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Vidyalankar final-essentials of communication systems
 

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Fundamentals of communication systems

Fundamentals of communication systems

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    Vidyalankar final-essentials of communication systems Vidyalankar final-essentials of communication systems Presentation Transcript

    • Essentials Of Communication Systems A Presentation By- A. S. Kurhekar http://sites.google.com/site/anilkurhekar100
    • Overview of Analog Technology
      • Areas of Application
        • Old telephone networks
        • Most television broadcasting at present
        • Radio broadcasting
    • Analog Signals: The Basics Time Signal Frequency = Cycles/Second Cycle Amplitude
    • Amplitude and Cycle
      • Amplitude
        • Distance above reference line
      • Cycle
        • One complete wave
      • Frequency
      • Frequency
        • Cycles per second
        • Hertz is the unit used for expressing frequency
      • Frequency spectrum
        • Defines the bandwidth for different analog communication technologies
    • Frequency Spectrum and Bandwidth
      • Available range of frequencies for communication
      • Starts from low frequency communication such as voice and progresses to high frequency communication such as satellite communication
      • The spectrum spans the entire bandwidth of communicable frequencies
    • Frequency Spectrum
      • Low-end
        • Voice band
      • Middle
        • Microwave
      • High-end
        • Satellite communication
      Low Frequency High Frequency Radio Frequency Coaxial Cable MHz Voice KHz Satellite Transmission Microwave MHz
    • An Overview of Digital Technology
      • Areas of Application
        • Computers
        • New telephone networks
        • Phased introduction of digital television technology
      • Digital Technology
        • Basics
        • Digital signals that could be assigned digital values
      • Digital computer technology
        • Digital signals
        • Binary representation
          • Encoded into ones and zeros
    • Digital Signal And Binary Signals
      • Digital signals
        • Value limited to a finite set
        • Digital systems more robust
        • Binary Signals
        • Has at most 2 values
        • Used to represent bit values
        • Bit time T needed to send 1 bit
        • Data rate R=1/T bits per second
      t x(t) t x(t) 1 0 0 0 1 1 0 T
    • Digital Terms
        • Pulse
        • Pulse duration
        • Pulse amplitude
        • Signal strength
      • Clock Speed and Execution Speed
        • Pulse duration is inversely proportional to the clock frequency
        • Faster the clock speed, the smaller the pulse duration
        • Smaller the pulse duration, the faster the execution in general
    • Performance Metrics
      • Analog Communication Systems
        • Metric is fidelity
        • Want m(t)  m(t)
      • Digital Communication Systems
        • Metrics are data rate (R bps) and probability of bit error (P b =p(b  b))
        • Without noise, never make bit errors
        • With noise, P b depends on signal and noise power, data rate, and channel characteristics.
    • Data Rate Limits
      • Data rate R limited by signal power, noise power, distortion, and bit error probability
      • Without distortion or noise, can have infinite data rate with P b =0.
      • Shannon capacity defines maximum possible data rate for systems with noise and distortion
        • Rate achieved with bit error probability close to zero
        • In white Gaussian noise channels, C=B log(1+SNR)
        • Does not show how to design real systems
      • Shannon obtained C=32 Kbps for phone channels
        • Get higher rates with modems/DSL (use more BW)
        • Nowhere near capacity in wireless systems
    • Signal Energy and Power
      • The energy in a signal g(t) is
      • The power in a signal g(t) is
      • Power is often expression in dBw or dBm
        • [10 log 10 P] dBW is dB power relative to Watts
        • [10 log 10 (P/.001)] dBm is dB power relative to mWatts
        • Signal power/energy determines its resistance to noise
    • The Communication System
      • Communication systems modulate analog signals or bits for transmission over channel.
      • The building blocks of a communication system convert information into an electronic format for transmission, then convert it back to its original format after reception.
      • Goal of transmitter (modulator) and receiver (demodulator) is to mitigate distortion/noise from the channel.
      • Digital systems are more robust to noise and interference.
      • Performance metric for analog systems is fidelity, for digital it is rate and error probability.
      • Data rates over channels with noise have a fundamental capacity limit.
    • The Backdrop
      • Data rates over channels with noise have a fundamental capacity limit.
      • Signal energy and power determine resistance to noise
      • Communication system shift, scale, and invert signals
      • Unit impulse and step functions important for analysis
      • Fourier series represents periodic signals in terms of exponential or sinusoidal basis functions
      • Exponentials are eigenfunctions of LTI filters
      • Fourier transform is the spectral components of a signal
      • Rectangle in time is sinc in frequency; Time-limited signals are not bandlimited and vice versa
    • Communication System Block Diagram Source Encoder Source Decoder Channel Receiver Text Images Video
      • Source encoder converts message into message signal or bits.
