CHAPTER 1 Signals and Spectra School of Computer

CHAPTER 1 Signals and Spectra School of Computer and Communication Engineering, Amir Razif Arief b. Jamil Abdullah EKT 431: Digital Communications

Coursework Contribution Coursework: Lab: 30% l Project 1 l Project 2 l Project 3 l Assignments, Attendance & Quizzes: 10% l Assignments; minimum 4. l Attendance; subjected to university regulation. l Quizzes; minimum 8. l Test: 10 % l Two tests. Exam: 50% l Lecturer: Amir Razif Arief b. Jamil Abdullah l Office: Grnd Floor, House #8 A, KKF, Kuala Perlis l E-mail: amirrazif@unimap. edu. my l Office tel#: 04 -9854251 @ 019 4659277 l HP#: Upon Request l l Teaching Engineer: Mohd Fairuz b. Mohd Fadzil l Office: House #1, KKF, Kuala Perlis 2006 -01 -24 Lecture 1 2

Practical information l Course material l Course text book: l “Digital Communications: Fundamentals and Applications” by Bernard Sklar, Prentice Hall, 2005, ISBN: 0 -13 -084788 -7 l 2006 -01 -24 Reference Books; l “Introduction to Digital Communications”, by Pursley M. B, IE Pearson Hall 2005 l “Information Transmission, Modulation and Noise ”, by M. Schwartz, Mc Graw Hill 2005 l “Digital Communications”, by Proakis, John G. International Eddition, Mc Graw. Hill 3 rd Ed. 1995 Lecture 1 3

Outcome • To understand use various terminologies in Digital Communications. • To be able to explain the differences between analog and digital communications. • To describe the basic building blocks of a digital communication system and the performance objectives for good communication. • To analyze the signals transmission via channel. • To study the base band data transmission, digital modulation and spread spectrum communications. • To explore the basic principles of telephony system.

Today, we are going to talk about: What are the features of a digital communication system? l Why “digital” instead of “analog”? l What do we need to know before taking off toward designing a DCS? l Classification of signals l Random processes l Autocorrelation l Power and energy spectral densities l Noise in communication systems l Signal transmission through linear systems l Bandwidth of a signal l 2006 -01 -24 Lecture 1 5

Introduction Deal with transformation of information; voice, video or data, over a channel that consists of wire, waveguide and space. þ Digital communication systems are becoming attractive because of the growing demand for data communication and digital transmission offers data processing options. þ

Revision • Signals & Systems Fourier transform, signal analysis • Communications Systems PAM, PWM, PPM, PCM, ASK, FSK, PSK, line coding • Communication Network LAN, wireless network, circuit switching, multiple access

Scope of the course • Communications is a process by which information is exchanged between individuals through a common system of symbols, signs, or behaviour. • Communication systems are reliable, economical and efficient means of communications • Public switched telephone network (PSTN), mobile telephone communication (GSM, 3 G, . . . ), broadcast radio or television, navigation systems, . . . • The course is aiming at introducing fundamental issues in designing a (digital) communication system 2006 -01 -24 Lecture 1 8

Scope of the course. . . • Example of a (digital) communication system: • Cellular wireless communication systems BS Base Station (BS) UE UE UE User Equipment (UE) 2006 -01 -24 Lecture 1 9

Scope of the course … l Learning fundamental issues in designing a digital communication system (DCS): l Utilized techniques l Formatting and source coding l Modulation (Basebandpass signaling) l Channel coding l Equalization l Synchronization l. . l Design goals l Trade-off between various parameters 2006 -01 -24 Lecture 1 10

Block Diagram of DCS General structure of a communication system Noise Source SOURCE Info. Received Transmitted Received info. signal Transmitter Receiver Channel User Transmitter Formatter Source encoder Channel encoder Modulator Receiver Formatter 2006 -01 -24 Source decoder Channel decoder Lecture 1 Demodulator 11

Block Diagram and Transformation • The upper block are the signal transformation from source to transmitter (XMT); format, source encode, encrypt, channel encode, multiplex, pulse modulated, band pass modulated, frequency spread and multiple excess. • The lower block are the signal transformation from receiver (RCV) to sink; reversing the signal processing of the upper block. • For wireless communication; (i) transmitter consist of frequency up-conversion stage to a radio frequency, high power amplifier and antenna. (ii) receiver consist of antenna and low noise amplifier (LNA).

Cont’d… • Signal processing steps; (i) input information source is convert to binary digits ( bits) (ii) bits grouped to form message symbol (mi ) (iii) system using channel coding; sequence of message symbol transform to sequence of channel symbol (ui ) or bit stream. • The key signal processing blocks of DCS are formatting, modulation, demodulation/detection and synchronization. (1) Formatting: transform source information into bits. Information is inform of bit stream up to pulse-modulation block. (2) Modulation: process of converting the channel symbol to waveform compatible to transmission channel. - binary representation baseband waveform

Cont’d… (3) Pulse Modulation: - transform binary representation to baseband waveform. Include filtering to minimize the binary waveform. When pulse modulation is applied to binary symbols result in pulse-code modulation (PCM). - line code, M-ary pulse modulation. (4) Band Pass Modulation: - required if the transmission medium do not support the propagation of pulse-like waveform. þ Equalization - implemented to compensate for any signal distortion caused by nonideal hc(t). þ Source Codin - produce AD conversion and remove redundant information. - channel coding can reduce the probability of error and reduce snr. þ Multiplexing þ Encryption; - provides communication privacy, prevent intrusion. - combine signal of different characteristics or sources to share communication resources.

