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Performance of Wireless Vs Performance of Wire-line communication systems

How SNR of same BER changes in wireless and wire-line communication system

CedrickManirafasha
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Performance of Wireless Vs Performance of Wire-line communication systems

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  1. Performance of Wireless Vs Performance of Wire-line communication Systems Manirafasha Cedrick M.Tech Communication Systems MANIRAFASHA 1

  2. Wireless Communication • Broadcast and reception of electromagnetic waves • These waves are characterized by either their frequency (f) or their wavelength (λ) • In a vacuum, the speed of propagation of these waves (c) is same as that of light c= λ x f Where c= 3 x 108m/s MANIRAFASHA 2

  3. Electromagnetic spectrum MANIRAFASHA 3

  4. Radio propagation mechanisms • Reflection: When the propagating radio wave hits an object which is very large compared to its wavelength, the wave gets reflected by the object and there is a phase shift of 180 degrees. Ex: Wall • Diffraction: The wave bends at the edges of the object (impenetrable objects) Ex: Edge of a building • Scattering: When wave travels through a medium which contains many objects with dimensions small when compared to its wavelengths. Ex: Tree leaves MANIRAFASHA 4

  5. Characteristics of the wireless channel • 1) Path loss: Ratio of the transmitted signal to the power of the same signal received by the intended receiver. • 2) Fading: Fluctuations in signal strength at the receiver • 3) Interference: – Constructive: Enhances the signal’s amplitude – Destructive: Attenuates the signal’s amplitude • 4) Doppler Shift: Change in frequency • 5) Transmission Rate Constraints – Nyquist’s Theorem – Shannon’s Theorem MANIRAFASHA 5

  6. Model for multipath propagation y(t) x(t) Output Input h(t) • Any path of wireless environment is characterized by – Delay ζi – Attenuation ai MANIRAFASHA 6

  7. Multipath scenario • 0thpath a0,ζ0 • 1stpath a1,ζ1 … • (L-1)thpath aL-1,ζL-1 Multipath response= sum of individual responses • h(t)=a0δ(t-ζ0) + a1δ(t-ζ1) +…+ aL-1δ(t-ζL-1) a0δ(t-ζ0) a1δ(t-ζ1) aL-1δ(t-ζL-1)  1 L h(t)=     ( ) a t i i  0 i MANIRAFASHA 7

  8. Transmitted signal   2 j F t ( ) Re{ ( ) } S t S t e c p Where, Sp= Passband signal S(t)= Complex baseband signal Fc= Carrier frequency       2 ( ) j Fc t th 0 Re{ ( ) } a S t e 0 0 0 ...    ) 1     2 ( ) j Fc t th ( Re{ ( } L a S t e  1 L  ) 1  1 L L MANIRAFASHA 8

  9. Receiver pass band signal • The received pass band signal is the sum of various multipath components  1 L   i       2 ( ) j Fc t ( ) Re{ ( ) } yp t a S t e i i i 0 ...  1 L   i       2 ( ) j Fc t ( ) ( ) y t a S t e i i i 0 Complex baseband received signal Maximum frequency (Fm) << Carrier Frequency (Fc) Complex phase factor MANIRAFASHA 9

  10. Narrowband assumption • Fm<<Fc    ( ) ( ) S t S t i  1 L       2 j F ( ) ( ) ( ) y t a e S t c i i 0  i  ( ) ( ) y t h S t Where h= complex coefficient It depends on the attenuation and ai,ζi MANIRAFASHA 10

  11. Destructive interference 1  i      2 Fc i h a e j i  0         2 2 j Fc j Fc h a e a e 0 1 0 1 1 F       , 1 , 1 a a o o 0 1 2 c 1    2 j Fc Fc   e 0 1 1 h e e 2    t    ) 1  S  j 1 1 hS 1 ( 0 h     ( ) ( ) 0 ( ) 0 y t t • Destructive interference => Received signal =0 • Different multipath components cancel each other MANIRAFASHA 11

  12. Constructive interference 1 F       , 1 , 1 a a o o 0 1 c 1    2 j Fc   0 1 1 h e  e Fc       j 1 1 hS 1 1 S 2 h e    ( ) ( ) 2 ( ) y t t t • In constructive interference, multipath components are added constructively • h magnitude changes with time – Where h= Fading channel coefficient MANIRAFASHA 12

  13. Bit Error Rate (BER) • Metric which can be employed to characterize the performance of a communication system • If 10,000 bits are sent and 100 are received in error, • BER is 01 . 0 100 10000 100 1   • The range of the probability of bit error is 0  5 . 0  eP MANIRAFASHA 13

  14. BER • In Binary Phase Shift Key (BPSK) we have – 0 modulated as √P – 1 modulated as -√P and P=Average power of modulation • y= x + n – x= transmitted signal – n= Noise (additive) – If n is white Gaussian, then y=Additive White Gaussian Noise (AWGN) • Probability of error = P(n > √P) MANIRAFASHA 14

  15. BER 2    n 1  2  e dn 2 2 2 P n     t dn dt    2  t 1 e dt 2  2 P  P  ( ) Pe Q  2 Probability of Bit Error in AWGN for BPSK modulated transmission of average power=P  ( ) BER Q SNR P SNR= Signal to Noise Ratio  SNR  2 MANIRAFASHA 15

  16. • Ex 1) With SNR= 10 dB, what is BER for AWGN with BPSK? 10 log 10  SNR  SNR dB 10 log 1  SNR SNR  Q 1 10 10     4 ( ) 82 . 7 10 Pe    4 472 . 2 10 Q • Ex2) What is SNR required for BER= 10-6? 1 6 2   1 1  SNR SNR     10 6 10 2 e e 2  2       6 2 ln 2 10 26  24 . SNR  10 log ( 26 24 . ) 14 19 . SNR dB 10 dB MANIRAFASHA 16

  17. BER for wireless communication systems In wireless communication there is fading due to multipath • • y=hx+n h=fading coefficient, n= AWGN with variance=0, y= Received signal, x= Transmitted signal . P h a h ae h P h      2 2  2 a P 2 a  P   2 SNR a SNR F 2   2 ( ) ( ) Q a SNR F a da A 0 2   a ( ) 2 F a ae A 1 SNR    1 ( ) AverageBER 2 2 SNR MANIRAFASHA 17

  18. • Ex 3) Compute BER for wireless communication system for SNR=20dB 1 1 1 SNR  SNR       6 10 1 ( ) 2 2 2 2 2 SNR SNR 1  1 1 SNR  SNR       6 6 10 10 2 2 2 2 2 SNR 1 (  SNR 1 SNR     6 2 ( ) 10 ) 4 2 2  SNR 2   1 (   6 2 2 10 )    5 . 4 99 10 SNR    6 1  1 ( 2 10 2 )  10 log 56 98 . SNR SNR dB 10 dB MANIRAFASHA 18

  19. • For AWGN (Wire-line) channel, the SNR required to achieve BER=10-6is 14.19dB • For a wireless channel, the SNR required to achieve BER=10-6is 56.98dB • 56.98dB-14.19dB=43dB • For BER=10-6, 43dB extra SNR is required • Wireless communications consumes more power • In wireless, BER is proportional to 1/2SNR • In wire-line, BER decreases exponentially with respect to SNR MANIRAFASHA 19

  20. • There are 2 expressions for BER in wireless communications 1 SNR    1 ( ) BER Exact 2 2 SNR 1  BER Approximate 2 SNR MANIRAFASHA 20

  21. Thank you MANIRAFASHA 21

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