shaping Review. Intersymbol interference (ISI) Pulse shaping to reduce ISI Embracing ISI

Similar documents
Digital Baseband Systems. Reference: Digital Communications John G. Proakis

EE5713 : Advanced Digital Communications

Lecture 5b: Line Codes

Weiyao Lin. Shanghai Jiao Tong University. Chapter 5: Digital Transmission through Baseband slchannels Textbook: Ch

Principles of Communications Lecture 8: Baseband Communication Systems. Chih-Wei Liu 劉志尉 National Chiao Tung University

Digital Communications

Summary: ISI. No ISI condition in time. II Nyquist theorem. Ideal low pass filter. Raised cosine filters. TX filters

Pulse Shaping and ISI (Proakis: chapter 10.1, 10.3) EEE3012 Spring 2018

Signal Design for Band-Limited Channels

Example: Bipolar NRZ (non-return-to-zero) signaling

Square Root Raised Cosine Filter

that efficiently utilizes the total available channel bandwidth W.

EE4061 Communication Systems

II - Baseband pulse transmission

DIGITAL COMMUNICATIONS. IAGlover and P M Grant. Prentice Hall 1997 PROBLEM SOLUTIONS CHAPTER 6

A Family of Nyquist Filters Based on Generalized Raised-Cosine Spectra

Es e j4φ +4N n. 16 KE s /N 0. σ 2ˆφ4 1 γ s. p(φ e )= exp 1 ( 2πσ φ b cos N 2 φ e 0

Principles of Communications

References Ideal Nyquist Channel and Raised Cosine Spectrum Chapter 4.5, 4.11, S. Haykin, Communication Systems, Wiley.

EE 661: Modulation Theory Solutions to Homework 6

Data Detection for Controlled ISI. h(nt) = 1 for n=0,1 and zero otherwise.

3.2 Complex Sinusoids and Frequency Response of LTI Systems

Lecture 28 Continuous-Time Fourier Transform 2

ELEN E4810: Digital Signal Processing Topic 11: Continuous Signals. 1. Sampling and Reconstruction 2. Quantization

Chapter 5 Frequency Domain Analysis of Systems

FROM ANALOGUE TO DIGITAL

Chapter 12 Variable Phase Interpolation

Sample Problems for the 9th Quiz

Chapter 5 Frequency Domain Analysis of Systems

EE303: Communication Systems

RADIO SYSTEMS ETIN15. Lecture no: Equalization. Ove Edfors, Department of Electrical and Information Technology

ESS Dirac Comb and Flavors of Fourier Transforms

Systems & Signals 315

LOPE3202: Communication Systems 10/18/2017 2

Homework 4. May An LTI system has an input, x(t) and output y(t) related through the equation y(t) = t e (t t ) x(t 2)dt

2A1H Time-Frequency Analysis II

Lecture 27 Frequency Response 2

ECS 332: Principles of Communications 2012/1. HW 4 Due: Sep 7

Capacity Penalty due to Ideal Zero-Forcing Decision-Feedback Equalization

This examination consists of 11 pages. Please check that you have a complete copy. Time: 2.5 hrs INSTRUCTIONS

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

Analog Electronics 2 ICS905

Communication Theory Summary of Important Definitions and Results

EE 3054: Signals, Systems, and Transforms Summer It is observed of some continuous-time LTI system that the input signal.

TSKS01 Digital Communication Lecture 1

3.9 Diversity Equalization Multiple Received Signals and the RAKE Infinite-length MMSE Equalization Structures

EE401: Advanced Communication Theory

Signals and Systems: Part 2

Contents Equalization 148

a) Find the compact (i.e. smallest) basis set required to ensure sufficient statistics.

