Intro Digital Communication

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    DIGITAL COMMUNICATIONS(ADVANCED LEVEL)

    Chapter 1:An Introduction to Digital Communications

    Lectured by Assoc. Prof. Dr. Thuong Le-TienNational Distinguished Lecturer

    Cell: 0903 787 989Email: thuongle@hcmut.edu.vn

    August, 2014

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    Historical Background

    *Information theory and coding:

    Shannons 1948 paper was followed by three

    ground-breaking advances in coding theory

    1. Development of the first nontrivial error correcting codesby Golay 1949 and Hamming 1950

    1. Development of Turbo Codes by Berou, Glavieux and

    Thitimjshima 1993 provide near-optimum error-correcting

    coding and decoding performance in additive white

    Gaussian noise (AWGN)

    1. Rediscovery of Low Density Parity Check (LDPC) codes

    (by first original by Gallager 1962) by Tanner 1981

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    The Internet::

    Advanced Research Project Agency Network (ARPANET) 1971 by

    US Department of Defence* The pioneering work in packet switching was done on the ARPANET

    1985: ARPANET renamed the Internet

    Wireless Communications

    1864: James Clerk Maxwell formulated the elctromagnetic theoryof light and predicted the existence of radio waves; then set four

    equations connect electric and magnetic quantities

    1884 Henrich Hertz demonstrated the existence of radio waves

    experimentally

    Dec 12, 1901, Guglielmo Marconi received a radio signal at Signal

    Hill in Newfoundland from Cornwall England (2100miles away)

    1906, Fessenden, a self-educated academic, made history by

    conducting the first radio broadcast, transmitting music and voice (AM)

    * 1988, the first digital cellular system in Europe GSM and AMPS in US

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    There are two basic models of communications* Broadcasting* Point-to-point

    Transmitter*Modulation

    *Coding

    Channel*Attenuation

    *Noise*Distortion*Interference*Fading

    Receiver*Detection (Demod+Decod)

    *Filtering (Equalization)

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    (a) FDMA (b) TDMA) (b) CDMA (freq.-hop)

    In multiple access, same channel is used to transmit multiple informationchannels transported by multiple messages to different users

    TDMA (Time division multiple access), users occupy different time slotFDMA (Freq. division multiple access), users occupy different freq. bands

    CDMA (Code division multiple access), users occupy the same frequencyband but modulate their messages with different codes

    WDMA (Wavelength division multiple access): another category ofFDMA but used in optical communications

    SDMA (Space division multiple access)

    MULTIPLE ACCESS TECHNIQUE

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    Communication Networks

    Operated by OpenSystem Interconnection(OSI)composed of 7 layers

    7.

    6.

    5.

    4.

    3.

    2.

    1.

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    Digital Communications

    There are 3 layers of the OSI model where itcan effect the design of DCS

    Physical Layer: communications between nodes

    through MODEMData-link Layer: Error Detection and Correction; a

    portion of the data link layer, called the Medium

    access control (MAC) sub-layer, allowing frames to

    be send over the shared transmission channel

    Network Layer: Routing, quality of services

    and Flow Control

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    General Block Diagram of a DCS

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    Important features of a DCS:

    Transmitter sends a waveform from a finiteset of possible waveforms during a limitedtime

    Channel distorts, attenuates the transmitted

    signal and adds noise, interferences to it. Receiver decides which waveform was

    transmitted from the noisy received signal

    Probability of erroneous decision (or BER) is

    an important measure for the systemperformance

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    Digital versus analog

    Advantages of digital communications: Regenerator at receiver

    Different kinds of digital signal are treated

    identically.

    Data

    Voice

    Media

    Propagation distance

    Original

    pulse

    Regenerated

    pulse

    A bit is a bit!

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    Shannons information capacity theorem

    Bit error rate (BER) The information capacity of channel or the

    maximum rate of transmission withouterrors:

    C=B log2(1+SNR)=3.32B log10(1+SNR) b/s

    The efficiency of Dig-Com uses =R/C withR is the sampling rate.

    C provide the approach of trade-offbetween B and received SNR

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    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 uncertaintyin signal values before it actually occurs.

    Thermal noise in electronic circuits due to

    the random movement of electrons

    Reflection of radio waves from differentlayers of ionosphere

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    (a) Transmitted signal(b) Effects of distortion(c) Effects of interference(d) Effects of noise

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    Periodic and non-periodic signals

    Analog and discrete signals

    A discrete signal

    Analog signals

    A non-periodic signalA periodic signal

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    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.

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    Random process

    A random process is a collection of time functions, orsignals, 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.

    Sample functions

    or realizations

    (deterministic

    function)

    Randomvariables

    time (t)

    Realnumber

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    Strictly stationary: If none of the statistics of the random processare affected by a shift in the time origin.

    Wide sense stationary (WSS): If the mean and autocorrelationfunction do not change with a shift in the origin time.

    Cyclostationary: If the mean and autocorrelation function areperiodic in time.

    Ergodic process: A random process is ergodic in mean andautocorrelation, if

    and

    , respectively.

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    Autocorrelation

    Autocorrelation of an energy signal

    Autocorrelation of a power signal

    For a periodic signal:

    Autocorrelation of a random signal

    For a WSS process:

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    Spectral density

    Energy signals:

    Energy spectral density (ESD):

    Power signals:

    Power spectral density (PSD):

    Random process: Power spectral density (PSD):

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    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.

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    Noise in communication systems

    Thermal noise is described by a zero-mean Gaussianrandom process, n(t).

    Its PSD is flat, hence, it is called white noise.

    [w/Hz]

    Probability density function

    Power spectral

    density

    Autocorrelation

    function

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    Signal transmission throughlinear systems

    Deterministic signals:

    Random signals:

    Ideal distortionless transmission:All the frequency components of the signal not only arrive

    with an identical time delay, but also are amplified orattenuated equally.

    Input Output

    Linear system

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    Bandwidth of signal

    Baseband versus bandpass:

    Baseband

    signal

    Bandpass

    signal

    Local oscillator

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    Different definition of bandwidth:

    a) Half-power bandwidth

    b) Noise equivalent bandwidthc) Null-to-null bandwidth

    d) Fractional power containment bandwidth

    e) Bounded power spectral densityf) Absolute bandwidth

    (a)

    (b)

    (c)(d)

    (e)50dB