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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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  • 1. Essentials Of Communication Systems A Presentation By- A. S. Kurhekar http://sites.google.com/site/anilkurhekar100
  • 2. Overview of Analog Technology
    • Areas of Application
      • Old telephone networks
      • Most television broadcasting at present
      • Radio broadcasting
  • 3. Analog Signals: The Basics Time Signal Frequency = Cycles/Second Cycle Amplitude
  • 4. 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
  • 5. 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
  • 6. 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
  • 7. 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
  • 8. 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
  • 9. 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
  • 10. 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.
  • 11. 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
  • 12. 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
  • 13. 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.
  • 14. 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
  • 15. 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
  • 16. 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
  • 17. 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) +
  • 18. 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
  • 19. 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)
  • 20. 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)
  • 21. 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)
  • 22. 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
  • 23. 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)
  • 24. 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
  • 25. 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).
  • 26. 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
  • 27. 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
  • 28. 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:
  • 29. 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.
  • 30. 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
  • 31. 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
  • 32. 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+  )
  • 33. 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
  • 34. 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
  • 35. 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)
  • 36. 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
  • 37. 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
  • 38. 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
  • 39. 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
  • 40. 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 (  )
  • 41. 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)
  • 42. 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)
  • 43. 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
  • 44. 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
  • 45. 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
  • 46. 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)
  • 47. 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 )+ 
  • 48. 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
  • 49. 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
  • 50. 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 )
  • 51. 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.
  • 52. 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.
  • 53. 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
  • 54. 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
  • 55. 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.
  • 56. 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
  • 57. 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
  • 58. 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
  • 59. Current Band-Allocations
  • 60. 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
  • 61.
    • Thank You !!!

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