PCM Reference Chapter 12.1, Communication Systems, Carlson. PCM.1

Similar documents
Digital Signal Processing

Pulse-Code Modulation (PCM) :

Chapter 10 Applications in Communications

7.1 Sampling and Reconstruction

Communication Engineering Prof. Surendra Prasad Department of Electrical Engineering Indian Institute of Technology, Delhi

E303: Communication Systems

Random Signal Transformations and Quantization

VID3: Sampling and Quantization

Lecture 5b: Line Codes

Example: sending one bit of information across noisy channel. Effects of the noise: flip the bit with probability p.

Finite Word Length Effects and Quantisation Noise. Professors A G Constantinides & L R Arnaut

One Lesson of Information Theory

Principles of Communications

Quantization 2.1 QUANTIZATION AND THE SOURCE ENCODER

ELECTRONICS & COMMUNICATIONS DIGITAL COMMUNICATIONS

16.36 Communication Systems Engineering

Multimedia Communications. Scalar Quantization

Lecture 12. Block Diagram

Scalar and Vector Quantization. National Chiao Tung University Chun-Jen Tsai 11/06/2014

Assume that the follow string of bits constitutes one of the segments we which to transmit.

Quantisation. Uniform Quantisation. Tcom 370: Principles of Data Communications University of Pennsylvania. Handout 5 c Santosh S.

Digital Signal Processing 2/ Advanced Digital Signal Processing Lecture 3, SNR, non-linear Quantisation Gerald Schuller, TU Ilmenau

Channel capacity. Outline : 1. Source entropy 2. Discrete memoryless channel 3. Mutual information 4. Channel capacity 5.

4 An Introduction to Channel Coding and Decoding over BSC

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 3. Quantization and Coding. Version 2, ECE IIT, Kharagpur

encoding without prediction) (Server) Quantization: Initial Data 0, 1, 2, Quantized Data 0, 1, 2, 3, 4, 8, 16, 32, 64, 128, 256

Digital communication system. Shannon s separation principle

9 THEORY OF CODES. 9.0 Introduction. 9.1 Noise

channel of communication noise Each codeword has length 2, and all digits are either 0 or 1. Such codes are called Binary Codes.

Physical Layer and Coding

Digital Communications III (ECE 154C) Introduction to Coding and Information Theory

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

CS578- Speech Signal Processing

EE 5345 Biomedical Instrumentation Lecture 12: slides

LOPE3202: Communication Systems 10/18/2017 2

Figure 1.1 (a) Model of a communication system, and (b) signal processing functions.

Modern Digital Communication Techniques Prof. Suvra Sekhar Das G. S. Sanyal School of Telecommunication Indian Institute of Technology, Kharagpur

Multimedia Networking ECE 599

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

ECE 564/645 - Digital Communications, Spring 2018 Homework #2 Due: March 19 (In Lecture)

Time-domain representations

Analysis of methods for speech signals quantization

Ma/CS 6b Class 24: Error Correcting Codes

STATISTICS FOR EFFICIENT LINEAR AND NON-LINEAR PICTURE ENCODING

MARKOV CHAINS A finite state Markov chain is a sequence of discrete cv s from a finite alphabet where is a pmf on and for

ETSF15 Analog/Digital. Stefan Höst

EE 121: Introduction to Digital Communication Systems. 1. Consider the following discrete-time communication system. There are two equallly likely

Chapter 9 Fundamental Limits in Information Theory

Channel Coding and Interleaving

CSCI 2570 Introduction to Nanocomputing

Information Theory - Entropy. Figure 3

E4702 HW#4-5 solutions by Anmo Kim

Image Compression using DPCM with LMS Algorithm

ANALYSIS OF A PARTIAL DECORRELATOR IN A MULTI-CELL DS/CDMA SYSTEM

FACULTY OF ENGINEERING MULTIMEDIA UNIVERSITY LAB SHEET

EE-597 Notes Quantization

Source Coding. Scalar Quantization

L. Yaroslavsky. Fundamentals of Digital Image Processing. Course

An Introduction to (Network) Coding Theory


Lecture 2: Introduction to Audio, Video & Image Coding Techniques (I) -- Fundaments

Compression methods: the 1 st generation

DESIGN AND IMPLEMENTATION OF ENCODERS AND DECODERS. To design and implement encoders and decoders using logic gates.

Shannon's Theory of Communication

EE 229B ERROR CONTROL CODING Spring 2005

Chapter 7: Channel coding:convolutional codes

Revision of Lecture 4

Introduction to digital systems. Juan P Bello

ITCT Lecture IV.3: Markov Processes and Sources with Memory

Lecture 2: Introduction to Audio, Video & Image Coding Techniques (I) -- Fundaments. Tutorial 1. Acknowledgement and References for lectures 1 to 5

Mapper & De-Mapper System Document

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

Image Dependent Log-likelihood Ratio Allocation for Repeat Accumulate Code based Decoding in Data Hiding Channels

SPEECH ANALYSIS AND SYNTHESIS

Basic information theory

Ma/CS 6b Class 25: Error Correcting Codes 2

EE 230 Lecture 40. Data Converters. Amplitude Quantization. Quantization Noise

IOSR Journal of Mathematics (IOSR-JM) e-issn: , p-issn: x. Volume 9, Issue 2 (Nov. Dec. 2013), PP

