1. 1 Data and Computer Communications Chapter 3 Data Transmission Required Reading: Stallings chapter 3

2. 2 Physical Layer Application Presentation Session transport Network Data link Physical Application Presentation Session transport Network Data link Physical Network Data link Physical Source nodeDestination node Intermediate node Signals Packets Bits Frames

3. 3 Physical / Data Link Layer Interface NL DLL PL Frame HDR ACK HDR SenderReceiver Transmitted Bits

4. 4 Physical Layer aCommunications and Information Theory are topics of whole courses aWe’ll cover some theoretical basics regarding communications over a physical channel aWe discover that there are physical limitations to communications over a given channel aWe’ll cover some fundamental theorems

5. 5 Terminology (1) zTransmitter zReceiver zMedium yGuided medium xe.g. twisted pair, optical fiber yUnguided medium xe.g. air, water, vacuum

6. 6 Terminology (2) zDirect link yNo intermediate devices zPoint-to-point yDirect link yOnly 2 devices share link zMulti-point yMore than two devices share the link

7. 7 Terminology (3) zSimplex yOne direction (but in Europe means half duplex) xe.g. Television zHalf duplex yEither direction, but only one way at a time xe.g. police radio zFull duplex yBoth directions at the same time xe.g. telephone

8. 8 Electromagnetic Signals zFunction of time yAnalog (varies smoothly over time) yDigital (constant level over time, followed by a change to another level) zFunction of frequency ySpectrum (range of frequencies) yBandwidth (width of the spectrum)

9. 9 Frequency, Spectrum and Bandwidth zTime domain concepts yContinuous signal xVaries in a smooth way over time yDiscrete signal xMaintains a constant level then changes to another constant level yPeriodic signal xPattern repeated over time yAperiodic signal xPattern not repeated over time

10. 10 Periodic Signal Characteristics yAmplitude (A): signal value, measured in volts yFrequency (f ): repetition rate, cycles per second or Hertz yPeriod (T): amount of time it takes for one repetition, T=1/f yPhase (Φ): relative position in time, measured in degrees or radians

11. 11 time (sec) amplitude (volts) 1 cycle frequency (hertz) = cycles per second phase difference Analog Signaling zrepresented by sine waves

12. 12 Digital Signaling zrepresented by square waves or pulses time (sec) amplitude (volts) 1 cycle frequency (hertz) = cycles per second

13. 13 Continuous & Discrete Signals

14. 14 Periodic Signals

15. 15 Sine Wave zPeak Amplitude (A) ymaximum strength of signal yvolts zFrequency (f) yRate of change of signal yHertz (Hz) or cycles per second yPeriod = time for one repetition (T) yT = 1/f zPhase (  ) yRelative position in time

16. 16 Varying Sine Waves Sin2πt0.5Sin2πt Sin4πt or Phase Shift in seconds Phase Shift in radians

17. 17 Wavelength ( ) zDistance occupied by one cycle zDistance between two points of corresponding phase in two consecutive cycles zAssuming signal velocity in space is equal to v y = vT or y f = v yHere, V=c = 3*10 8 ms -1 (speed of light in free space)

18. 18 Frequency Domain Concepts zA Signal is usually made up of many frequencies zComponents are sine waves zIt Can be shown (Fourier analysis) that any signal is made up of component sine waves zOne can plot frequency domain functions instead of/in addition to time domain functions

19. 19 Addition of Frequency Components (a) Sin(2πft) (b) (1/3)Sin(2π(3f)t) (c) (4/π)[Sin(2πft)+(1/3)Sin(2π(3f)t)]

20. 20 Frequency Domain Note: For square waves, only odd harmonics exist (plus the fundamental component of course). (a) Frequency domain function for s(t)=(4/π)[Sin(2πft)+(1/3)Sin(2π(3f)t)] (b) Frequency domain function for a single square pulse s(t)=1 for -X/2<t<X/2 Figure a is discrete because the time domain function is periodic. Figure b is continuous because the time domain function is aperiodic. See Figure 3.16 Page 103. Note that s(f) is of the form

21. 21 Communications Basics aRepresent a signal as a single-valued function of time, g(t), to model behavior of a signal (may be voltage, current or other change) aJean-Baptiste Fourier showed we can represent a periodic signal (given some conditions) as the sum of a possibly infinite number of sines and cosines Period = T g(t) = (1/2)c +  a n sin(2  nft) +  b n cos(2  nft) n=1 f = 1/T is fundamental frequency a & b coefficients are the amplitude of the n th harmonic This is a Fourier Series

