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Communication System Overview

Communication System Overview. Gwo-Ruey Lee. Outlines. Communication System Digital Communication System Modulation. Communication System. 1/6. Input Transducer Transmitter Channel Receiver Output Transducer. Communication System. 2/6. Input transducer

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Communication System Overview

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  1. Communication System Overview Gwo-Ruey Lee

  2. Outlines • Communication System • Digital Communication System • Modulation

  3. Communication System 1/6 • Input Transducer • Transmitter • Channel • Receiver • Output Transducer

  4. Communication System 2/6 • Input transducer • Messages can be categorized as analog (continuous form)or digital (discrete form). • The message produced by a source must be converted by a transducer to a form suitable for the particular type of communication system employed.

  5. Communication System 3/6 • Transmitter • The purpose of the transmitter is to couple the message to the channel. • Modulation • For ease of radiation • to reduce noise and interference • For channel assignment • For multiplexing or transmission of several message over a single channel • To overcome equipment limitation

  6. Communication System 4/6 • Channel • Different forms • The signal undergoes degradation from transmitter to receiver • Noise, fading, interference……

  7. Communication System 5/6 • Receiver • The receiver is to extract the desired message from the received signal at the channel output and to convert it to a form suitable for the output transducer • Demodulation

  8. Communication System 6/6 • Output Transducer • The output transducer completes the communication system • The device converts the electric signal at its input into the form desired for the system user

  9. Digital Communication System 1/6

  10. Digital Communication System 2/6 • Source Encoder/ Decoder • The purpose of source coding is to reduce the number of bits required to convey the information provided by the information source. • The task of source coding is to represent the source information with the minimum of symbols. • High compression rates (Good compression rates) make be achieved with source encoding with lossless or little loss of information. • Source Coding • Fixed-length coding • Pulse-code modulation (PCM) • Differential Pulse-code modulation (DPCM) • Variable-length coding • Huffman Coding/ entropy coding

  11. Digital Communication System 3/6 • Channel Encoder/ Decoder • A way of encoding data in a communications channel that adds patterns of redundancy into the transmission path in order to lower the error rate. • The task of channel coding is to represent the source information in a manner that minimizes the error probability in decoding. • Error Control Coding • Error detection coding • Error correct coding

  12. Digital Communication System 4/6 • Error Control Coding • Linear block code • Convolutional code • RS code • Modulation Coding • Trellis code • Turbo code

  13. Digital Communication System 5/6 • Synchronization • Symbol/ Timing synchronization • Frequency synchronization • Carrier frequency synchronization • Sampling frequency synchronization • Two basic types of synchronization • Data-aid algorithm • Training sequences • Preambles • Non-data-aid algorithm • Blind

  14. Digital Communication System 6/6 • Channel Estimation • A channel estimate is only a mathematical estimation of what is truly happening in nature. • Allows the receiver to approximate the effect of the channel on the signal. • The channel estimate is essential for removing inter symbol interference, noise rejection techniques etc. • Two basic types of channel estimation methods • Data-aid algorithm • Training sequences • pilots • Non-data-aid algorithm • Blind

  15. Carrier: Amplitude Frequency Phase Modulation 1/10 • Analog Modulation • AM • FM • PM • Pulse Modulation • PAM / PPM / PCM / PWM • Digital Modulation • ASK • FSK • PSK • QAM

  16. Modulation 2/10 • Mapping • The process of mapping the information bits onto the signal constellation plays a fundamental role in determining the properties of the modulation • Modulation type • Phase shift keying (PSK) • Quadrature Amplitude Modulation (QAM)

  17. Modulation 3/10 • M-ary Phase Shift Keying • Consider M-ary phase-shift keying (M-PSK) for which the signal set is where is the signal energy per symbol, is the symbol duration, and is the carrier frequency. • This phase of the carrier takes on one of the M possible values, namely, , where .

  18. Modulation 4/10 • An example of signal-space diagram for 8-PSK

  19. Modulation 5/10 • Phase shift keying • BPSK • QPSK with Gray code • M-ary PSK where

  20. Modulation 6/10 • BER versus SNR curves in AWGN channel using BPSK, QPSK, 8-PSK,16-PSK .

  21. Modulation 7/10 • Quadrature Amplitude Modulation • The transmitted M-ary QAM signal for symbol n can be expressed as • where E is the energy of the signal with the lowest amplitude, and , and are amplitudes taking on the values • Note that M is assumed to be a power of 4. • The parameter a can be related to the average signal energy ( ) by

  22. Modulation 8/10 • An example of signal-space diagram for 16-square QAM.

  23. Modulation 9/10 • QAM

  24. Modulation 10/10 • BER versus SNR curves in AWGN channel using BPSK/QPSK, 16QAM, 64QAM, 256QAM.

  25. Communication System Overview • Readings • Any book about communications

  26. Random Process/ Stochastic Process

  27. Outlines 1/10 • Basic Concepts • Stationary Process • Transmission over Linear Time-Invariant (LTI) Systems

  28. Basic Concepts 2/10 • Why study random processes? • Due to the uncertainty of 1. noise and 2. the unpredictable nature of information itself. • Information signal usually is randomlike • We can not predict the exact value of the signal • Signal must be distributed by its statistical properties. • Ex: mean, variance…..

