Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
140 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Competitive Analysis of Arbitrary Varying Channels (2407.02949v1)

Published 3 Jul 2024 in cs.IT and math.IT

Abstract: Arbitrary varying channels (AVC) are used to model communication settings in which a channel state may vary arbitrarily over time. Their primary objective is to circumvent statistical assumptions on channel variation. Traditional studies on AVCs optimize rate subject to the worst-case state sequence. While this approach is resilient to channel variations, it may result in low rates for state sequences that are associated with relatively good channels. This paper addresses the analysis of AVCs through the lens of competitive analysis, where solution quality is measured with respect to the optimal solution had the state sequence been known in advance. Our main result demonstrates that codes constructed by a single input distribution do not achieve optimal competitive performance over AVCs. This stands in contrast to the single-letter capacity formulae for AVCs, and it indicates, in our setting, that even though the encoder cannot predict the subsequent channel states, it benefits from varying its input distribution as time proceeds.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (38)
  1. The capacities of certain channel classes under random coding. The Annals of Mathematical Statistics, pages 558–567, 1960.
  2. R. Ahlswede. Elimination of correlation in random codes for arbitrarily varying channels. Z. Wahrsch. Verw. Gebiete, 33:159–175, 1978.
  3. I. Csiszár and P. Narayan. Arbitrarily varying channels with constrained inputs and states. IEEE Transactions on Information Theory, 34(1):27–34, 1988.
  4. I. Csiszár and P. Narayan. Capacity of the Gaussian arbitrarily varying channel. IEEE Transactions on Information Theory, 37(1):18–26, January 1991.
  5. I. Csiszár and P. Narayan. The capacity of the arbitrarily varying channel revisited : Positivity, constraints. IEEE Transactions on Information Theory, 34(2):181–193, 1988.
  6. M. Luby. LT codes. In Proceedings of the 43rd Annual IEEE Symposium on Foundations of Computer Science, pages 271–280, 2002.
  7. D. J. C. MacKay. Fountain codes. IEE Proceedings-Communications, 152(6):1062–1068, 2005.
  8. A. Shokrollahi. Raptor codes. IEEE Transactions on Information Theory, 52(6):2551–2567, 2006.
  9. O. Shayevitz and M. Feder. Communicating using feedback over a binary channel with arbitrary noise sequence. In IEEE International Symposium on Information Theory (ISIT), pages 1516–1520, 2005.
  10. Using zero-rate feedback on binary additive channels with individual noise sequences. In IEEE International Symposium on Information Theory (ISIT), pages 1431–1435, 2007.
  11. Zero-rate feedback can achieve the empirical capacity. IEEE Transactions on Information Theory, 56(1):25–39, 2009.
  12. O. Shayevitz and M. Feder. Achieving the empirical capacity using feedback: Memoryless additive models. IEEE Transactions on Information Theory, 55(3):1269–1295, 2009.
  13. Comments on unknown channels. In Information Theory Workshop, pages 172–176. IEEE, 2012.
  14. Y. Lomnitz and M. Feder. Communication over individual channels. IEEE Transactions on Information Theory, 57(11):7333–7358, 2011.
  15. Y. Lomnitz and M. Feder. Communication over individual channels–a general framework. arXiv preprint, arXiv:1203.1406, 2012.
  16. N. Blits. Rateless codes for finite message set. M.Sc. dissertation, Tel-Aviv University, 2012.
  17. Y. Lomnitz and M. Feder. Universal communication over arbitrarily varying channels. IEEE Transactions on Information Theory, 59(6):3720–3752, 2013.
  18. On the Capacity of Additive AVCs with Feedback. In IEEE International Symposium on Information Theory (ISIT), pages 504–509, 2022.
  19. Rateless coding for arbitrary channel mixtures with decoder channel state information. IEEE Transactions on Information Theory, 55(9):4119–4133, 2009.
  20. A. D. Sarwate. Robust and adaptive communication under uncertain interference. PhD thesis, University of California, Berkeley, 2008.
  21. A. D. Sarwate and M. Gastpar. Rateless codes for AVC models. IEEE Transactions on Information Theory, 56(7):3105–3114, 2010.
  22. M. V. Burnashev. Data transmission over a discrete channel with feedback. Random transmission time. Probl. Inf. Transm., 12(4):250––265, 1976.
  23. SWIPT-enabled relaying in IoT networks operating with finite blocklength codes. IEEE Journal on Selected Areas in Communications, 37(1):74–88, 2018.
  24. Throughput analysis of low-latency IoT systems with QoS constraints and finite blocklength codes. IEEE Transactions on Vehicular Technology, 69(3):3093–3104, 2020.
  25. Performance analysis and optimization of NOMA with HARQ for short packet communications in massive IoT. IEEE Internet of Things Journal, 8(6):4736–4748, 2020.
  26. Finite block length analysis of RIS-assisted UAV-based multiuser IoT communication system with non-linear EH. IEEE Transactions on Communications, 70(5):3542–3557, 2022.
  27. Wireless access for ultra-reliable low-latency communication: Principles and building blocks. IEEE Network, 32(2):16–23, 2018.
  28. Guest Editorial xURLLC in 6G: Next Generation Ultra-Reliable and Low-Latency Communications. IEEE Journal on Selected Areas in Communications, 41(7):1963–1968, 2023.
  29. Ultra-Reliable Low-Latency Communications: Foundations, Enablers, System Design, and Evolution Towards 6G. Foundations and Trends® in Communications and Information Theory, 20(5-6):512–747, 2023.
  30. M. Langberg and O. Sabag. Competitive channel-capacity. IEEE Transactions on Information Theory, 2024.
  31. A. Lapidoth and P. Narayan. Reliable communication under channel uncertainty. IEEE Transactions on Information Theory, 44(10):2148–2177, 1998.
  32. O. Kosut and J. Kliewer. Finite blocklength and dispersion bounds for the arbitrarily-varying channel. In IEEE International Symposium on Information Theory (ISIT), pages 2007–2011, 2018.
  33. N. Shulman and M. Feder. Static broadcasting. In IEEE International Symposium on Information Theory, page 23, 2000.
  34. N. Shulman. Communication over an unknown channel via common broadcasting. Ph.D. dissertation, Tel Aviv University, 2003.
  35. C. Shannon. A mathematical theory of communication. Bell System Technical Journal, 27(3):379–423, 623–656, July 1948.
  36. I. Csiszár and J. Korner. Information Theory: Coding Theorems for Discrete Memoryless Systems, 2nd edition. Akademiai Kiado, New York, NY, 1997.
  37. S. Z. Stambler. Shannon theorems for a full class of channels with state known at the output. Problems of Information Transmission, 11(4):3–12, (In Russian). 1975.
  38. R. W. Yeung. Information theory and network coding. Springer Science & Business Media, 2008.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now