Wireless Networks

, Volume 25, Issue 4, pp 2101–2115 | Cite as

Optimal CSMA scheduling with dual access probability for wireless networks

  • Jin-Ghoo Choi
  • Changhee JooEmail author


Recently optimal Carrier Sense Multiple Access (CSMA) scheduling schemes have attracted much attention in wireless networks due to their low complexity and provably optimal throughput. However, in practice, the schemes incur strong positive correlations between consecutive link schedules and let a scheduled link likely remain scheduled in the next time slot, which leads to poor delay performance. In this paper, we revise the original optimal CSMA algorithm for discrete-time systems, the Queue-length based CSMA (Q-CSMA), and substantially improve its delay performance. In our proposed algorithm, called Dual Access Probability CSMA (DAP-CSMA), each link has two state-dependent access probabilities depending on the link activity of the previous time, which allow us to accelerate the turn-off of previously scheduled links. This rapid activity transition reduces the correlation of the link service process and thereby improves the delay performance. We show that our DAP-CSMA is provably efficient and attains the optimal throughput. The simulation results demonstrate that our proposed scheduling significantly outperforms Q-CSMA in various scenarios and can be combined with a recent variant of Q-CSMA for better delay performance, Delayed-CSMA.


Optimal CSMA Distributed link scheduling Throughput optimality Delay Wireless networks 



This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1D1A1B07048948), and in part by IITP grant funded by the Korea government (MSIT) (No. 2015-0-00278, Research on Near-Zero Latency Network for 5G Immersive Service).


