Physical Layer Yan Wang 2 CS 428/528 Computer Networks Analog - PowerPoint PPT Presentation

Physical Layer Yan Wang

2 CS 428/528 Computer Networks Analog vs. Digital Data • Means by which information is represented • Analog ▫ Continuous values ▫ Voice, video, etc. • Digital ▫ Discrete values ▫ ASCII data, numeric data etc.

3 CS 428/528 Computer Networks Data Transmission • A signal is an electrical or electromagnetic encoding of data • Signaling is the act of propagating a signal along a medium ▫ guided media: signals are sent along a physical path (e.g., wire, cable, fiber) ▫ unguided media: signals are broadcast (e.g., air, vacuum) • A guided medium may be either ▫ point – to – point : direct link between two devices ▫ multipoint : more than two devices share the medium

4 CS 428/528 Computer Networks A Mathematical View of Signals • A signal is a function of time: x(t) ▫ A signal x(t) is periodic if and only if x(t +T) = x(t), for - ∞< t < ∞ ▫ Otherwise, it is aperiodic ▫ A signal x(t) is analog if it has infinite possibilities ▫ Otherwise, it is digital

5 CS 428/528 Computer Networks Examples of Aperiodic Signals

6 CS 428/528 Computer Networks Periodic Analog Signals - Sinusoidal Waves • Amplitude: the value of the signal at a time ▫ Peak Amplitude (A): maximum strength of signal ▫ volts • Frequency (f) ▫ Rate of change of signal ▫ Hertz (Hz) or cycles per second ▫ Period = time for one repetition (T) ▫ T = 1/f • Phase ( Φ ) ▫ Relative position in time

7 CS 428/528 Computer Networks Analog vs. Digital Signals • Means by which data are propagated • Analog ▫ Continuously vary ▫ Various media ▫ wire, optic fiber, air • Digital ▫ Dis-continuously vary ▫ Use direct current component • Different characteristics in transmission ▫ Analog – less distortion, more sensitive to noise ▫ Digital – larger distortion, less sensitive to noise

8 CS 428/528 Computer Networks Varying Sine Wave: s(t) = A sin(2 π ft + Φ ) s(t) = A/2 sin(2 π ft + Φ ) s(t) = A sin(2 π f/2t + Φ )s(t) = A sin(2 π ft + Φ /2)

9 CS 428/528 Computer Networks Periodic Digital Signals – Square Waves (Pulses) • Amplitude ▫ Volts ▫ On/OFF – High/Low volts • Frequency (f) ▫ Rate of change of signal ▫ Hertz (Hz) or cycles per second ▫ Period = time for one repetition (T) ▫ T = 1/f • No phase

10 CS 428/528 Computer Networks Ideal Digital Signals - Square Waves

11 CS 428/528 Computer Networks Real Square Waves

12 CS 428/528 Computer Networks Fourier Analysis • Any periodic signal can be represented as a sum of sinusoids, known as Fourier series:          x ( t ) a a cos( 2 nf t ) b sin( 2 nf t ) 0 n 0 n 0   n 1 n 1 where 1 T   a x ( t ) dt 0 T 0 2  T   a x ( t ) cos( 2 nf 0 ) t dt n T 0 2  T   b x ( t ) sin( 2 nf 0 ) t dt n T 0

13 CS 428/528 Computer Networks Square Wave with An Increasing Number Of Harmonics By Peretuset (Own work) [GFDL (http://www.gnu.org/copyleft/fdl.html) or CC BY 3.0 (http://creativecommons.org/licenses/by/3.0)], via Wikimedia Commons

14 CS 428/528 Computer Networks Some Terminologies • The spectrum of a signal is the range of frequencies that it contains • The absolute bandwidth is the width of the spectrum ▫ The absolute bandwidth of the square wave is infinite • Due to the limitations of real-world media, a signal must be represented in a limited band of frequencies. This band is referred to as the effective bandwidth , or just bandwidth . • The exact range of this “limited band” is largely an engineering issue

15 CS 428/528 Computer Networks Examples • Consider a square wave x(t) whose fundamental frequency f=1M Hz. • If the representation of x(t) by harmonics 1f+3f+5f is good enough, then the (effective) bandwidth of x(t) is 5M - 1M = 4M Hz. • A more faithful representation that uses up to 9f will have the bandwidth of 9M-1M = 8M Hz.

16 CS 428/528 Computer Networks Bandwidth of Human Voice • Typically, a baby can hear from 20 Hz to 20 KHz. • Many adults are not as capable. • Speech bandwidth 100Hz to 7KHz • Voice telephone systems pass frequencies from 300 Hz to 3300 Hz ▫ a transmission medium meeting this specification is called voice grade.

