Tdm fdm

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Tdm fdm

  1. 1. FDM vs TDM Frequency division multiplexing(Analog Exchange) • • • • • • 1 chl 300 Hz to 3400 Hz 3chls : Basic Group(BG): 12 – 24 KHz 12chls: Sub Group : 4BG: 60 -108KHz 60chls:SuperGroup:5SubGp: 312 – 552 KHz 900chls:Master Group:16 super Gp 312 – 4028 KHz 2700chls:Super Master Group: (3 MG):312 -12336 KHz 1
  2. 2. Time division multiplexing • • • • • Voice frequency 4KHZ Sampling rate 8kilo samples per second Each sample requires 8bits 1 voice frequency requires 64kbps 32 voice frequencies (PCM) requires 2Mbps 2
  3. 3. System Capacity 2Mb PCM 8Mb Ist order 34Mb 2nd order 140Mb/STM1 3rd order Higher order No. of voice channels 30 120 2Gb/STM16 480 1920 30000 3
  4. 4. Chapter 2 Discrete-Time signals and systems • • • • • Representation of discrete-time signals (a)Functional (b)Tabular (c)Sequence Examples of singularity functions Impulse, Step and Ramp functions and shifted Impulse, Step and Ramp functions 4
  5. 5. Energy and power signals • If E=infinity and P= finite then the signal is power signal • If E=finite and P= zero then the signal is Energy signal • If E=infinite and P= infinite then the signal is neither energy nor power signal 5
  6. 6. Periodic and non-periodic signals • X(n+N) = x(n) for all n, and N is the fundamental time period. 6
  7. 7. Block diagram Representation of Discrete-Time systems • • • • • • • Adder A constant multiplier A signal multiplier A unit delay element A unit advance element Folding Modulator 7
  8. 8. Classcification of Discrete-time systems • • • • • • Static vs dynamic systems Time invariant vs time-variant systems Linear vs nonlinear systems systems Causal vs non causal systems Stable vs unstable systems Recursive vs non recursive systems 8
  9. 9. Static vs dynamic systems • A discrete time system is called static or memoryless if its output at any instant ‘n’ depends at most on the input sample at the same time, but not on past or future samples of the input. In any other case, the system is said to be dynamic or to have memory. 9
  10. 10. Time invariant vs time-variant systems • A system is called time-invariant if its impute-output characteristics do not change with time. • X(n-k) y(n-k) 10
  11. 11. Linear vs nonlinear systems systems • Linear system obeys the principle of superposition • T[a x1(n) + b x2(n)] = a T[x1(n)] + b T[x2(n)] 11
  12. 12. LTI system • The system which obeys both Linear property and Time invariant property. 12
  13. 13. Causal vs non causal systems • A system is said to be causal if the output of the system at any time depends only on present and past inputs, but not depend on future inputs. If a system doesnot satisfy this definition , it is called noncausal system. • All static systems are causal systems. 13
  14. 14. Stable vs unstable systems • An arbitrary relaxed system is said to be bounded input – bounded output (BIBO) stable if an only if every bounded input produces a bounded output. • y(n) = y2(n) + x(n) where x(n)=δ(n) 14
  15. 15. Recursive vs non recursive systems • Recursive system uses feed back • The output depends on past values of output. 15
  16. 16. Interconnection of discrete time systems • • • • Addition/subtraction Multiplication Convolution Correlation (a) Auto (b) Cross correlation 16
  17. 17. Convolution • Used for filtering of the signals in time domain. Used for LTI systems only • 1.comutative law • 2.associative law • 3.d17istributive law 17
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  19. 19. Correlation • Auto correlation improves the signal to noise ratio • Cross correlation attenuates the unwanted signal 19
  20. 20. Digital signal Distortion Distortion less transmission y(n) = α.x(n-l) 20
  21. 21. Random signal vs Deterministic signal • Deterministic signal: can be represented in mathematical form since the present, past and future values can be predicted based on the equation • Random signal: can not be put in mathematical form. Only the average, rms, peak value and bandwidth can be estimated with in a given period of time. 21
  22. 22. Advantages of digital over analog signals • • • • • • Faithful reproduction by reshaping Information is only 2 bits (binary) Processed in microprocessor Signals can be compressed Error detection and correction available Signals can be encrypted and decrypted 22
  23. 23. Disadvantages • Occupies more bandwidth • Difficult to process the digital signals in microwave frequency range since the speed of the microprocessor is limited (3 GHz) • Digital signals have to be converted into analog signals for radio communication. 23
  24. 24. Advantages of Digital signal processing over analog signal processing • • • • • • • • Flexible in system reconfiguration Accuracy , precise and better tolerances Storage Low cost Miniaturization Single micro processor Software operated (programmed) Artificial intelligence 24
