1. Performed by : Nidhi Gupta, 63/EC/07 Rupinder Singh, 83/EC/07 Siddi Jai Prakash, 101/EC/07 Electronics and Communication Division Netaji Subhas Institute of Technology, Delhi PERFORMANCE ANALYSIS OF FREE SPACE OPTICAL (FSO) COMMUNICATION USING DIFFERENT CODING SCHEMES Mentored by: Prof. Subrat Kar Dept. of Electrical Engineering IIT Delhi Dr. S.P. Singh Electronics and Communication Division Netaji Subhas Institute of Technology, Delhi

2. OBJECTIVE To design a communication system between earth and a geo Satellite with free space as the channel. Aperture averaging – Aperture effects at the transmitter Adaptive Optics - Use of collimating lenses at transmitter and receiver Spatial Diversity – Using a number of transmitters arranged horizontally or vertically Error Control Coding – Using ECC, for data transmission.

3. METHODOLOGY Source used was a simple coherent laser light. No source coding was used Different channel coding schemes namely, Convolutional, LDPC and RS were used M-PPM and OOK modulation techniques were used. Channel was the free space with different turbulence conditions The detection at the receiver side was direct.

4. Features – High Data Bandwidth of 1 Gbps and above – Low BER, High SNR – Narrow Beam Size – Power Efficient and Data Security – Cheap – Quick to deploy and redeploy – Channel Impairments like Dispersion, Scattering, Turbulence Free Space Optical Communication (FSO)

5. Terrestrial FSO Block Diagram

6. Comparative Study of Fiber Optical Cable and FSO Communication

7. ERROR CONTROL CODING TECHNIQUES

8. Figure Convolutional Encoder Convolutional codes are performed on bit to bit basis.  m-bit information symbol (each m-bit string) to be encoded is transformed into an n- bit symbol, where m/n is the code rate (n ≥ m)  transformation is a function of the last k information symbols, where k is the constraint length of the code, using the generator matrix. CONVOLUTIONAL CODES

9. Implementation in MATLAB Encoding using the function ‘convenc’ using a trellis structure ‘trellis’  [msg_enc_bi, stateEnc] = convenc(msg_orig, trellis, stateEnc) Decoding using the function ‘vitdec’ and ‘hard’ decoding  [msg_dec, metric, stateDec, in] = vitdec(msg_demod_bi(:), trellis, tblen, 'cont', 'hard', metric, stateDec, in)

10. Low Density Parity Check Codes (LDPC) ENCODING IN MATLAB MATLAB has a fixed size of sparse matrix 32400 x 64800 Hence, we generate our own custom sparse matrix. We then generate Parity Check bits using LU decomposition of sparse matrix Finally, we solve for c in L(Uc) = B.s, where H = [A|B], s = input vector

11. Decoding -The Optimized Algorithm There are three key variables in the algorithm: L(r ji ), L(q ij ), and L(Q i ). L(q ij ) is initialized as L(q ij ) = L(c i ). For each iteration, update L(r ji ), L(q ij ), and L(Q i ) using the following equations: At the end of each iteration, L(Q i ) provides an updated estimate of the log-likelihood ratio for the transmitted bit c i. The soft-decision output for c i is L(Q i ). The hard-decision output for c i is 1 if L(Q i ) < 0, and 0 else.

12. REED SOLOMON CODES Figure A pictorial representation of the transmitted bits after Reed Solomon Encoding. For Reed- Solomon codes, the code minimum distance is given by dmin = n - k + 1 The code is capable of correcting any combination of t or fewer errors, where t can be expressed as In the simulation, we have used M = 5 is the no. of bit sequences in a symbol, K = 127 is the number of data symbols being encoded, and N = 255 is the total number of code symbols in encoded block. Therefore, Code Rate = 127/255 ~ 0.5

13. MODULATION SCHEMES

14. Adaptive OOK

15. PPM M-PPM

16. COMMUNICATION CHANNEL

17. Communication Channels Lognormal: For standard devaitions between 0.001 to 0.6 Gamma-Gamma: for higher standard deviations:

18. MONTE CARLO SIMULATIONS Monte Carlo simulation performs analysis by building models of possible results by substituting a range of values—a probability distribution—for any factor that has inherent uncertainty

19. coding gain is the measure in the difference between the signal to noise ratio (SNR) levels between the uncoded system and coded system required to reach the same bit error rate (BER) levels when used with the error correcting code (ECC). CODING GAIN

20. RESULTS 4-PPM under Lognormal (var=0.1)

21. 4-PPM under Lognormal (var=0.01)

22. 4-PPM under Lognormal (var=0.001)

23. OOK under Lognormal (var=0.1)

24. OOK under Lognormal (var=0.01)

25. OOK under Lognormal (var=0.001)

26. 4-PPM under Gamma-Gamma (var=0.1)

27. 4-PPM under Gamma-Gamma (var=0.01)

28. 4-PPM under Gamma-Gamma (var=0.001)

29. OOK under Gamma-Gamma (var=0.1)

30. OOK under Gamma-Gamma (var=0.01)

31. OOK under Gamma-Gamma (var=0.001)

32. Effect of PPM index on curve (LDPC)

33. Effect of Coding Rate on Coding Gain

35. Recommendation for Future Work One can investigate the effects of Medium turbulence channels, namely gamma-gamma using Accept Reject Method. High turbulence channels, namely exponential on the BER in the communication link. Implement more coding techniques like Turbo coding and Trellis coded modulation (TCM).

36. References: M. Karimi, M. Nesiri-Kenari, “BER Analysis of Cooperative Systems in Free-Space Optical Networks” J. of Lightwave Technology, vol. 27, no. 24, pp. 5637-5649, Dec 15, 2009 E. W. B. R. Strickland, M. J. Lavan, V. Chan, “Effects of fog on the bit-error rate of a free space laser Communication system,” Appl.Opt., vol. 38, no. 3, pp. 424–431, 1999. M. Uysal, J. Li, and M. Yu, “Error rate performance analysis of coded free-space optical links over gamma -gamma atmospheric turbulence channels,” IEEE Trans. Wireless Communication, vol. 5, no. 6, pp.1229–1233, 2006. L. Andrews, R. Phillips, C. Hopen, Laser Beam Scintillation With Applications. New York: SPIE Press, 2001. Bernard Sklar, Reed – Solomon Codes. Stephen B. Wicker, Vijay K. Bhargava, “An introduction to Reed Solomon Codes. Ghassemlooy, Z. And Popoola, W.O Terrestrial Free Space optical Communication. Gallager, Robert G., “Low-Density Parity-Check Codes”, Cambridge, MA, MIT Press, 1963. Amin Shokrollahi, “LDPC Codes: An introduction”, Digital Fountain, Inc, April 2, 2003 Henk Wymeersch, Heidi Steendam and Marc Moeneclaey, DIGCOM research group, TELIN Dept., Ghent University, “Log-domain decoding of LDPC codes over GF(q)”.