From Glauber dynamics to Metropolis algorithm: Smaller delay in optimal CSMA

IEEE International Symposium on Information Theory - Proceedings

Volumn , Issue , 2012, Pages 2681-2685

  Lee, Chul Ho ^a   Eun, Do Young ^a   Yun, Se Young ^b   Yi, Yung ^b  

^a North Carolina State University   (United States)

^b Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

DELAY PERFORMANCE; FINITE-SIZED SYSTEMS; GLAUBER DYNAMICS; LARGE N; MAC SCHEDULING; METROPOLIS ALGORITHMS; OPTIMALITY; PARAMETERIZED; SECOND ORDERS; STATIONARY DISTRIBUTION;

ALGORITHMS; CARRIER SENSE MULTIPLE ACCESS; INFORMATION THEORY; MARKOV PROCESSES; PROBABILITY DISTRIBUTIONS;

OPTIMIZATION;

EID: 84867513137     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISIT.2012.6284006     Document Type: Conference Paper

References (20)

1. P. W. Glynn and W. Whitt, "Logarithmic asymptotics for steady-state tail probabilities in a single-server queue," Journal of Applied Probability, vol. 31, pp. 131-156, 1994.
2. W. K. Hastings, "Monte Carlo sampling methods using markov chains and their applications," Biometrika, vol. 57, no. 1, pp. 97-109, 1970.
3. L. Jiang, M. Leconte, J. Ni, R. Srikant, and J. Walrand, "Fast mixing of parallel Glauber dynamics and low-delay CSMA scheduling," in Proceedings of IEEE Infocom, 2011.
4. L. Jiang and J. Walrand, "A distributed CSMA algorithm for throughput and utility maximization in wireless networks," IEEE/ACM Transactions on Networking, vol. 18, no. 3, pp. 960 -972, June 2010.
5. G. L. Jones, "On the Markov chain central limit theorem," Probability Surveys, vol. 1, pp. 299-320, 2004.
6. C.-H. Lee, D. Y. Eun, S.-Y. Yun, and Y. Yi, "From Glauber dynamics to Metropolis algorithm: smaller delay in optimal CSMA," North Carolina State University, Tech. Rep., May 2012, available at http://www4.ncsu.edu/ ~dyeun/pub/techrep-csma12.pdf.
7. D. A. Levin, Y. Peres, and E. L. Wilmer, Markov chains and mixing times. American Mathematical Society, 2009.
8. J. Liu, Y. Yi, A. Proutiere, M. Chiang, and H. V. Poor, "Towards utility-optimal random access without message passing," Wireless Communications and Mobile Computing, vol. 10, no. 1, pp. 115-128, Jan. 2010.
9. M. Lotfinezhad and P. Marbach, "Throughput-optimal random access with order-optimal delay," in Proceedings of IEEE Infocom, 2011.
10. N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, "Equation of state calculations by fast computing machines," The Journal of Chemical Physics, vol. 21, no. 6, pp. 1087-1092, 1953.
11. A. Mira, "Ordering and improving the performance of Monte Carlo Markov chains," Statistical Science, vol. 16, no. 4, pp. 340-350, 2001.
12. J. Ni, B. Tan, and R. Srikant, "Q-csma: Queue-length based CSMA/CA algorithms for achieving maximum throughput and low delay in wireless networks," in Proceedings of IEEE Infocom, 2010.
13. P. H. Peskun, "Optimum Monte-Carlo sampling using Markov chains," Biometrika, vol. 60, pp. 607-612, 1973.
14. S. M. Ross, Stochastic processes, 2nd ed. John Wiley & Son, 1996.
15. D. Shah, D. N. C. Tse, and J. N. Tsitsiklis, "Hardness of low delay network scheduling," IEEE Trans. on Information Theory, 2009, submitted.
16. D. Shah and J. Shin, "Delay optimal queue-based CSMA," in Proceedings of ACM Sigmetrics, 2010.
17. J. Shin, D. Shah, and S. Rajagopalan, "Network adiabatic theorem: An efficient randomized protocol for contention resolution," in Proceedings of ACM Sigmetrics, 2009.
18. V. Subramanian and M. Alanyali, "Delay performance of CSMA in networks with bounded degree conflict graphs," in IEEE ISIT, 2011.
19. L. Tassiulas and A. Ephremides, "Stability properties of constrained queueing systems and scheduling for maximum throughput in multihop radio networks," IEEE Transactions on Automatic Control, vol. 37, no. 12, pp. 1936-1949, December 1992.
20. Y. Yi and M. Chiang, Next-Generation Internet Architectures and Protocols. Cambridge University Press, 2011, chapter 9: Stochastic network utility maximization and wireless scheduling.