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Suayb S. Arslan, Pamela C. Cosman and Laurence B. Milstein

Progressive Source Transmissions using Joint Source-Channel Coding (JSCC) and Hierarchical Modulation in Packetized Networks. Suayb S. Arslan, Pamela C. Cosman and Laurence B. Milstein. Department of Electrical and Computer Engineering University of California, San Diego. 12/03/2009

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Suayb S. Arslan, Pamela C. Cosman and Laurence B. Milstein

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  1. Progressive Source Transmissions using Joint Source-Channel Coding (JSCC) and Hierarchical Modulation in Packetized Networks Suayb S. Arslan, Pamela C. Cosman and Laurence B. Milstein Department of Electrical and Computer Engineering University of California, San Diego 12/03/2009 Hilton Hawaiian Village, Honolulu, Hawaii, USA Preliminary Exam

  2. Outline • Progressive Sources • UEP techniques • A new UEP technique based on packetization • System Model & Optimization • Numerical Results • Conclusion & Future work Preliminary Exam IEEE GLOBECOM 2009

  3. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion & Future work Progressive Source Compression • Ex: SPIHT image compression algorithm [SPIHT ‘96]. 4% gives you only a brief description of the source. Preliminary Exam IEEE GLOBECOM 2009

  4. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Progressive Source Compression • 20% is good enough to say what the picture looks like. Preliminary Exam IEEE GLOBECOM 2009

  5. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Progressive Source Compression • At 40%, it begins to refine the image. Preliminary Exam IEEE GLOBECOM 2009

  6. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Progressive Source Compression • At 100%, it gives more refinement but no major difference from 40%. Preliminary Exam IEEE GLOBECOM 2009

  7. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Progressive Source Compression • We consider progressive type of encoders. (Embedded image encoders: EZW, SPIHT, etc…) • Result: Very sensitive to bit errors. • Protection and performance improvement is achieved by channel coding. • UEP: Unequal error protection is beneficial for progressively encoded sources. This can be provided by several known techniques. Preliminary Exam IEEE GLOBECOM 2009

  8. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Error Propagation • If we do not truncate, we end up with error propagation: Single bit is in error 0.25bpp=65536bits. 1500th bit position Preliminary Exam IEEE GLOBECOM 2009 03/05/2009

  9. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Error Propagation • If we do not truncate, we end up with error propagation: Single bit is in error 0.25bpp=65536bits. 1500th bit position 20000th bit position Preliminary Exam IEEE GLOBECOM 2009

  10. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Error Propagation • If we do not truncate, we end up with error propagation: Single bit is in error 0.25bpp=65536bits. No error 0.075bpp=19660bits 20000th bit position Preliminary Exam IEEE GLOBECOM 2009

  11. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Unequal Error Protection:(a) Joint Source Channel Coding • Different channel codes per packet. Each packet has different significance in terms of the end reconstruction quality of the source. • For a given packet size, amount of information bits and parity bits are subject to a trade off. • Optimal allocation is studied in literature [Lu ‘98]. Preliminary Exam IEEE GLOBECOM 2009

  12. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Unequal Error Protection:(b) Hierarchical Modulation • HP: High Priority • LP: Low Priority • is hierarchical modulation parameter. • HP and LP bits have different BERs even if . • It is used in DVB-T standard and many other hot spots. • It is previously considered in an image transmission scenario [Morimoto ‘96]. Preliminary Exam IEEE GLOBECOM 2009

  13. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Unequal Error Protection:(c) Packetization & Channel Coding • Two different packetization strategies: Sequential Packetization (SP) and Folded Packetization (FP). • BL bits are HP bits and are more important than EL bits, LP bits. • Since HP and LP BERs are different, by using FP we give more protection to the first half of the packets than the second half. SP FP Preliminary Exam IEEE GLOBECOM 2009