  25. 25. Multi channel and Multidimensional Signals • Multichannel: • S(t) = [ s1(t), s2(t), s3(t)…] • Multi Dimensional: A value of a signal is a function of M independent variables. • S(x,y,t) = [ s1(x,y,t), s2(x,y,t), s3(x,y,t)…] 25
  26. 26. Analog-discrete-digital-PCM • • • • • • Analog frequency Demo of Time and frequency signals Digital frequency Periodic and aperiodic signals Sampling theorem A to D / D to A conversion 26
  27. 27. Variable sampling rate 27
  28. 28. x(t) |X(f)| t 0 -f (a) x δ ∞ (t) = n = -∞ δ (t-nT s) ... s -2T s 0 2T s 4T m ∞ 1 T s δ (f-nf n = -∞ s ... t -2f s s -f 0 s fs 2f f s (d) δ (t) |X s (f)| ... -4T s -2T s 0 2T s 4T t s (e) Ch2. Formatting ) ... (c) x s (t) = x(t) x f f (b) X δ (f) = ... -4T 0 m ... -2f s -f s -f m 0 (f) [Fig 2.6] Sa mp ling the orem u sing th e freq u en cy co nvo lution pro pe rty o f the Fou rier tra nsform fm fs 2f f s 28 [6]
  29. 29. Sampling • The operation of A/D is to generate sequence by taking values of a signal at specified instants of time • Consider a system involving A/D as shown below: x(t ) x (t) Analog-todigital converter x(nT ) x[n] xh(t) 0 T 2T 3T 4T 5T Digital-toanalog converter xh (t ) x(t) is the input signal and xh(t) is the sampled and reconstructed signal t T = sample period 29
  30. 30. Impulse Sampling What is Impulse Sampling? – Suppose a continuous–time signal is given by x(t), -∞ < t < +∞ – Choose a sampling interval T and read off the value of x(t) at times nT, n = -∞,…,-1,0,1,2,…,∞ – The values x(nT) are the sampled version of x(t) 30
  31. 31. Impulse Sampling • The sampling operation can be represented in a block diagram as below: ∞ δ T (t ) = ∑ δ (t − nT ) n = −∞ x(t ) xS (t ) • This is done by multiplying the signal x(t) by a train of impulse function δT(t) • The sampled signal here is represented by xS(t) and the sampling period is T 31
  32. 32. Impulse Sampling • From the block diagram, define a mathematical representation of the sampled signal using a train of δfunction xs ( t ) = ∞ ∑ x( nT )δ ( t − nT ) n = −∞ ∞ = x( t ) ∑ δ ( t − nT ) n = −∞ = x ( t )δ T ( t ) continuous –time signal function Train of periodic impulse 32 function
  33. 33. Impulse Sampling • We therefore have • And 1 ∞ jnω 0t δT (t) = ∑ e T n = −∞ It turns out that the problem is much easier to understand in the frequency domain. Hence, we determine the Fourier Transform of xs(t). 1 ∞ jnω 0t xs ( t ) = x( t ) ∑ e T n = −∞ 1 ∞ = ∑ x ( t ) e jnω 0t T n = −∞ 33
  34. 34. Impulse Sampling • Looking at each term of the summation, we have the frequency shift theorem: CTFT x( t ) e jnω 0t ↔ X ( ω − nω 0 ) • Hence the Fourier transform of the sum is: 1 ∞ X s ( ω ) = ∑ X ( ω − nω 0 ) T n = −∞ 34
  35. 35. Impulse Sampling i.e. The Fourier transform of the sampled signal is simply the Fourier transform of the continuous signal repeated at the multiple of the sampling frequency and scaled by 1/T. X(ω) 1 ω 0 Xs(ω) 1/T -2ω0 -ω0 0 ω0 ω 2ω0 35
  36. 36. Nyquist Sampling Theorem • The Nyquist sampling theorem can be stated as: If a signal x(t) has a maximum frequency content (or bandwidth) ω max, then it is possible to reconstruct x(t) perfectly from its sampled version xs(t) provided the sampling frequency is at least 2 × ω max 36
  37. 37. Nyquist Sampling Theorem • The minimum sampling frequency of 2 × ωmax is known as the Nyquist frequency, ωNyq • The repetition of X(ω) in the sampled spectrum are known as aliasing. Aliasing will occur any time the sample rate is not at least twice as fast as any of the frequencies in the signal being sampled. • When a signal is sampled at a rate less than ωNyq the distortion due to the overlapping spectra is called aliasing distortion 37
  38. 38. Multichannel Tx-Rx • Station A cable Station B 38
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  40. 40. Design of discrete time systems 40
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  56. 56. PERIODIC AND APERIODIC SIGNALS OF ANALOG AND DISCRETE 56
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  59. 59. Computing the Z-transform: an example = α nu[n] x[n] • Example 1: Consider the time function X(z) = ∞ ∑ x[n]z −n = n=−∞ = 1 1− αz −1 = ∞ ∑ n=0 α n z −n = ∞ ∑ (αz −1 )n n=0 z z −α 59
  60. 60. Another example … x[n] = −α nu[−n − 1] X(z) = ∞ ∑ −1 x[n]z −n = n=−∞ ∑ −α n z −n = n=−∞ • ∑ n=−∞ (αz −1 )n = ∑ (αz −1 )n n=−∞ l = −n;n = −∞ ⇒ l = ∞;n = −1 ⇒ l = 1 −1 −1 ∞ ∞ l=1 l=0 ∑ −(zα −1 )l = 1 − ∑ (zα −1 )l = 1 − 1 1 = 1 − zα −1 1 − αz −1 60
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  72. 72. Frequency domain of a rectangular pulse Fourier coefficients of the rectangular pulse train with time period Tp is fixed and the pulse width tow varies. 72
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  76. 76. SAMPLING IN TIME DOMAIN 76
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