  29. Real line Basic Concepts 3/10 • Random Variable (r.v.) • Consider an experiment with sample space . The element of are the random outcomes, , of the experiment. If to every , we assign a real value , such a rule is called a random variable (r.v.)

  30. 1. : ensemble 2. : sample function (or a realization) 3. : r.v. 4. : numerical value r.v. Basic Concepts 4/10 • Random Process (r.p.) • A random process is the mapping of the outcomes in into a set of real valued functions of time, called sample function .

  31. Basic Concepts 5/10 • Classification of random process • From the perspective of time • Random process: • for , t has a continuous of values • Random sequence: • for , t can take on a finite or countably infinite number of values  • From the perspective of the valueof • Continuous: • can take on a continuous of values • Discrete : • Values of are countable

  32. Basic Concepts 6/10 • Classification of random process • Continuous random process • Discrete random process • Continuous random sequence • Discrete random sequence

  33. Basic Concepts 7/10 • 1st-order distributions function • It describes the instantaneous amplitude distribution of a random process • Mean: • 2nd-order distributions function • It distributes the structure of the signal in the time domain • Autocorrelation Function (A.F.)

  34. Basic Concepts 8/10 • Autocovariance • Cross-correlation • If and are orthogonal  • If and are statistically uncorrelated 

  35. Basic Concepts 9/10 • Crosscovariance • The autocorrelation function of a real WSS process is

  36. Basic Concepts 10/10 • The cross-correlation function of two real WSS process and is • If and are orthogonal  • If and are statistically uncorrelated  • Power Spectral Density (PSD) • PSD represents the distribution of signal strength (ie, energy or power) with frequency • The PSD of WSS process is the Fourier transform (FT) of the A.F.

  37. Stationary Process 1/9 • Stationary • A random process whose statistical properties do not change over time • Stationary Process • Strictly-Sense Stationary (SSS) • Wide-Sense Stationary (WSS) • Strictly-Sense Cyclostationary • Wide-Sense Cyclostationary

  38. Stationary Process 2/9 • Strictly-Sense Stationary (SSS) • A nth-order strictly-sense stationary process is a process in which for all , all , and all • Note: Mth-order stationary of the above equation holds for all . • Example: 2nd-order SSS process  1st-order SSS process

  39. Stationary Process 3/9 • A example of 2nd-order stationary

  40. Stationary Process 4/9 • Wide-Sense Stationary (WSS) • A random process is wide-sense stationary process (WSS) if • Its mean is constant • Its A.F. depends only on the time difference.

  41. Stationary Process 5/9 • The relationship between SSS and WSS • SSS  WSS (True) • SSS  WSS (Fault) • 1st-order SSS  • 2nd-order SSS  • For Gaussian process : SSS  WSS • Since the joint-Gaussian pdf is completely specified by its mean and A.F.

  42. Stationary Process 6/9 • Strictly-Sense Cyclostationary • A nth-order strictly-sense cyclostationary process is a process in which for all , all , and integer m ( mT is integer multiples of period T )

  43. Stationary Process 7/9 • Wide-Sense Cyclostationary • A random process with and is wide-sense cyclostationary if • Its mean satisfies • Its a.F. satisfies

  44. Stationary Process 8/9 • Ergodic Process • A random process is strictly ergodic process if all time and ensemble (statistical) average are interchangeable including mean, A.F. PSD, etc. • A random process is wise-sense ergodic if it it ergodic in the mean and the A.F. • mean ergodic • A.F. ergodic

  45. Stationary Process 9/9 • The relationship between ergodic and stationary • Ergodic  stationary (True) • Ergodic  stationary (Fault)

  46. Transmission over LTI Systems 1/3 • Linear Time-Invariant (LTI) Systems

  47. Transmission over LTI Systems 2/3 • Assumptions: and are real-valued and is WSS. • The mean of the output • The cross-correlation function

  48. Transmission over LTI Systems 3/3 • The A.F. of the output • The PSD of the output

  49. Random Process/ Stochastic Process • Readings • Communication Systems, 4th edition, Simon Haykin, Wiley • Chapter 1 – 1.1 ~1.7, 1.8

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