Advertisement

Wireless Networks

, Volume 24, Issue 5, pp 1405–1418 | Cite as

Resource allocation for real-time traffic in unreliable wireless cellular networks

  • Jun Xu
  • Chengcheng Guo
  • Hao Zhang
  • Jianfeng Yang
Article
  • 228 Downloads

Abstract

Providing reliable transmission for real-time traffic in wireless cellular networks is a great challenge due to the unreliable wireless links. This paper concentrates on the resource allocation problem aiming to improve the real-time throughput. First, the resource allocation problem is formulated as a Markov Decision Process and thus the optimal resource allocation policy could be obtained by adopting the value iteration algorithm. Considering the high time complexity of the optimal algorithm, we further propose an approximate algorithm which decomposes the resource allocation problem into two subproblems, namely link scheduling problem and packet scheduling problem. By this method, the unreliable wireless links are only constrained in the link scheduling problem, and we can focus on the real-time requirement of traffic in packet scheduling problem. For the link scheduling problem, we propose the maxRel algorithm to maximize the long-term network reliability, and we theoretically prove that the maxRel algorithm is optimal in scenarios with dynamic link reliabilities. The Least Laxity First algorithm is adopted for the packet scheduling problem. Extensive simulation results show that the proposed approximate resource allocation algorithm makes remarkable improvement in terms of time complexity, packet loss rate and delay.

Keywords

Resource allocation Wireless cellular network Network reliability Real-time MDP 

Notes

Acknowledgements

This work is supported by Grant No. 413000016 from Wuhan University.