      • Transmitter converts message signal or bits into format appropriate for channel transmission (analog/digital signal).
      • Channel introduces distortion, noise, and interference.
      • Receiver decodes received signal back to message signal.
      • Source decoder decodes message signal back into original message.
      Transmitter
    • Analysis Outline
      • Channel Distortion and Equalization
      • Ideal Filters
      • Energy Spectral Density and its Properties
      • Power Spectral Density and its Properties
      • Filtering and Modulation based on PSD
    • Channel Distortion
      • Channels introduce linear distortion
        • Electronic components introduce nonlinear distortion
      • Simple equalizers invert channel distortion
        • Can enhance noise power
      X(f) X(f)+N(f)/H(f) H(f) 1/H(f) N(f) +
    • Filters
      • Low Pass Filter (linear phase)
      • Band Pass Filter (linear phase)
      • Most filtering (and other signal processing) is done digitally (A/D followed by DSP)
      1 -B B 1 1
    • Energy Spectral Density (ESD)
      • Signal energy:
      • ESD measures signal energy per unit Hz.
      • ESD of a modulation signal
      f Contains less information than Fourier Transform (no phase)  g (f) .25[  g (f-f 0 )+  g (f+f 0 )] X cos(2  f 0 t)
    • Autocorrelation
      • Defined for real signals as  g (t)=g(t) * g(-t)
        • Measures signal self-similarity at t
        • Can be used for synchronization
      • ESD and autocorrelation FT pairs:  g (t)   g (f)
      • Filtering based on ESD
       g (f) |H(f)| 2  g (f) H(f)
    • Power Spectral Density
      • Similar to ESD but for power signals (P=E/t)
      • Distribution of signal power over frequency
       |G T (f)| 2 1 2T g T (t) -T T   T=  S g (f)
    • Filtering and Modulation
      • Filtering
      • Modulation
        • When S g (f) has bandwidth B<f 0 ,
      S g (f) |H(f)| 2 S g (f) H(f) S g (f) .25[S g (f-f 0 )+ S g (f+f 0 )] X cos(2  f c t) otherwise +cross terms
    • Modulation and Autocorrelation
      • Modulation
        • When S g (f) has bandwidth B<f 0
      • Autocorrelation
      S g (f) .25[S g (f-f 0 )+ S g (f+f 0 )] X cos(2  f c t)
    • Probability Theory
      • Mathematically characterizes random events.
      • Defined on a probability space: (S,{ A i },P(•))
        • Sample space of possible outcomes z i .
      • Sample space has a subset of events A i
      • Probability defined for these subsets.
      S A 2       A 3
    • Probability Measures-I
      • P(S)=1
      • 0  P(A)  1 for all events A
      • If (A  B)=  then P(AUB)=P(A)+P(B).
      • Conditional Probability:
        • P(B|A)=P(A  B)/P(A)
        • Bayes Rule: P(B|A)=P(A|B)P(B)/P(A)
      • Independent Events:
        • A and B are independent if P(A  B)=P(B)P(A)
        • Independence is a property of P(•)
        • For independent events, P(B|A)=P(B).
    • Probability Measure-II
      • Bernoulli Trials:
      • Total Probability Theorem:
        • Let A 1 ,A 2 , …, A n be disjoint with  i A i= S
        • Then:
      • Random Variables and their CDF and pdf
        • CDF: F x ( x )=P(x  x )
        • pdf: p x (x)=dF x (x)/dx
      • Means, Moments, and Variance
      A 1 A 2 A 3 S B P 1 P 3 P 2 0 1 2 3 x x x S
    • Gaussian Random Variables
      • pdf defined in terms of mean and variance
      • Gaussian CDF defined by Q function:
       x  x N (  ,  2 ) Z~ N (  ) Tails decrease exponentially
    • Several Random Variables
      • Let X and Y be defined on (S,{A i },P(•))
      • Joint CDF F X, Y (x ,y)=P (x  x , y  y )
      • Joint pdf:
      • Conditional densities:
      • Independent RVs:
    • Sums of Random Variables and the Central Limit Theorem
      • Sums of RVs: z=x + y
        • P z ( z )=p y ( y )  p x ( x )
        • Mean of sum is sum of means
        • Variance of sum is sum of variances
      • Central Limit Theorem: x 1 ,…,x n i.i.d
        • Let y=  i x i , z=(y-E[y])/s Y
        • As n  , z becomes Gaussian, E[y]=0, s y 2 =1.