22. 22 Time -> Harmonic spectrum Original As we add more harmonics the signal reproduces the original more closely

23. 23 aNo transmission facility can transmit signals without losing some power aUsually this attenuation is frequency dependent so the signal becomes distorted aGenerally signal is completely attenuated above some max frequency (due to medium characteristics or intentional filtering) aThe signal is bandwidth limited Signal Transmission

24. 24 aTime T necessary to transmit a character depends on coding method and signalling speed aSignaling speed = number of times per second the signal changes value and is measured in baud aNote that baud rate is not necessarily the same as the bit rate aBy limiting the bandwidth of the signal we also limit the data rate even if a channel is perfect aOvercome this by encoding schemes Signal Transmission

25. 25 Spectrum & Bandwidth zSpectrum yrange of frequencies contained in signal zAbsolute bandwidth ywidth of spectrum zEffective bandwidth yOften just bandwidth yNarrow band of frequencies containing most of the energy zDC Component yComponent of zero frequency

26. 26 Signal with DC Component (a) s(t)=1+(4/π)[Sin(2πft)+(1/3)Sin(2π(3f)t)]

27. 27 Data Rate and Bandwidth zAny transmission system has a limited band of frequencies zThis limits the data rate that can be carried See Figure 3.8 Page 79

28. 28 Bandwidth zWidth of the spectrum of frequencies that can be transmitted yif spectrum=300 to 3400Hz, bandwidth=3100Hz zGreater bandwidth leads to greater costs zLimited bandwidth leads to distortion zAnalog measured in Hertz, digital measured in baud

29. 29 BPS vs. Baud zBPS=bits per second zBaud=# of signal changes per second zEach signal change can represent more than one bit, through variations on amplitude, frequency, and/or phase

30. 30 Analog and Digital Data Transmission zData yEntities that convey meaning zSignals yElectric or electromagnetic representations of data zTransmission yCommunication of data by propagation and processing of signals

31. 31 Data zAnalog yContinuous values within some interval ye.g. sound, video zDigital yDiscrete values ye.g. text, integers

32. 32 Acoustic Spectrum (Analog)

33. 33 Signals zMeans by which data are propagated zAnalog yContinuously variable yVarious media xwire, fiber optic, space ySpeech bandwidth 100Hz to 7kHz yTelephone bandwidth 300Hz to 3400Hz yVideo bandwidth 4MHz zDigital yUse two DC components

34. 34 Digital Text Signaling zTransmission of electronic pulses representing the binary digits 1 and 0 zHow do we represent letters, numbers, characters in binary form? zEarliest example: Morse code (dots and dashes) zMost common current form: ASCII

35. 35 ASCII Character Codes zUse 8 bits of data (1 byte) to transmit one character z8 binary bits has 256 possible outcomes (0 to 255) zRepresents alphanumeric characters, as well as “special” characters

36. 36 Digital Image Signaling z Pixelization and binary representation Code: 00000000 00111100 01110110 01111110 01111000 01111110 00111100 00000000

37. 37 Data and Signals zUsually use digital signals for digital data and analog signals for analog data zCan use analog signal to carry digital data yModem zCan use digital signal to carry analog data yCompact Disc audio

38. 38 Why Study Analog? zTelephone system is primarily analog rather than digital (designed to carry voice signals) zLow-cost, transmission medium (present almost at all places at all times zIf we can convert digital information (1s and 0s) to analog form (audible tone), it can be transmitted inexpensively

39. 39 Voice Signals zEasily converted from sound frequencies (measured in loudness/db) to electromagnetic frequencies, measured in voltage zHuman voice has frequency components ranging from 20Hz to 20kHz zFor practical purposes, the telephone system has a narrower bandwidth than human voice, from 300 to 3400Hz

40. 40 Analog Signals Carrying Analog and Digital Data

41. 41 Digital Signals Carrying Analog and Digital Data

42. 42 Analog Transmission zAnalog signal transmitted without regard to content zMay be analog or digital data zAttenuated over distance zUse amplifiers to boost signal zAlso amplifies noise

43. 43 Digital Transmission zConcerned with content zIntegrity endangered by noise, attenuation etc. zRepeaters used zRepeater receives signal zExtracts bit pattern zRetransmits zAttenuation is overcome zNoise is not amplified

44. 44 Advantages of Digital Transmission zDigital technology yLow cost LSI/VLSI technology zData integrity yLonger distances over lower quality lines zCapacity utilization yEconomical high bandwidth links yHigh degree of multiplexing easier with digital techniques zSecurity & Privacy yEncryption zIntegration yCan treat analog and digital data similarly