  1. 1.
    Joo, C., & Kang, S. (2017). Joint scheduling of data transmission and wireless power transfer in multi-channel device-to-device networksnetworks. Journal of Communications and Networks, 19(2), 180–188.CrossRefGoogle Scholar
  2. 2.
    Tassiulas, L., & Ephremides, A. (1992). Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks. IEEE Transactions on Automatic Control, 37(12), 1936–1948.MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Neely, M. J., Modiano, E., & Rohrs, C. E. (2005). Dynamic power allocation and routing for time-varying wireless networks. IEEE Journal on Selected Areas in Communications, 23(1), 89–103.CrossRefGoogle Scholar
  4. 4.
    Wu, X., Srikant, R., & Perkins, J. R. (2007). Scheduling efficiency of distributed greedy scheduling algorithms in wireless networks. IEEE Transactions on Mobile Computing, 6(6), 595–605.CrossRefGoogle Scholar
  5. 5.
    Modiano, E., Shah, D., & Zussman, G. (2006). Maximizing throughput in wireless networks via gossiping. In Proceedings of the ACM SIGMETRICS.Google Scholar
  6. 6.
    Joo, C., & Shroff, N. (2009). Performance of random access scheduling schemes in multi-hop wireless networks. IEEE/ACM Transactions on Networking, 17(5), 1481–1493.CrossRefGoogle Scholar
  7. 7.
    Sanghavi, S., Bui, L., & Srikant, R. (2007). Distributed link scheduling with constant overhead. In Proceedings of the ACM SIGMETRICS.Google Scholar
  8. 8.
    Jiang, L., & Walrand, J. (2010). A distributed CSMA algorithm for throughput and utility maximization in wireless networks. IEEE/ACM Transactions on Networking, 18(3), 960–972.CrossRefGoogle Scholar
  9. 9.
    Jiang, L., Shah, D., Shin, J., & Walrand, J. (2010). Distributed random access algorithm: Scheduling and congestion control. IEEE Transactions on Information Theory, 56(12), 6182–6207.MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Rajagopalan, S., Shah, D., & Shin, J. (2009). Network adiabatic theorem: An efficient randomized protocol for contention resolution. In Proceedings of the ACM SIGMETRICS.Google Scholar
  11. 11.
    Ni, J., Tan, B., & Srikant, R. (2010). Q-CSMA: Queue-length based CSMA/CA algorithms for achieving maximum throughput and low delay in wireless networks. In IEEE INFOCOM.Google Scholar
  12. 12.
    Ghaderi, J., & Srikant, R. (2012). Effect of access probabilities on the delay performance of Q-CSMA algorithms. In IEEE INFOCOM.Google Scholar
  13. 13.
    Lee, C., Eun, D., Yun, S., & Yi, Y. (2012). From Glauber dynamics to Metropolis algorithm: Smaller delay in optimal CSMA. In IEEE ISIT.Google Scholar
  14. 14.
    Huang, P., & Lin, X. (2013). Improving the delay performance of CSMA algorithms: A virtual multi-channel approach. In IEEE INFOCOM.Google Scholar
  15. 15.
    Kwak, J., Lee, C., & Eun, D. (2014). A high-order Markov chain based scheduling algorithm for low delay in CSMA networks. In IEEE INFOCOM.Google Scholar
  16. 16.
    Wang, Y., & Xia, Y. (2013). A distributed CSMA algorithm for wireless networks based on Ising model. In IEEE GLOBECOM.Google Scholar
  17. 17.
    Proutiere, A., Yi, Y., Lan, T., & Chiang, M. (2010). Resource allocation over network dynamics without timescale separation. In Proceedings of the IEEE INFOCOM.Google Scholar
  18. 18.
    Qian, D., Zheng, D., Zhang, J., & Shroff, N. (2010). CSMA-based distributed scheduling in multi-hop MIMO networks under SINR model. In Proceedings of the IEEE INFOCOM.Google Scholar
  19. 19.
    Choi, J., Joo, C., Zhang, J., & Shroff, N. (2014). Distributed link scheduling under SINR model in multi-hop wireless networks. IEEE/ACM Transactions on Networking, 22(4), 1204–1217.CrossRefGoogle Scholar
  20. 20.
    Yun, S., Shin, J., & Yi, Y. (2013). CSMA over time-varying channels: Optimality, uniqueness and limited backoff rate. In Proceedings of the ACM MOBIHOC.Google Scholar
  21. 21.
    Kim, T., Ni, J., Srikant, R., & Vaidya, N. (2011). On the achievable throughput of CSMA under imperfect carrier sensing. In Proceedings of the IEEE INFOCOM.Google Scholar
  22. 22.
    Jiang, L., Leconte, M., Ni, J., Srikant, R., & Walrand, J. (2012). Fast mixing of parallel glauber dynamics and low-delay CSMA scheduling. IEEE Transactions on Information Theory, 58(10), 6541–6555.MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Shah, D., & Shin, J. (2010). Delay optimal queue-based CSMA. In Proceedings of the ACM SIGMETRICS.Google Scholar
  24. 24.
    Lotfinezhad, M., & Marbach, P. (2011). Throughput-optimal random access with order-optimal delay. In Proceedings of the IEEE INFOCOM.Google Scholar
  25. 25.
    Lee, D., Yun, D., Shin, J., Yi, Y., & Yun, S. (2014). Provable per-link delay-optimal CSMA for general wireless network topology. In IEEE INFOCOM.Google Scholar
  26. 26.
    Eryilmaz, A., Srikant, R., & Perkins, J. (2005). Stable scheduling policies for fading wireless channels. IEEE/ACM Transactions on Networking, 13(2), 411–424.CrossRefGoogle Scholar
  27. 27.
    Bruneel, H. (1983). On the behavior of buffers with random server interruptions. Performance Evaluation, 3, 165–175.MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Kelly, F., Maulloo, A., & Tan, D. (1998). Rate control in communication networks: Shadow prices, proportional fairness and stability. Journal of the Operational Research Society, 49, 237–252.CrossRefzbMATHGoogle Scholar
  29. 29.
    Choi, J., & Bahk, S. (2007). Cell throughput analysis of the proportional fair scheduler in the single cell environment. IEEE Transactions on Vehicular Technology, 56(2), 766–778.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Yeungnam UniversityGyeongsanRepublic of Korea
  2. 2.UNIST, 50 UNIST-gilUlsanRepublic of Korea

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