    • Stationarity, Mean, Autocorrelation
      • A random process is (strictly) stationary if time shifts don’t change probability:
        • P(x(t 1 )  x 1 ,x(t 2 )  x 2 ,…,x(t n )  x n )= P(x(t 1 +T)  x 1 ,x(t 2 +T)  x 2 ,…,x(t n +T)  x n )
        • True for all T and all sets of sample times
      • Mean of random process: E[x(t)]=
        • Stationary process: E[X(t)]=
      • Autocorrelation of a random process:
        • Defined as R X (t 1 ,t 2 )= E[x(t 1 )x(t 2 )]]
        • Stationary process: R x (t 1 ,t 2 )=R X (t 2 -t 1 )
        • Correlation of process samples over time
      x(t) x
    • Wide Sense Stationary (WSS)
      • A process is WSS if
        • E[x(t)] is constant
        • R X (t 1 ,t 2 )= E[X(t 1 )X(t 2 )]]=R X (t 2 -t 1 )= R X (t)
        • Intuitively, stationary in 1 st and 2 nd moments
      • Ergodic WSS processes
        • Have the property that time averages equal probabilistic averages
        • Allow probability characteristics to be obtained from a single sample over time
    • Power Spectral Density (PSD)
      • Defined only for WSS processes
      • FT of autocorrelation function: R X (t)  S X (f)
      • E[X 2 (t)]=  S X (f) df
      • White Noise: Flat PSD
        • Good approximation in practice
      • Modulation:
      .5N 0  (  ) .5N 0  S n (f) R n (  ) f S n (f) .25[S n (f-f c )+ S n (f-f c )] X cos(2  f c t+  )
    • Gaussian Processes
      • z(t) is a Gaussian process if its samples are jointly Gaussian
      • Filtering a Gaussian process results in a Gaussian process
      • Integrating a Gaussian process results in a Gaussian random variable
    • Examples of noise in Communication Systems
      • Gaussian processes
        • Filtering a Gaussian process yields a Gaussian process.
        • Sampling a Gaussian process yields jointly Gaussian RVs
        • If the autocorrelation at the sample times is zero, the RVs are independent
      • The signal-to-noise power ratio (SNR) is obtained by integrating the PSD of the signal and integrating the PSD of the noise
      • In digital communications, the bit value is obtained by integrating the signal, and the probability of error by integrating Gaussian noise
    • Introduction to Carrier Modulation
      • Basic concept is to vary carrier signal relative to information signal or bits
        • The carrier frequency is allocated by a regulatory body like the FCC – spectrum is pretty crowded at this point.
      • Analog modulation varies amplitude (AM), frequency (FM), or phase (PM) of carrier
      • Digital modulation varies amplitude (MAM), phase (PSK), pulse (PAM), or amplitude/phase (QAM)
    • Double Sideband (Suppressed Carrier) Amplitude Modulation
      • Modulated signal is s (t)=m(t)cos(2pi f c t)
        • Called double-sideband suppressed carrier (DSBSC) AM
      • Generation of DSB-SC AM modulation
        • Direct multiplication (impractical)
        • Nonlinear modulators: Basic premise is to add m(t) and the carrier, then perform a nonlinear operation
        • Generates desired signal s(t) plus extra terms that are filtered out.
        • Examples include diode/transistor modulators, switch modulators, and ring modulators
    • Coherent Detection of DSBAM
      • Detector uses another DSB-SC AM modulator
      • Demodulated signal: m´(t)=.5cos(f 2 -f 1 )m(t)
        • Phase offset: if f 2 -f 1 =  p/2, m´(t)=0
      • Coherent detection via PLL (f 2  f 1 ) required
        • Will study at end of AM discussion
      m(t) cos(  c t+    DSBSC Modulator s(t) DSBSC Modulator LPF m´(t) cos(  c t+    Channel
    • Introduction to Angle Modulation and FM
      • Information encoded in carrier freq./phase
      • Modulated signal is s(t)=A c cos(q(t))
        • q (t)=f (m (t))
      • Standard FM: q (t)=2pf c t+2pk f  m(t)dt
        • Instantaneous frequency: f i =f c + k f m(t)
        • Signal robust to amplitude variations
        • Robust to signal reflections and refractions
      • Analysis is nonlinear
        • Hard to analyze
    • FM Bandwidth and Carson’s Rule
      • Frequency Deviation: Df=k f max |m (t)|
        • Maximum deviation of w i from w c : w i =w c + k f m(t)
      • Carson’s Rule:
        • B s depends on maximum deviation from w c AND how fast w i changes
      • Narrowband FM: Df<<B m  B s  2B m
      • Wideband FM: Df>>B m  B s  2Df
      B s  2  f+2B m
    • Spectral Analysis of FM
      • S (t)= A cos (w c t + k f  m (a) da)
        • Very hard to analyze for general m(t).