  14. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Unequal Error Protection:(c) Packetization & Channel Coding - 2 • We obtain the packets of bits after CRC and channel coding. We combine them using SP and FP, modulate and produce packets of symbols as shown in a) and b). Preliminary Exam IEEE GLOBECOM 2009

  15. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Unequal Error Protection:(c) Packetization & Channel Coding - 3 Preliminary Exam IEEE GLOBECOM 2009

  16. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion System Model - 1 Preliminary Exam IEEE GLOBECOM 2009

  17. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion System Model - 2 • Define the following vectors: • Where is the distortion up to and including packet l. And is the code rate protecting the first half, and is the code rate protecting the second half. • For a given , we can determine . It is used in our optimization algorithm to find optimal hierarchical parameters: • Then we optimize over all ( , ) pairs. Preliminary Exam IEEE GLOBECOM 2009

  18. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Upper bounds for coded Hierarchical System • We need to derive BER/PER expressions for coded hierarchical system. • We show that Viterbi hard decision upper bounds for BSC can be extended to any hierarchical system with L priority layers. • Upperbounds are not tight, thus we use approximate but more accurate BER/PER curves. They are obtained by using nonlinear regression methods. Preliminary Exam

  19. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Optimization - 1 • We adopt distortion optimal approach. Average distortion function is given by • is SNR, is the probability that i-th packet is correctly received. N: Total number of packets. • Bounded constrained optimization problem is: Preliminary Exam IEEE GLOBECOM 2009

  20. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Optimization - 2 • It can be converted into an optimization problem with inequality constraints: • are upper and lower bounds for . Preliminary Exam IEEE GLOBECOM 2009

  21. Lagrangian function is given by: are Lagrangian multipliers. Unconstrained minimization problem is In trying to solve this problem, we need to solve a set of non linear equations: We use numerical techniques. • Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Optimization - 3 Preliminary Exam IEEE GLOBECOM 2009

  22. We proved the following proposition for the convexity of the cost function: • Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Optimization - 4 • We also proved and justified the following theorem: Preliminary Exam IEEE GLOBECOM 2009

  23. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Numerical Results – Simulation Parameters • Modulation: H-4PAM. • RCPC Code using Generator polynomial ¼ mother code : [117 127 155 171] in octal notation. Chosen from [Hagg ‘97], memory 6. • A (Lx X Ly) Lena image SPIHT encoded w/o Arithmetic Coding. • Decoder: Hard decision Viterbi Algorithm. • Channel: AWGN and flat fading Rayleigh channel. • Packet size, 450. • Number of packets for each transmission rate in bpp (0.25bpp): • : number of information bits for packet Preliminary Exam IEEE GLOBECOM 2009

  24. Numerical Results - Systems • Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion • In an image transmission system (SPIHT), we have the following systems: • seqConv1: Sequential Packetization with optimal Code rate using conventional 4PAM. (EEP scheme) • foldConv1: Folded Packetization with optimal Code rate using conventional 4PAM. • foldHier1: Folded Packetization with optimal Code rate using hierarchical 4PAM. • foldHier2: Folded Packetization with two optimal Code rates, using hierarchical 4PAM. • Note that foldConv1, foldHier1, foldHier2 are UEP schemes. Preliminary Exam IEEE GLOBECOM 2009

  25. Numerical Results • Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Preliminary Exam IEEE GLOBECOM 2009

  26. Numerical Results • Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Preliminary Exam IEEE GLOBECOM 2009

  27. Numerical Results • Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion • UEP schemes perform better than EEP schemes. • The non-concave behavior of these curves are a consequence that the code rate set is discrete. • The discrete nature of the code set is also the cause of non uniform gains going from one UEP scheme to another. • At low SNRs, the gap between the curves becomes more pronounceable as UEP makes more sense when the channel degrades. • Hierarchical parameter determines the BER for each packet in the first and second half of the packet stream simultaneously. • foldHier2is introduced to alleviate the constraints of hierarchical parameter and code rate set. Preliminary Exam IEEE GLOBECOM 2009