17 CS 428/528 Computer Networks Discussion 3 • Why use twisted pair cable in Ethernet cable? T-568B Straight-Through Ethernet Cable T-568B Straight-Through Ethernet Cable

18 CS 428/528 Computer Networks Twisted Cabling • Patented by the Bell in 1881 • A pair of cable counter-clock wise twisted together can reduce the Electromagnetic Interference (EMI) from external sources without shields • Use differential mode transmission

19 CS 428/528 Computer Networks Transmission Impairment (1) • Signal received may differ from signal transmitted • Analog - degradation of signal quality • Digital - bit errors • Caused by ▫ Attenuation and attenuation distortion ▫ Delay distortion ▫ Noise

20 CS 428/528 Computer Networks Transmission Impairment (2) • Attenuation ▫ signal strength falls off with distance ▫ attenuation increases with frequency ▫ depends on medium • Received signal strength: ▫ must be enough to be detected ▫ must be sufficiently higher than noise to be received without error

21 CS 428/528 Computer Networks Transmission Impairment (3) • Delay distortion ▫ Only in guided media ▫ Different frequency components propagate at different speeds over guided media • Noise ▫ Additional signals inserted between transmitter and receiver ▫ Thermal: due to thermal agitation of electrons ▫ cross talk: unwanted coupling between parallel signal paths ▫ impulse noise: due to, for example, lighting

22 CS 428/528 Computer Networks Transmission Impairment (4) • Signal-to-Noise ratio is measured in decibel s: signal power   ( S / N ) 10 log dB 10 noise power • Consequences ▫ limited data rate or limited distance ▫ errors in transmission inevitable

23 CS 428/528 Computer Networks Shannon Theorem   maximum data rate H log ( 1 S / N ) bits/sec 2 • Notice that we need the direct S/N ratio (not in decibel) in the formula. • Example: in voice telephone system, H=3300Hz-300Hz=3000Hz, suppose S/N dB =30 ▫ S/N = ? ▫ Max data rate = ? • Shannon’s theorem gives an upper bound of the channel capacity

24 CS 428/528 Computer Networks Analog vs. Digital Transmission • Analog data, analog signals ▫ Traditional telephone networks • Analog data, digital signals ▫ Modern telephone networks, musical CD • Digital data, analog signals ▫ Modem • Digital data, digital signals ▫ File exchanges in LANs

25 CS 428/528 Computer Networks Analog Signal/Transmission • Continuously varying signal • Can be used to transmit analog/digital data • Use amplifiers to boost energy in signal due to attenuation • Amplification distorts analog signal because noise is also amplified Sender Receiver Amplifier signal signal weakened amplified, and distorted including the over distance distortion

26 CS 428/528 Computer Networks Digital Signal/Transmission • (ideally) Sequence of discrete values • Can be used to transmit analog/digital data • Repeaters are used to restore signal periodically • Repeaters do not disturb the signal (and data) • Digital transmission is the future Sender Receiver Repeater 0,1 signals 0,1 signals weakened reproduced and distorted with full over distance strength

27 CS 428/528 Computer Networks Encoding: Digital Data, Digital Signals • Data Rate : number of bits/bytes transmitted per second – D ▫ Bit duration = 1/D • Modulation rate (bauds) : the rate at which the signal is changed, i.e., signal elements per second – M ▫ What is the relationship between D and M? • Encoding : mapping from data bits to signal elements ▫ NRZ, NRZI, Manchester, Differential Manchester, Delay Modulation, etc.

28 CS 428/528 Computer Networks Non-return to Zero (NRZ) Encoding • a positive voltage represents 1; a negative 0 • easy to implement • efficient use of bandwidth (modulation rate equals data rate in worst cases) • Problem: no synchronization available from signal ▫ Consider sending 1,000 consecutive 0s or 1s 1 0 1 1 0 0 0 1 1 0 1

29 CS 428/528 Computer Networks Non-return to Zero Inverted • Non-return to zero inverted on ones • Constant voltage pulse for duration of bit • Data encoded as presence or absence of signal transition at beginning of bit time • Transition (low to high or high to low) denotes a binary 1 • No transition denotes binary 0 • An example of differential encoding • Good for 1’s, bad for 0’s 0 1 1 0 1 0 0 1 0 1 1

30 CS 428/528 Computer Networks Manchester Encoding • In the middle of a bit period, a downward transition represents 1; an upward represents 0 • At least one transition per bit ▫ Self-clocking/synchronization; error detection • Problem: bit rate is half the baud rate 0 0 1 1 0 0 1 1 0 1

31 CS 428/528 Computer Networks Combined Example