      • Let m(t)=cos (w m t): Bandwidth f m
      • S(f) sequence of d functions at f=f c ± nf m
        • If Df <<f m , Bessel function small for f  (f c  f m )
        • If Df >>f m , significant components up to f c ±Df.
      S(f) for m(t)=cos(2  f m t) f c f c +f m f c +2f m f c +3f m f c + 4f m f c -4f m f c -3f m f c -2f m f c -f m f … … .5A c J n (  ) B  2  f WBFM .5A c J n (  )
    • Generating FM Signals
      • NBFM
      • WBFM
        • Direct Method: Modulate a VCO with m(t)
      • Indirect Method
      m(t) Product Modulator Asin(  c t) s(t) 2  k f  ( ·)dt  (t) -90 o LO + A c cos(  c t) + - Product Modulator (k 1 ,f 1 ) m(t) s 1 (t) Nonlinear Device s 2 (t) BPF s(t)
    • FM Detection
      • Differentiator and Envelope Detector
      • Zero Crossing Detector
        • Uses rate of zero crossings to estimate w i
      • Phase Lock Loop (PLL)
        • Uses VCO and feedback to extract m(t)
    • Introduction to Digital Modulation
      • Most information today is in bits
      • Baseband digital modulation converts bits into analog signals y(t) (bits encoded in amplitude)
      • Bandwidth and PSD of y(t) determined by pulse shape p(t) and a k :
      • If pulse duration is bit time T b , modulation called non-return to zero (NRZ); if less than T b , called return to zero (RZ)
      1 0 1 1 0 1 0 1 1 0 On-Off Polar t t T b
    • Pulse Shaping
      • Pulse shaping is the design of pulse p(t)
        • Want pulses that have zero value at sample times nT
      • Rectangular pulses don’t have good BW properties
      • Nyquist pulses allow tradeoff of bandwidth characteristics and sensitivity to timing errors
    • Passband Digital Modulation
      • Changes amplitude (ASK), phase (PSK), or frequency (FSK) of carrier relative to bits
      • We use BB digital modulation as the information signal m(t) to encode bits, i.e. m(t) is on-off, etc.
      • Passband digital modulation for ASK/PSK) is a special case of DSBSC; has form
      • FSK is a special case of FM
    • ASK, PSK, and FSK
      • Amplitude Shift Keying (ASK)
      • Phase Shift Keying (PSK)
      • Frequency Shift Keying
      1 0 1 1 1 0 1 1 1 0 1 1 AM Modulation AM Modulation FM Modulation m(t) m(t)
    • ASK/PSK Demodulation
      • Similar to AM demodulation, but only need to choose between one of two values (need coherent detection)
      • Decision device determines which of R 0 or R 1 that R(nT b ) is closest to
        • Noise immunity DN is half the distance between R 0 and R 1
        • Bit errors occur when noise exceeds this immunity
      s(t)  cos(  c t +  ) nT b Decision Device “ 1” or “0” r(nT b ) R 0 R 1 Integrator (LPF)  N a  r(nT b ) r(nT b )+ 
    • Noise in ASK/PSK
      • Probability of bit error: P b =p (|N ( nT b )|>DN=.5|R 1 -R 0 |)
      • N ( n T b ) is a Gaussian RV: N ~ N( m=0,s 2 =.25N o T b )
      • For x~N(0,1), Define Q (z)=p (x>z)
      • ASK:
      • PSK:
      s(t)  cos(  c t) nT b R(nT b )+ N(nT b ) “ 1” or “0” + N(t) Channel R 1 R 0  N
    • FSK Demodulation
      • Minimum frequency separation required to differentiate:
        • |f 1 -f 2 |  .5n/T b (MSK uses minimum separation of n=1)
        • With this separation, R 1 =0 if “0” sent, R 0 =0 if “1” sent
      • Comparator : R 1 =.5AT b if “1’ sent, R 0 =.5AT b if “0” sent
      • Comparator outputs “1” if (R 1 +N 1 )-(R 2 +N 2 )>0, otherwise “0”
        • Error probability depends on N 1 -N 2
      s(t)  cos(2  1 t) R 1 (nT b )+N 1 “ 1” or “0”  cos(  0 t) nT b R 0 (nT b )+N 2 Comparator
    • FSK Error Probability
      • Analysis similar to ASK/PSK
      • P b =p(N 1 -N 2 >.5AT b )
      • Distribution of N 1 -N 2
        • Sum of indep. Gaussians is Gaussian (|f 1 -f 2 |  .5n/T b )
        • Mean is sum of means, Variance is sum of variances
        • N 1 ,N 2 ~N(m=0,s 2 =.25N o T b ) (Same as in ASK/PSK)
        • N 1 -N 2 ~ N(m=0,s 2 =.5N o T b )
      • P b =p(N 1 -N 2 >.5AT b )= Q(.5AT b /  .5N 0 T b )
      • =Q(  .5T b A 2 /N 0 )=Q(  E b /N 0 )
    • Summary of Digital Modulation
      • Pulse shaping used in both baseband and passband modulation to determine signal BW and resistance to impairments.
