Turbo codes.ppt

1. Turbo Codes Prasanta Kumar Barik Computer Science & Engg . Regd No-0701106246

2. Agenda Project objectives and motivations Channel Coding Turbo Codes Technology Turbo Codes Performance Turbo Coding Application Conclusion

3. Communication System Structural modular approach Various components Of defined functions Channel Coding Source Coding Modulation Formatting Digitization Multiplexing Access techniques send receive

4. Channel Coding To encode the information sent over a communication channel in such a way that in the presence of channel noise, errors can be detected and/or corrected. Can be categorized into Backward error correction (BEC) Forward error correction (FEC ) Objective: provide coded signals with better distance properties

5. Types of coding Block coding Convolutional coding : codes differ from block codes in the sense that they do not break the message stream into fixed-size blocks. Instead redundancy is added continuously to the whole stream. The encoder keeps M previous input bits in memory. Each output bit of the encoder then depends on the current input bit as well as the M stored bits.

6. Structured Redundency Channel encoder Input word k-bit Output word n-bit Redundancy = (n-k) Code rate = k/n codeword Code sequence

7. A Need for Better Codes Energy efficiency vs Bandwidth efficiency Codes with lower rate (i.e. bigger redundancy) correct more errors.then communication system can operate with a lower transmit power, transmit over longer distances, tolerate more interference, use smaller antennas and transmit at a higher data rate. These properties make the code energy efficient. low-rate codes have a large overhead and are hence more heavy on bandwidth consumption. Also, decoding complexity grows exponentially with code length.

8. Shannon Theory For every combination of bandwidth (W), channel type, signal power (S) and received noise power (N), there is a theoretical upper limit on the data transmission rate R, for which error-free data transmission is possible. This limit is called channel capacity or also Shannon capacity. sets a limit to the energy efficiency of a code .

9. A decibel is a relative measure. If E is the actual energy and Eref is the theoretical lower bound , then the relative energy increase in decibels is   .   Since ,   A twofold relative energy increase equals 3dB.  

10. Turbo codes Turbo codes are a class of error correcting codes codes introduced in 1993 that come closer to approaching Shannon’s limit than any other class of error correcting codes. Turbo codes achieve their remarkable performance with relatively low complexity encoding and decoding algorithms.

11. Turbo Encoder Input RSC RSC Interleaver Systematic codeword random X Y1 Y2

12. Recursive Systematic Coders Copy of the data in natural order Recursive S1 S2 S3 Data stream Systematic Calculated parity bits

13. Interleaver The interleaver’s function is to permute low weight code words in one encoder into high weight code words for the other encoder. A “row-column” interleaver : data is written row-wise and read columnwise.While very simple, it also provides little randomness. A “helical” interleaver : data is written row-wise and read diagonally.   An “odd-even” interleaver : first, the bits are left uninterleaved and encoded, but only the odd-positioned coded bits are stored. Then, the bits arescrambled and encoded, but now only the even-positioned coded bits arestored . Odd-even encoders can be used, when the second encoder producesone output bit per one input bit .

14. INPUT X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15

15. Helical interleaver output X11 X7 X3 X14 X10 X1 X12 X8 X4 X15 X6 X2 X13 X9 X5 Row-column interleaver output X1 X6 X11 X2 X7 X12 X3 X8 X13 X4 X9 X14 X5 X10 X15

16. Odd-even interleaver output Encoder output without interleaving X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 Y1 - Y3 - Y5 - Y7 - Y9 - Y11 - Y13 - Y15 Encoder output with row-column interleaving X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 - Z6 - Z2 - Z12 - Z8 - Z4 - Z14 - Z10 - Final output of the encoder Y1 Z6 Y3 Z2 Y5 Z12 Y7 Z8 Y9 Z4 Y11 Z14 Y13 Z10 Y15

17. Turbo Decoding Criterion For n probabilistic processors working together to estimate common symbols, all of them should agree on the symbols with the probabilities as a single decoder could do

18. Turbo Decoder

19. Turbo Decoder The inputs to the decoders are the Log likelihood ratio (LLR) for the individual symbol d. LLR value for the symbol d is defined ( Berrou) as

20. Turbo Decoder The SISO decoder reevaluates the LLR utilizing the local Y1 and Y2 redundancies to improve the confidence The value z is the extrinsic value determined by the same decoder and it is negative if d is 0 and it is positive if d is 1 The updated LLR is fed into the other decoder and which calculates the z and updates the LLR for several iterations After several iterations , both decoders converge to a value for that symbol.

21. Turbo Decoding Compare the LLR output, to see if the estimate is towards 0 or 1 then take HD

22. How Do they Work (© IEEE spectrum)

23. How Do they Work (© IEEE spectrum)

24. Turbo Codes Performance

25. Turbo Codes Applications Deep space exploration Mobile 3G systems In use in Japan UMTS

26. Conclusion : End of Search Turbo codes achieved the theorical limits with small gap Give rise to new codes : Low Density Parity Check (LDPC) Need Improvements in decoding delay

27. Reference http://www.google.com [2] University of South Australia, Institute for Telecommunications Research,Turbo coding research group. http://www.itr.unisa.edu.au/~steven/turbo/. [3] S.A. Barbulescu and S.S. Pietrobon . Turbo codes: A tutorial on a new class of powerful error correction coding schemes. Part I: Code structures and interleaverdesign . J. Elec. and Electron.Eng ., Australia , 19:129–142, September 1999.

28. Thank You….. 