2A1H Time-Frequency Analysis II Bugs/queries to HT 2011 For hints and answers visit dwm/courses/2tf

IB Paper 6: Signal and Data Analysis

2 Frequency-Domain Analysis

EE4601 Communication Systems

2. SPECTRAL ANALYSIS APPLIED TO STOCHASTIC PROCESSES

EE538 Final Exam Fall :20 pm -5:20 pm PHYS 223 Dec. 17, Cover Sheet

LINEAR SYSTEMS. J. Elder PSYC 6256 Principles of Neural Coding

Ver 3808 E1.10 Fourier Series and Transforms (2014) E1.10 Fourier Series and Transforms. Problem Sheet 1 (Lecture 1)

EE4512 Analog and Digital Communications Chapter 4. Chapter 4 Receiver Design

Carrier frequency estimation. ELEC-E5410 Signal processing for communications

SEISMIC WAVE PROPAGATION. Lecture 2: Fourier Analysis

ECG782: Multidimensional Digital Signal Processing

ECE 301 Fall 2011 Division 1 Homework 10 Solutions. { 1, for 0.5 t 0.5 x(t) = 0, for 0.5 < t 1

Time and Spatial Series and Transforms

Optimized Impulses for Multicarrier Offset-QAM

EC Signals and Systems

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

EEL3135: Homework #3 Solutions

Topic 3: Fourier Series (FS)

Bridge between continuous time and discrete time signals

Problem Value

Data Encoding. Part II Digital to Digital Conversion. Surasak Sanguanpong

6.003: Signals and Systems. Sampling and Quantization

E2.5 Signals & Linear Systems. Tutorial Sheet 1 Introduction to Signals & Systems (Lectures 1 & 2)

Conversion from Linear to Circular Polarization and Stokes Parameters in FPGA. Koyel Das, Alan Roy, Gino Tuccari, Reinhard Keller

8 PAM BER/SER Monte Carlo Simulation

Problem Value

SPEECH ANALYSIS AND SYNTHESIS

Principles of Communications

Revision of Lecture 4

Chapter 10. Timing Recovery. March 12, 2008

NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY DEPARTMENT OF ELECTRONICS AND TELECOMMUNICATIONS

Chapter 1 Fundamental Concepts

Timbral, Scale, Pitch modifications

Fig. 2. Block Diagram of a DCS

Line Spectra and their Applications

Signals and Systems. Lecture 14 DR TANIA STATHAKI READER (ASSOCIATE PROFESSOR) IN SIGNAL PROCESSING IMPERIAL COLLEGE LONDON

Sensors. Chapter Signal Conditioning

Physical Layer and Coding

2.161 Signal Processing: Continuous and Discrete Fall 2008

Chapter 1 Fundamental Concepts

This examination consists of 10 pages. Please check that you have a complete copy. Time: 2.5 hrs INSTRUCTIONS

ELEN 4810 Midterm Exam

so mathematically we can say that x d [n] is a discrete-time signal. The output of the DT system is also discrete, denoted by y d [n].

Imperfect Sampling Moments and Average SINR

Chaos from linear systems: implications for communicating with chaos, and the nature of determinism and randomness

2.1 Basic Concepts Basic operations on signals Classication of signals

VID3: Sampling and Quantization

Review: Continuous Fourier Transform

Fourier Transform for Continuous Functions

Transcription:

Line Codes and Pulse Shaping Review Line codes Pulse width and polarity Power spectral density Intersymbol interference (ISI) Pulse shaping to reduce ISI Embracing ISI

Line Code Examples (review) on-off (RZ) polar (RZ) bipolar/ami (RZ) on-off (NRZ) polar (NRZ)

Line Code Desiderata (review) Features we want: Minimum bandwidth (NRZ) Easy clock recovery (RZ) Frequently, no DC value (bipolar pulses)

PSD of Line Codes The PSD of a line code depends on the shapes of the pulses that correspond to digital values. Assume PAM. The transmitted signal is the sum of weighted, shifted pulses. y(t) = a k p(t kt b ) k= where T b is spacing between pulses. (Pulse may be wider than T b.)

PSD of Line Codes (cont.) PSD depends on pulse shape, rate, and digital values {a k }. We can simplify analysis by representing {a k } as impulse train. PSD of y(t) is S y (f) = P(f) 2 S x (f). P(f) depends only on the pulse, independent of digital values or rate. S x (f) depends only on the bit sequence, and can be altered by the signaling scheme.