      • Digital passband modulation encodes binary bits into the amplitude, phase, or frequency of the carrier.
      • ASK/PSK special case of AM; FSK special case of FM
      • Noise immunity in receiver dictates how much noise reqd to make an error
      • White Gaussian noise process causes a Gaussian noise term to be added to the decision device input
      • Bit error probability with white noise is a function of the symbol energy to noise spectral density ratio.
      • BPSK has lower error probability than ASK for same energy per bit.
      • FSK same error prob. as ASK; less susceptible to amplitude fluctuations.
    • Performance Degradation
      • Phase offset Dq reduces noise immunity by cos (Dq).
      • If noise is not mean zero, causes P b to increase in one direction.
      • With timing offset, integrate over [D t , T b +Dt]
        • Interference from subsequent bit.
    • Multilevel Modulation
      • m bits encoded in pulse of duration T s (R b =m/T s )
        •  n constant over a symbol time T s , and can take M=2 m different values on each pulse.
      • Phase Shift Keying (MPSK)
      • Similar ideas in MFSK
      • Demodulation similar to binary case
      Higher data rate more susceptible to noise 00 10 01 11 T s 00 10 01 11
    • Key Points To Remember
      • PSD and pulse shaping in BB modulation
        • PSD depends on pulse shape
        • Nyquist pulses avoid ISI: p ( n T b )=0
        • Raised Cosine pulses trade BW efficiency for timing error robustness
      • Passband digital modulation for ASK/PSK is a special case of DSBSC;
      • FSK is a special case of FM:
      • Demodulation uses a decision device to determine if a “1’’ or “0’’ was sent
      • Noise can cause the decision device to output an erroneous bit
    • The Interpretations
      • Communication systems modulate analog signals or bits for transmission over channel.
      • The building blocks of a communication system convert information into an electronic format for transmission, then convert it back to its original format after reception.
      • Goal of transmitter (modulator) and receiver (demodulator) is to mitigate distortion/noise from the channel.
      • Digital systems are more robust to noise and interference.
      • Performance metric for analog systems is fidelity, for digital it is rate and error probability.
      • Data rates over channels with noise have a fundamental capacity limit.
    • What is Telemetry?
      • Telemetry : The process of measuring at a distance .
      • Aeronautical telemetry: The process of making measurements on an aeronautical vehicle and sending those measurements to a distant location for analysis
    • TELEMETERING APPLICATIONS
      • The use of telemetry spectrum is common to many different nations and many purposes
        • National defense
        • Commercial aerospace industry
        • Space applications
        • Scientific research
      • The primary telemetering applications are
        • Range and range support systems
          • Land mobile
          • Sea ranges
          • Air ranges
        • Space-based telemetry systems
        • Meteorological telemetry
    • Telemetry Use in Precision Agriculture
      • 􀂙 Differential GPS
      • 􀂙 Mobile phone reporting and control of center pivot irrigation systems
      • 􀂙 Soil moisture sensor networks
      • 􀂙 Climate control for high value crops
      • 􀂙 Real-time monitoring of equipment and people
    • Current Band-Allocations
    • Spectrum Encroachments 1435-1525 MHz: Manned Vehicle (L Band) Telemetry 2200-2390 MHz: Manned and Unmanned Vehicle (S Band) Telemetry WARC 92 BBA 97 Terrestrial DAB (Canada), CARIBSS, MediaStar WARC 92 US Alternative 2390 2350 2200 2250 2300 2200-2290 MHz: Unmanned 2360-2390 MHz: Manned 1525 1500 1435 1460 1485 One A/C can easily use over 20MHz of spectrum for a single mission
      • Thank You !!!