Gaussian source Assumptions d = (x-y) 2, given D, find lower bound of I(X;Y)

Direct-Sequence Spread-Spectrum

CODING SAMPLE DIFFERENCES ATTEMPT 1: NAIVE DIFFERENTIAL CODING

Basic Principles of Video Coding

Coding theory: Applications

Fault Tolerance Technique in Huffman Coding applies to Baseline JPEG

CSE468 Information Conflict

Chapter 3. Quantization. 3.1 Scalar Quantizers

10GBASE-KR Transmitter Compliance Methodology Proposal. Robert Brink Agere Systems May 13, 2005

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

ECE Information theory Final

Various signal sampling and reconstruction methods

Optimum Soft Decision Decoding of Linear Block Codes

Coding for Discrete Source

Lecture 7. Union bound for reducing M-ary to binary hypothesis testing

MAHALAKSHMI ENGINEERING COLLEGE-TRICHY QUESTION BANK UNIT V PART-A. 1. What is binary symmetric channel (AUC DEC 2006)

8 PAM BER/SER Monte Carlo Simulation

Logic. Combinational. inputs. outputs. the result. system can

Analog to Digital Conversion

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

Chapter 12 Variable Phase Interpolation

Transcription:

PCM Reference Chapter 1.1, Communication Systems, Carlson. PCM.1

Pulse-code modulation (PCM) Pulse modulations use discrete time samples of analog signals the transmission is composed of analog information sent at discrete times. The variation of pulse amplitude or pulse timing is allowed to vary continuously over all values. PCM the analog signal is quantized into a number discrete levels. PCM.

Example: Suppose that we wish to quantize a signal using eight discrete levels. At each sample time we must decide which of these eight levels is best approximation to the signal. We choose the closest value and use this value until the next sample time. Quantization noise t 8 7 6 4 5 3 1 0 Digits PCM.3

This process of quantization introduces some fluctuations about the true value; these fluctuation can be regarded as noise and called quantization noise. PCM.4

The next step is to assign a digit to each level. This is called digitization of the waveform. The digits are expressed in a coded form. The most common code used is a binary code. Digits 0 1 3 4 5 6 7 Binary code 000 001 010 011 100 101 110 111 PCM.5

Quantization noise Consider an input f (t) of continuous amplitude in the range ( f max, fmax ). Assuming using a uniform quantizer, the step-size of the quantizer is = f max / L where L is the total number of representation levels. f max PCM.6

For a uniform quantizer, the quantization error q is bounded by. / q / If the step-size is sufficient small, it is reasonable to assume that the quantization error is a uniformly distributed random variable, and the interfering effect of the quantization noise on the quantizer input is similar to that of thermal noise. We may express the probability density function of the quantization error as: p( q) = 1 0 < q < otherwise PCM.7

Therefore, the average power of the quantization noise is n q = = = / / 1 1 q / / p( q) dq q dq PCM.8

Example: Consider a full-load sinusoidal modulating signal of amplitude A, which utilizes all the representation levels provided. The average signal power is P = A The total range of the quantizer is A because the modulating signal swings between -A and A. Therefore, if it is a 4-bit quantizer, A = 4 = A 8 PCM.9

and the quantization noise is n = = 1 A 768 The S/N ratio is ( A / ) ( A / 768) = 384 = 5.8dB PCM.10

Nonuniform quantizing Uniform quantizaiton The S/N ratio is low for low level signal In telephone system, nonuniform quantizers are used Increase the S/N ratio for low level signal Example: Smaller range f max PCM.11

Nonuniform quantizing Nonuniform quantizer Equivalent to passing the baseband signal through a compressor and then applying the compressed signal to a uniform quantizer. output Compressor for the uniform quantizer input For uniform quantizer PCM.1

Nonuniform quantizing Compressor law µ-law log(1 + µ m v = log(1 + µ ) ) v 1 µ = 5 µ = 0 0 1 m PCM.13

Nonuniform quantizing Compressor law A-law v = A m 1+ log A 1+ log( A m ) 1+ log A 1 0 m A 1 m 1 A At the receiver An expander is used to restore the signal. The combination of a compressor and an expander is called a compander PCM.14

Advantages of PCM (digital communications) In long-distance communications, PCM signals can be completely regenerated at intermediate repeater stations because all the information is contained in the code. Essentially a noise-free signal is retransmitted at each repeater. The effects of noise do not accumulate and one need to concerned only about the effects of transmission noise between adjacent repeaters. Modulating and demodulating circuitry is all digital, thus affording high reliability and stability, and is readily adapted to integrated-circuit logic design. PCM.15

Signals may be stored and time scaled efficiently. 100Mbps link music 100 kbps source for 100 seconds = 10 Mbps Transmit time=0.1s Playback time = 10s Efficient codes can be utilized to reduce unnecessary repetition (redundancy) in messages. (source coding) 1 01 001 0001 00001 000001 0000001 0000000 Source Encoder 000 001 010 011 100 101 110 111 PCM.16

Appropriate coding can reduce the effects of noise and interference. (channel coding) Example: 1 encoded as 111 and 0 encoded as 000 101 Channel 111000111 Encoder Noisy Channel 101 The errors are removed Receiver 110010111 Errors PCM.17