45. 45 Transmission Media zthe physical path between transmitter and receiver zdesign factors ybandwidth yattenuation: weakening of signal over distances yinterference ynumber of receivers

46. 46 Impairments and Capacity zImpairments exist in all forms of data transmission zAnalog signal impairments result in random modifications that impair signal quality zDigital signal impairments result in bit errors (1s and 0s transposed)

47. 47 Transmission Impairments zSignal received may differ from signal transmitted zAnalog - degradation of signal quality zDigital - bit errors zCaused by yAttenuation and attenuation distortion yDelay distortion yNoise

48. 48 Transmission Impairments zAttenuation yloss of signal strength over distance zAttenuation Distortion ydifferent losses at different frequencies zDelay Distortion ydifferent speeds for different frequencies zNoise

49. 49 Attenuation transmitter receiver P 1 watts P 2 watts Attenuation 10 log 10 (P 1 /P 2 ) dB Amplification 10 log 10 (P 2 /P 1 ) dB

50. 50 Attenuation zSignal strength falls off with distance zDepends on medium zReceived signal strength: ymust be enough to be detected ymust be sufficiently higher than noise to be received without error zAttenuation is an increasing function of frequency

51. 51 Delay Distortion zOnly in guided media zPropagation velocity varies with frequency

52. 52 Noise (1) zAdditional signals inserted between transmitter and receiver zTypes of Noise: zThermal yDue to thermal excitement of electrons yUniformly distributed, cannot be eliminated yWhite noise zIntermodulation ySignals that are the sum and difference of original frequencies sharing a medium

53. 53 Noise (2) zCrosstalk yA signal from one line is picked up by another zNEXT (near-end crosstalk) yinterference in a wire at the transmitting end of a signal sent on a different wire zFEXT (far-end crosstalk) yinterference in a wire at the receiving end of a signal sent on a different wire zImpulse yIrregular pulses or spikes ye.g. External electromagnetic interference yShort duration yHigh amplitude yLess predictable

54. 54 Noise zEffect ydistorts a transmitted signal yattenuates a transmitted signal zsignal-to-noise ratio to quantify noise S/N db =10 log S= average signal power N= noise power SNSN

55. 55 Effect of noise Signal Noise Signal+Noise 0 1 1 1 1 0 0 0 0 1 Data Received Sampling times Bit error 0 1 0 1 1 0 0 1 0 1 Original data Logic Threshold

56. 56 Channel Capacity zData rate yIn bits per second yRate at which data can be communicated zBandwidth yIn cycles per second of Hertz yConstrained by transmitter and medium

57. 57 Maximum Data Rate aIn 1920s Nyquist (of the Nyquist Theorem) developed an equation for the maximum data rate of a noiseless channel yFor low pass filtered signal of bandwidth B ySampling at exactly 2B samples per sec allows reconstruction of the signal yMore samples are useless since the frequencies above B are filtered out C=Capacity=max data rate = 2B log 2 M bits/sec for M discrete levels

58. 58 Nyquist theorem “ In a perfectly noiseless channel, if f is the maxmimum frequency the medium can transmit, the receiver can completely reconstruct a signal by sampling it 2*f times per second” Nyquist, 1920

59. 59 Nyquist formula M Max data rate (C) 2 6200 bps 4 12400 bps 8 18600 bps 16 24800 bps M Max data rate (C) 2 6200 bps 4 12400 bps 8 18600 bps 16 24800 bps C = 2B log 2 M B = bandwidth M = number of discrete signal levels Theoretical capacity for Noiseless channel Example: Channel capacity calculation for voice bandwidth (~3100 Hz):

60. 60 aIn the ‘40s Shannon (of Shannon’s Law) extended the equation to a channel subject to thermodynamic (thermal) noise aThermal noise measured by ratio of signal (S) power to noise (N) power (signal-to-noise ratio - S/N) aBut represented as: 10 log 10 S/N aThese units are called decibels (dB) aNow, for a channel with signal to noise of S/N Capacity=C=max bits/sec = B log 2 (1 + S/N) Shannon’s Law Here, C=Theoretical Maximum capacity with noise Note: Only much lower rates are achieved since the equation assumes zero impulse noise and no attenuation and delay distortion.

61. 61 Bit rate and Baud rate zBit rate number of bits that are transmitted in a second zBaud rate number of line signal changes (variations) per second If a modem transmits 1 bit for every signal change bit rate = baud rate If a signal change represents 2 or more or n bits bit rate = baud rate *n