References

  1. 1.
    Sadi, Y., & ColeriErgen, S. (2015). Energy and delay constrained maximum adaptive schedule for wireless networked control systems. IEEE Transactions on Wireless Communications, 14(7), 3738–3751.CrossRefGoogle Scholar
  2. 2.
    Song, J., Han, S., Mok, A. K., Chen, D., Lucas, M., & Nixon, M. (2008). WirelessHART: Applying Wireless Technology in real-time industrial process control. RTAS, 2008, pp. 377–386.Google Scholar
  3. 3.
    Yan, M., Lam, K. Y., Han, S., Chan, E., Chen, Q., Fan, P., et al. (2014). Hypergraph-based data link layer scheduling for reliable packet delivery in wireless sensing and control networks with end-to-end delay constraints. Information Sciences, 278, 34–55.MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Shakkottai, S., & Srikant, R. (2002). Scheduling real-time traffic with deadlines over a wireless channel. Wireless Networks, 8(1), 13–26.CrossRefzbMATHGoogle Scholar
  5. 5.
    Li, Y., Zhang, H., Huang, Z., & Albert, M. (2014). Optimal link scheduling for delay-constrained periodic traffic over unreliable wireless links. INFOCOM, 2014, pp. 1465–1473Google Scholar
  6. 6.
    Hou, I. H., Borkar, V., & Kumar, P. R. (2009). A theory of QoS for wireless. INFOCOM, 2009, pp. 486–494Google Scholar
  7. 7.
    Nan, F., Yu, F. R., Sun, H., & Li, M. (2016). Adaptive power allocation schemes for spectrum sharing in interference-alignment-based cognitive radio networks. IEEE Transactions on Vehicular Technology, 65(5), 3700–3714.CrossRefGoogle Scholar
  8. 8.
    Jiang, H., Zhou, C., Wu, L., et al. (2015). TDOCP: A two-dimensional optimization integrating channel assignment and power control for large-scale WLANs with dense users. Ad Hoc Networks, 26, 114–127.CrossRefGoogle Scholar
  9. 9.
    Gabale, V., Raman, B., Dutta, P., & Kalyanraman, S. (2013). A classification framework for scheduling algorithms in wireless mesh networks. IEEE Communications Surveys and Tutorials, 15(1), 199–222.CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    Hou, I. H., & Kumar, P. R. (2011). A survey of recent results on real-time wireless networking. In Proceedings of the real-time wireless for industrial applications, pp. 1–6.Google Scholar
  12. 12.
    Hou, I. H., & Kumar, P. R. (2009). Scheduling heterogeneous real-time traffic over fading wireless channels. IEEE/ACM Transactions on Networking, 22(5), 1631–1644.CrossRefGoogle Scholar
  13. 13.
    Puterman, M. L. (1994). Markov decision processes: Discrete stochastic dynamic programming. New Jersey: Wiley.CrossRefzbMATHGoogle Scholar
  14. 14.
    Abu Alsheikh, M., Hoang, D. T., Niyato, D., Tan, H. P., & Lin, S. (2015). Markov decision processes with applications in wireless sensor networks: A survey. IEEE Communications Surveys and Tutorials, 17(3), 1239–1267.CrossRefGoogle Scholar
  15. 15.
    Willig, A., & Uhlemann, E. (2014). Deadline-aware scheduling of cooperative relayers in TDMA-based wireless industrial networks. Wireless Networks, 20(1), 73–88.CrossRefGoogle Scholar
  16. 16.
    Wang, R., & Lau, V. K. (2013). Delay-aware two-hop cooperative relay communications via approximate MDP and stochastic learning. IEEE Transactions on Information Theory, 59(11), 7645–7670.MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Zhou, B., Cui, Y., & Tao, M. (2015). Stochastic throughput optimization for two-hop systems with finite relay buffers. IEEE Transactions on Signal Processing, 63(20), 5546–5560.MathSciNetCrossRefGoogle Scholar
  18. 18.
    Moghadari, M., Hossain, E., & Le, L. B. (2013). Delay-optimal distributed scheduling in multi-user multi-relay cellular wireless networks. IEEE Transactions on Communications, 61(4), 1349–1360.CrossRefGoogle Scholar
  19. 19.
    Niafar, S., Tan, X., & Tsang, D. H. (2016). Optimal downlink scheduling for heterogeneous traffic types in LTE-A based on MDP and chance-constrained approaches. Mobile Networks and Applications, 21(3), 390–401.CrossRefGoogle Scholar
  20. 20.
    Xu, J., Yang, J., Xie, Y., Guo, C., & Yu, Y. (2016). MDP based link scheduling in wireless networks to maximize the reliability. Wireless Networks, 22(5), 1659–1671.CrossRefGoogle Scholar
  21. 21.
    Lei, L., Kuang, Y., Cheng, N., Shen, X., & Lin, C. (2016). Delay-optimal dynamic mode selection and resource allocation in device-to-device communications-Part I: Optimal policy. IEEE Transactions on Vehicular Technology, 65(5), 3474–3490.CrossRefGoogle Scholar
  22. 22.
    Lei, L., Kuang, Y., Cheng, N., Shen, X., & Lin, C. (2015). Delay-optimal dynamic mode selection and resource allocation in device-to-device communications-Part II: Practical algorithm. IEEE Transactions on Vehicular Technology, 65(5), 3491–3505.CrossRefGoogle Scholar
  23. 23.
    Wu, H., Lin, X., Liu, X., Tan, K., & Zhang, Y. (2014). Decomposition of large-scale MDPs for wireless scheduling with load-and channel-awareness. In IEEE information theory and applications workshop, pp. 1–10Google Scholar
  24. 24.
    Gilbert, E. N. (1960). Capacity of a burst-noise channel. Bell System Technical Journal, 39(5), 1253–1265.MathSciNetCrossRefGoogle Scholar
  25. 25.
    Elliott, E. O. (1963). Estimates of error rates for codes on burst-noise channels. The Bell System Technical Journal, 42(5), 1977–1997.CrossRefGoogle Scholar
  26. 26.
    Hong, S. W., & Moayeri, N. (1995). Finite-state Markov channel-a useful model for radio communication channels. IEEE Transactions on Vehicular Technology, 44(1), 163–171.CrossRefGoogle Scholar
  27. 27.
    Liu, J. W. (2000). Real-time systems. New York: Prentice Hall.Google Scholar
  28. 28.
    Jain, R., Hawe W., & Chiu D. (1984). A Quantitative measure of fairness and discrimination for resource allocation in Shared Computer Systems. DEC-TR-301, September, 1984.Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Jun Xu
    • 1
  • Chengcheng Guo
    • 1
  • Hao Zhang
    • 2
  • Jianfeng Yang
    • 1
  1. 1.Electronic Information SchoolWuhan UniversityWuhanChina
  2. 2.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote SensingWuhan UniversityWuhanChina

Personalised recommendations