Power Spectral Density of Line Codes (review) In general, the PSD of a line code is S y (f) = P(f) 2 S x (f) where S x (f) = F {R x (t)} = T b so ( S y (f) = P(f) 2 n= T b n= R n e jn2πft b R n e jn2πft b In many cases, only R, or just a few terms are not equal to zero. We wantto limit the bandwidth of the transmitted signal, so we can modify P(f), or modify x(t), or both )

Modifying x(t) Polar signaling. Transmit as +p(t) and as p(t), Only R, and S y (f) = P(f) 2 R = P(f) 2 T b T b PSD of polar signaling depends only on spectrum of p(t).

Modifying x(t) (cont.) Bipolar signaling: transmit s as alternating ±p(t), and s as. Bipolar signaling for full-width (NRZ) pulses. S y (f) = P(f) 2 2T b ( cos2πt b f) = P(f) 2 T b sin 2 (πt b f) For half-width (RZ) pulses: S y (f) = P(f) 2 T b 4 sinc 2( πft n b ) sin 2 (πt b f) 2 PSD depends on P(f), but decays more rapidly due to sin 2 terms.

Modifying p(t) Split phase or Manchester encoding: p(t) t t PSD is ( S y (f) = P(f) 2 T b n= R n e jn2πft b ) = P(f) 2 T b

Comparison of PSD s

PSD of Polar Signaling (Matlab Experiment).5 N =.5 N =.5.5 3 2 2 3 N =.5.5 3 2 2 3 N =.5.5 3 2 2 3 3 2 2 3 N =.5.5 3 2 2 3 N =.5.5 3 2 2 3

Intersymbol Interference (ISI) Pulses transmitted over a physical channel (linear time-invariant system) are distorted smoothed and stretched. Example: impulse response of an RC circuit is h(t) = e t/rc u(t). For RC =.5, the pulse response is t < h(t) Π(t 2 ) = e 2t < t < ( e 2 )e 2t t >.5 2 3 4 5

ISI Example: h(t) = RC e t/rc RC =.5.5.5.5.5 2 4 6 8 2 4 6 8 RC =. RC = 2..5.5.5.5 2 4 6 8 2 4 6 8

Reducing ISI: Pulse Shaping A time-limited pulse cannot be bandlimited Linear channel distortion results in spread out, overlapping pulses Nyquist introduced three criteria for dealing with ISI. The first criterion was that each pulse is zero at the sampling time of other pulses. { t = p(t) = t = ±kt b, k = ±,±2,... Harry Nyquist, Certain topics in telegraph transmission theory, Trans. AIEE, Apr. 928

Pulse Shaping: sinc Pulse Let R b = /T b. The sinc pulse sinc(πr b t) satisfies Nyquist s first crierion for zero ISI: { t = sinc(πr b t) = t = ±kt b, k = ±,±2,... This pulse is bandlimited. Its Fourier transform is P(f) = ( f ) Π R b R b Unfortunately, this pulse has infinite width and decays slowly.

Nyquist Pulse Nyquist increased the width of the spectrum in order to make the pulse fall off more rapidly. The Nyquist pulse has spectrum width 2 (+r)r b, where < r <. If we sample the pulse p(t) at rate R b = /T b, then p(t) = p(t)iii Tb (t) = p(t)δ(t) = δ(t). The Fourier transform of the sampled signal is P(f) = = P(f) R b III Rb (f) = P(f kr b ) k=

Nyquist Pulse (cont.) Since we are sampling below the Nyquist rate 2R b, the shifted transforms overlap. Nyquist s criterion requires pulses whose overlaps add to for all f. For parameter r with < r <, the resulting pulse has bandwidth B r = 2 (R b +rr b ) The parameter r is called roll-off factor and controls how sharply the pulse spectrum declines above 2 R b.

