Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
194 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

A Learning-based Distributed Algorithm for Scheduling in Multi-hop Wireless Networks (2312.04754v1)

Published 7 Dec 2023 in cs.NI

Abstract: We address the joint problem of learning and scheduling in multi-hop wireless network without a prior knowledge on link rates. Previous scheduling algorithms need the link rate information, and learning algorithms often require a centralized entity and polynomial complexity. These become a major obstacle to develop an efficient learning-based distributed scheme for resource allocation in large-scale multi-hop networks. In this work, by incorporating with learning algorithm, we develop provably efficient scheduling scheme under packet arrival dynamics without a priori link rate information. We extend the results to distributed implementation and evaluation their performance through simulations.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (29)
  1. C. Joo, G. Sharma, N. B. Shroff, and R. R. Mazumdar, “On the Complexity of Scheduling in Wireless Networks,” EURASIP Journal of Wireless Communications and Networking, October 2010.
  2. X. Lin and N. B. Shroff, “The Impact of Imperfect Scheduling on Cross-Layer Congestion Control in Wireless Networks,” IEEE/ACM Trans. Netw., vol. 14, no. 2, pp. 302–315, April 2006.
  3. C. Joo, X. Lin, and N. B. Shroff, “Greedy Maximal Matching: Performance Limits for Arbitrary Network Graphs Under the Node-Exclusive Interference Model,” IEEE Trans. Autom. Control, vol. 54, no. 12, pp. 2734–2744, Dec 2009.
  4. C. Joo and N. B. Shroff, “Local Greedy Approximation for Scheduling in Multi-hop Wireless Networks,” IEEE Trans. Mobile Comput., vol. 11, no. 3, pp. 414–426, March 2012.
  5. X. Wu, R. Srikant, and J. R. Perkins, “Scheduling Efficiency of Distributed Greedy Scheduling Algorithms in Wireless Networks,” IEEE Trans. Mobile Comput., vol. 6, no. 6, pp. 595–605, 2007.
  6. X. Lin and S. B. Rasool, “Distributed and Provably Efficient Algorithms for Joint Channel-assignment, Scheduling, and Routing in Multichannel Ad Hoc Wireless Networks,” IEEE/ACM Trans. Netw., vol. 17, no. 6, pp. 1874–1887, Dec. 2009.
  7. C. Joo and N. B. Shroff, “Performance of Random Access Scheduling Schemes in Multi-hop Wireless Networks,” IEEE/ACM Trans. Netw., vol. 17, no. 5, October 2009.
  8. L. Tassiulas and A. Ephremides, “Stability Properties of Constrained Queueing Systems and Scheduling Policies for Maximal Throughput in Multihop Radio Networks,” IEEE Trans. Autom. Control, vol. 37, no. 12, pp. 1936–1948, December 1992.
  9. J. Choi, “On Improving Throughput of Multichannel ALOHA using Preamble-based Exploration,” Journal of Communications and Networks, vol. 22, no. 5, pp. 380–389, 2020.
  10. B. Hajek and G. Sasaki, “Link Scheduling in Polynominal Time,” IEEE Trans. Inf. Theory, vol. 34, no. 5, September 1988.
  11. L. Bui, S. Sanghavi, and R. Srikant, “Distributed Link Scheduling with Constant Overhead,” IEEE/ACM Trans. Netw., vol. 17, no. 5, pp. 1467–1480, October 2009.
  12. L. Jiang and J. Walrand, “A Distributed CSMA Algorithm for Throughput and Utility Maximization in Wireless Networks,” IEEE/ACM Trans. Netw., vol. 18, no. 13, pp. 960–972, June 2010.
  13. 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,” IEEE/ACM Trans. Netw., vol. 20, no. 3, June 2012.
  14. C. Joo, “On Random Access Scheduling for Multimedia Traffic in Multi-hop Wireless Networks,” IEEE Trans. Mobile Comput., vol. 12, no. 4, pp. 647–656, April 2013.
  15. S. A. Borbash and A. Ephremides, “Wireless Link Scheduling With Power Control and SINR Constraints,” IEEE Trans. Inf. Theory, vol. 52, no. 11, pp. 5106–5111, November 2006.
  16. J.-G. Choi, C. Joo, J. Zhang, and N. B. Shroff, “Distributed Link Scheduling Under SINR Model in Multihop Wireless Networks,” IEEE/ACM Trans. Netw., vol. 22, no. 4, pp. 1204–1217, Aug 2014.
  17. F. Li, D. Yu, H. Yang, J. Yu, H. Karl, and X. Cheng, “Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks: A Survey,” IEEE Wireless Communications, vol. 27, no. 1, pp. 24–30, 2020.
  18. Q. Zhao, L. Tong, A. Swami, and Y. Chen, “Decentralized Cognitive MAC for Opportunistic Spectrum Access in Ad Hoc Networks: A POMDP Framework,” IEEE J. Sel. Areas Commun., vol. 25, no. 3, pp. 589–600, April 2007.
  19. T. Stahlbuhk, B. Shrader, and E. Modiano, “Learning algorithms for scheduling in wireless networks with unknown channel statistics,” Ad Hoc Networks, vol. 85, pp. 131 – 144, 2019.
  20. W. Chen, Y. Wang, and Y. Yuan, “Combinatorial Multi-Armed Bandit: General Framework and Applications,” in International Conference on Machine Learning, 2013.
  21. T. Lai and H. Robbins, “Asymptotically Efficient Adaptive Allocation Rules,” Adv. Appl. Math., vol. 6, no. 1, pp. 4–22, March 1985.
  22. P. Auer, N. Cesa-Bianchi, and P. Fischer, “Finite-time Analysis of the Multiarmed Bandit Problem,” Machine Learning, vol. 47, no. 2, pp. 235–256, May 2002.
  23. V. Anantharam, P. Varaiya, and J. Walrand, “Asymptotically Efficient Allocation Rules for the Multiarmed Bandit Problem with Multiple Plays-Part I: I.I.D. Rewards,” IEEE Trans. Autom. Control, vol. 32, no. 11, pp. 968–976, November 1987.
  24. K. Liu and Q. Zhao, “Distributed Learning in Multi-Armed Bandit With Multiple Players,” IEEE Trans. Signal Processing, vol. 58, no. 11, pp. 5667–5681, Nov 2010.
  25. Y. Gai, B. Krishnamachari, and R. Jain, “Combinatorial Network Optimization With Unknown Variables: Multi-Armed Bandits With Linear Rewards and Individual Observations,” IEEE/ACM Trans. Netw., vol. 20, no. 5, pp. 1466–1478, Oct 2012.
  26. Y. Gai and B. Krishnamachari, “Decentralized Online Learning Algorithms for Opportunistic Spectrum Access,” in IEEE GLOBECOM, Dec 2011.
  27. A. Anandkumar, N. Michael, A. K. Tang, and A. Swami, “Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret,” IEEE J. Sel. Areas Commun., vol. 29, no. 4, pp. 731–745, April 2011.
  28. H. Tibrewal, S. Patchala, M. K. Hanawal, and S. J. Darak, “Distributed Learning and Optimal Assignment in Multiplayer Heterogeneous Networks,” in IEEE INFOCOM, 2019.
  29. S. Kang and C. Joo, “Low-Complexity Learning for Dynamic Spectrum Access in Multi-User Multi-Channel Networks,” IEEE Trans. Mobile Comput., 2021.
Citations (5)

Summary

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