Digital Communication System l Important features of a DCS: l The transmitter sends a waveform from a finite set of possible waveforms during a limited time l The channel distorts, attenuates the transmitted signal and adds noise to it. l The receiver decides which waveform was transmitted given the noisy received signal l The probability of erroneous decision is an important measure for the system performance 2006 -01 -24 Lecture 1 15

Digital versus Analog l Advantages of digital communications: l Regenerator receiver l Signal; original-> distortion-> degraded-> badly degraded. . , -> amplified & regenerated Original pulse Regenerated pulse Propagation distance Voice Data A bit is a bit! Media l 2006 -01 -24 Different kinds of digital signal are treated identically. Lecture 1 16

Digital versus Analog. . cont’d • • Digital signals are regenerated. Less distortion due to ‘ 1’ and ‘ 0’ state. Availability of error detection and correction. Digital is more reliable, cheap cost and more flexible compare to analog. Different types of digital signals; data, telegraph, telephone television, have identical signal transmission a bit. Protect against interference, jamming and provide encryption/privacy. Distorted analog signal cannot be removed by amplification and cannot be regenerated.

Classification of Signals l Deterministic and random signals l Deterministic signal: No uncertainty with respect to the signal value at any time. l 2006 -01 -24 Random signal: Some degree of uncertainty in signal values before it actually occurs. l Thermal noise in electronic circuits due to the random movement of electrons l Reflection of radio waves from different layers of ionosphere Lecture 1 18

Classification of Signals … l Periodic and non-periodic signals A periodic signal l A non-periodic signal Analog and discrete signals A discrete signal 2006 -01 -24 Analog signals Lecture 1 19

Classification of Signals. . l Energy and power signals l A signal is an energy signal if, and only if, it has nonzero but finite energy for all time: l A signal is a power signal if, and only if, it has finite but nonzero power for all time: General rule: Periodic and random signals are power signals. Signals that are both deterministic and 2006 -01 -24 non-periodic are energy signals. Lecture 1 20

Random Process l A random process is a collection of time functions, or signals, corresponding to various outcomes of a random experiment. For each outcome, there exists a deterministic function, which is called a sample function or a realization. Real number Random variables Sample functions or realizations (deterministic function) time (t) 2006 -01 -24 Lecture 1 21

Random Process … l Strictly stationary: If none of the statistics of the random process are affected by a shift in the time origin. l Wide sense stationary (WSS): If the mean and autocorrelation functions do not change with a shift in the origin time. l Cyclostationary: If the mean and autocorrelation functions are periodic in time. l Ergodic process: A random process is ergodic in mean and autocorrelation, if and, respectively. 2006 -01 -24 Lecture 1 22

Autocorrelation l Defn: autocorrelation refers to the matching of a signal with a l Autocorrelation of an energy signal l Autocorrelation of a power signal delayed version of itself. l l For a periodic signal: Autocorrelation of a random signal l For a WSS process: 2006 -01 -24 Lecture 1 23

Spectral Density • Energy signals: • Energy spectral density (ESD): • Power signals: • Power spectral density (PSD): • Random process: • Power spectral density Lecture 1 (PSD): 2006 -01 -24 24

Properties of an Autocorrelation function l For real-valued (and WSS in case of random signals): 1. Autocorrelation and spectral density form a Fourier transform pair. 2. Autocorrelation is symmetric around zero. 3. Its maximum value occurs at the origin. 4. Its value at the origin is equal to the average power or energy. 2006 -01 -24 Lecture 1 25

Noise in Communication Systems • Thermal noise; thermal motion of electrons in all disipative components, is described by a zero-mean Gaussian random process, n(t). • Its PSD is flat, hence, it is called white noise. • n- Gaussian probability density function [w/Hz] Power spectral density Autocorrelation function Probability density function 2006 -01 -24 Lecture 1 26

Signal Transmission through Linear Systems Input Output Linear system Deterministic signals: l Random signals: l l Ideal distortion less transmission: All the frequency components of the signal not only arrive with an identical time delay, but also are amplified or attenuated equally. 2006 -01 -24 Lecture 1 27

Signal Transmission … - cont’d l Ideal filters: Non-causal! Low-pass Band-pass l High-pass Realizable filters: RC filters 2006 -01 -24 Butterworth filter Lecture 1 28

Bandwidth of signal S Baseband versus bandpass: Baseband signal Bandpass signal Local oscillator Bandwidth dilemma: S Bandlimited signals are not realizable! S Realizable signals have 1 infinite bandwidth! 2006 -01 -24 Lecture S 29

Bandwidth of Signal … l Different definition of bandwidth: a) Half-power bandwidth b) Noise equivalent bandwidth c) Null-to-null bandwidth a) Fractional power containment bandwidth b) Bounded power spectral density c) Absolute bandwidth (a) (b) (c) (d) 2006 -01 -24 Lecture 1 (e)50 d. B 30