Nyquist Pulse (cont.) Many pulse spectra satisfy this condition. e.g., trapezoid: f < 2 ( r)r b P(f) = f ( r)r b 2R b 2 ( r)r b < f < 2 (+r)r b f > 2 ( r)r b A trapezoid is the difference of two triangles. Thus the pulse with trapezoidal Fourier transform is the difference of two sinc 2 pulses. Example: for r = 2, so the pulse is This pulse falls off as /t 2. ( P(f) = 3 f ) 2 Λ 3 2 R b ( f ) 2 Λ 2 R b p(t) = 9 4 sinc2 ( 3 2 R bt) 4 sinc2 ( 2 R bt)

Nyquist Pulse (cont.) Nyquist chose a pulse with a vestigial raised cosine transform. This transform is smoother than trapezoid, so pulse decays more rapidly. The Nyquist pulse is parametrized by r. Let f x = rr b /2.

Nyquist Pulse (cont.) Nyquist pulse spectrum is raised cosine pulse with flat porch. ( ( )) f < 2 R b f x f 2 P(f) = 2 sinπ R b f 2f 2 R b < f x x f > 2 R b +f x The transform P(f) is differentiable, so the pulse decays as /t 2.

Nyquist Pulse (cont.) Special case of Nyquist pulse is r = : full-cosine roll-off. P(f) = 2 (+cosπt bf)π(f/r b ) = cos 2 ( 2 πt bf)π( 2 T bf) This transform P(f) has a second derivative so the pulse decays as /t 3. p(t) = R b cosπr b t 4R 2 b t2 sinc(πr bt) = sin(2πr bt) 2πt( 4R 2 b t2 )

Controlled ISI (Partial Response Signaling) The second Nyquist approach embraced ISI: if we know the adjacent bits then interference is known! We can achieve lower bandwidth by using an even wider pulse. ISI can be canceled using knowledge of the pulse shape. In this case p(t) is equal to for t = and t = T b. The signal at time T b depends on both x() and x(t b ). If both are, the output y(t) = 2 if one is, and the other is -, the output is if both are -, the output is -2. Given a starting value for a k, where x(t) = k a kδ(t kt b ), we can subtract the ISI term by term and recover the bit sequence {a k }.

Partial Response Signaling (cont.) The ideal duobinary pulse is p(t) = The Fourier transform of p(t) is P(f) = 2 ( πf cos R b sinπr b t πr b t( R b t) R b ) ( ) f Π e jπf/r b R b The spectrum is confined to the theoretical minimum of R b /2.

Zero-ISI, Duobinary, Modified Duobinary Pulses Suppose p a (t) satisfies Nyquist s first criterion (zero ISI). p a (t) 2T b T b T b 2T b t Then p b (t) = p a (t)+p a (t T b ) is a duobinary pulse with controlled ISI. p b (t)=p a (t)+p a (t T b ) 2T b T b T b 2T b t

Zero-ISI, Duobinary, Modified Duobinary Pulses (cont.) By shift theorem, P b (f) = P a (+e j2πt bf ) Since P b (R b /2) =, most (or all) of the pulse energy is below R b /2. We can eliminate unwanted DC component using modified duobinary, where p c ( T b ) =, p c (T b ) =, and p c (nt B ) = for other integers n. p c (t) = p a (t+t b ) p a (t T b ) = P c (f) = 2jP a (f)sin2πt b f The transform of p c (t) has nulls at and ±R b /2. p c (t)=p a (t+t b ) p a (t T b ) 2T b T b T b 2T b

Zero-ISI, Duobinary, Modified Duobinary Pulses (cont.) Zero ISI.5 5 4 3 2 2 3 4.5 Duobinary.5 5 4 3 2 2 3 4 Modified Duobinary.5.5 5 4 3 2 2 3 4

Zero-ISI, Duobinary, Modified Duobinary Pulses (cont.) 5 5 2.5.5.5.5 2 2 5 5 2.5.5.5.5 2 2 5 5 2.5.5.5.5 2

Partial Response Signaling Detection Suppose that sequence is transmitted (first bit is startup digit). Digit x k Bipolar amplitude - - - - Combined amplitude -2 2 Decode sequence Partial response signaling is susceptible to error propagation. If a nonzero value is misdetected, zeros will be misdetected until the next nonzero value. This can be eliminated by differential encoding, where the input stream is preprocessed, so that a receiver can directly read out the data bits.