  28. Numerical Results • Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Preliminary Exam IEEE GLOBECOM 2009

  29. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Numerical Results - Explanations • For foldHier2, it is observed that channel code rates have the following relation: • In JSCC-only mechanism we would have . In other words more protection for the first half of the packet stream. • Our conjecture is that it is because with hierarchical modulation, higher code rate for the first half of the packets means that we increase the number of information bits in the first half. Then, the hierarchical parameters adjust themselves to protect the bits of the first half more than the bits of the remaining half. Preliminary Exam IEEE GLOBECOM 2009

  30. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Numerical Results Preliminary Exam IEEE GLOBECOM 2009

  31. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Numerical Results - Explanations • propConv1 provides only two levels of protection. • propHier1 and propHier2 provides as many protection levels as the number of packets. • propHier2 system is also compared with code rates in reverse order , with optimal hierarchical parameter set. It is observed that propHier2 protects almost all the packets better than it does with the reverse order. • This shows that UEP is provided with JSCC and Hierarchical parameters. Since channel code allocation mechanism also arranges the number information bits within each packet, our system benefits from these two mechanisms (JSCC and Hierarchical Modulation) in different ways. Preliminary Exam IEEE GLOBECOM 2009

  32. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Conclusion • Our system provides UEP using three different mechanisms: • Packetization. • Channel Coding. • Hierarchical Modulation. • We have the following constraints: • Hierarchical parameters determines PERs of the first and second half of the packet stream simultaneously. • Code rate set is discrete and not necessarily capacity achieving. • Joint optimization of these three mechanisms is performed. • C1: A new UEP method using packetization combined with channel coding is proposed. • C2: Hierarchical JSCC system is shown to perform better than a baseline JSCC-only mechanism. • C3: JSCC and Hierarchical parameters provide UEP in reverse directions of the packet stream. In our system, JSCC allocates more information bits in the first half of the packets. Preliminary Exam IEEE GLOBECOM 2009

  33. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion Final Remarks • This work was supported by Intel Inc., the Center for Wireless Communications (CWC) at UCSD, and the UC Discovery Grant program of the state of California. Preliminary Exam IEEE GLOBECOM 2009

  34. Progressive Sources • UEP techniques • System Model & Optimization • Numerical Results • Conclusion References • [SPIHT ‘96], Said A. and Pearlman W.A., “A New Fast and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 6, pp. 243-250, June 1996. • [Morimoto ‘96], Morimoto, M., Okada, M., and Komaki, S., “A Hierarchical System for Miltimedia Mobile Communication,” First International Workshop on Wireless Image/Video Communications, Sep. 1996. • [Lu ‘98] J. Lu, A. Nosratinia, and B. Aazhang, “Progressive source channel coding of images over bursty error channels,” in Proc. International Conference on Image Processing, Chicago, Ill, USA, October 1998. • Kleider J.E. and Abousleman G.P., Robust Image Transmission using Source Adaptive Modulation and Trellis-Coded Quantization, Image Processing, 1999. ICIP 99. Proceedings., Vol.1 pp.396 - 400 1999. • Pei, Y. Modestino, J.W. , “Multi-layered video transmission over wireless channels using an adaptive modulation and coding scheme”, Proceedings of IEEE International Conference on Image Processing ,vol. 2, pp. 10091012, Thessaloniki, Greece, October 2001. • P.G. Sherwood and K. Zeger, “Progressive Image Coding for Noisy Channels,” IEEE Signal Processing Letters, vol. 4, No. 7, pp. 189-191, July. 1997 • [Hagg ‘97] Hagenauer, J. “Rate-Compatible Punctured Convolutional Codes(RCPC Codes) and Their Applications,” IEEE Transactions on Communications, vol. 36, No. 4, pp. 389-400, April. 1997. • J. G. Proakis, Digital Communications, 3rd edition, New York: Mc-Graw Hill, 1995. Preliminary Exam IEEE GLOBECOM 2009

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