Modulation and coding course- lecture 1

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Modulation and coding course- lecture 1

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Scope of the course -Digital Communication systems -Practical information -Course material -Schedule -Staff - Communication is a process by which information is exchanged between individuals through a common system of symbols, signs, or behavior -Grading

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  1. Digital Communication I: Modulation and Coding Course Period 3 - 2007 Catharina Logothetis Lecture 1
  2. Course information Scope of the course Digital Communication systems Practical information Course material Schedule Staff Grading More information on: Introduction to digital communication systems Lecture 1 2
  3. Scope of the course Communication is a process by which information is exchanged between individuals through a common system of symbols, signs, or behavior Communication systems are reliable, economical and efficient means of communications Public switched telephone network (PSTN), mobile telephone communication (GSM, 3G, ...), broadcast radio or television, navigation systems, ... The course is aiming at introducing fundamental issues in designing a (digital) communication system Lecture 1 3
  4. Scope of the course ... Example of a (digital) communication systems: Cellular wireless communication systems BS Base Station (BS) UE UE UE User Equipment (UE) Lecture 1 4
  5. Scope of the course ... General structure of a communication systems Noise Transmitted Received Received Info. signal signal info. SOURCE Source Transmitter Channel Receiver User Transmitter Source Channel Formatter Modulator encoder encoder Receiver Source Channel Formatter Demodulator decoder decoder Lecture 1 5
  6. Scope of the course … Learning fundamental issues in designing a digital communication system (DCS): Utilized techniques Formatting and source coding Modulation (Baseband and bandpass signaling) Channel coding Equalization Synchronization .... Design goals Trade-offs between various parameters Lecture 1 6
  7. Practical information Course material Course text book: “Digital communications: Fundamentals and Applications” by Bernard Sklar,Prentice Hall, 2001, ISBN: 0-13-084788-7 Additional recommended books: “Communication systems engineering”, by John G. Proakis and Masoud Salehi, Prentice Hall, 2002, 2nd edition, ISBN: 0-13- 095007-6 “Introduction to digital communications”, by Michael B. Pursley, Pearson, Prentice Hall, 2005, International edition, ISBN: 0-13- 123392-0 ”Digital communications”, by Ian A. Glover and Peter M. Grant, Pearson, Prentice Hall, 2004, 2nd edition, ISBN: 0-13-089399-4 Material accessible from course homepage: News Lecture slides (.ppt, pdf) Laboratory syllabus (Lab. PM) Set of exercises and formulae Old exams Lecture 1 7
  8. Schedule 13 lectures: from week 5 to week 8 10 tutorials: week 5 to week 8 1 mandatory laboratory work: Week 9 Final written exam on 12th of March 2007 kl 9.00-14.00. Lecture 1 8
  9. Staff Course responsible and lecturer and giving tutorials: Catharina Logothetis Office: Hus 7 (våning 6), Ångström Tel.: 018-471 3068 Email: Lecture 1 9
  10. Today, we are going to talk about: What are the features of a digital communication system? Why “digital” instead of “analog”? What do we need to know before taking off toward designing a DCS? Classification of signals Random process Autocorrelation Power and energy spectral densities Noise in communication systems Signal transmission through linear systems Bandwidth of signal Lecture 1 10
  11. Digital communication system Important features of a DCS: Transmitter sends a waveform from a finite set of possible waveforms during a limited time Channel distorts, attenuates the transmitted signal and adds noise to it. Receiver decides which waveform was transmitted from the noisy received signal Probability of erroneous decision is an important measure for the system performance Lecture 1 11
  12. Digital versus analog Advantages of digital communications: Regenerator receiver Original Regenerated pulse pulse Propagation distance Different kinds of digital signal are treated identically. Voice Data A bit is a bit! Media Lecture 1 12
  13. Classification of signals Deterministic and random signals Deterministic signal: No uncertainty with respect to the signal value at any time. Random signal: Some degree of uncertainty in signal values before it actually occurs. Thermal noise in electronic circuits due to the random movement of electrons Reflection of radio waves from different layers of ionosphere Lecture 1 13
  14. Classification of signals … Periodic and non-periodic signals A periodic signal A non-periodic signal Analog and discrete signals A discrete signal Analog signals Lecture 1 14
  15. Classification of signals .. Energy and power signals A signal is an energy signal if, and only if, it has nonzero but finite energy for all time: 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 non-periodic are energy signals. Lecture 1 15
  16. Random process 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. Random variables Real number Sample functions or realizations (deterministic function) time (t) Lecture 1 16
  17. Random process … Strictly stationary: If none of the statistics of the random process are affected by a shift in the time origin. Wide sense stationary (WSS): If the mean and autocorrelation function do not change with a shift in the origin time. Cyclostationary: If the mean and autocorrelation function are periodic in time. Ergodic process: A random process is ergodic in mean and autocorrelation, if and , respectively. Lecture 1 17
  18. Autocorrelation Autocorrelation of an energy signal Autocorrelation of a power signal For a periodic signal: Autocorrelation of a random signal For a WSS process: Lecture 1 18
  19. Spectral density Energy signals: Energy spectral density (ESD): Power signals: Power spectral density (PSD): Random process: Power spectral density (PSD): Lecture 1 19
  20. Properties of an autocorrelation function 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. Lecture 1 20
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