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Group-based joint signaling and data resource allocation in MTC-underlaid cellular networks

  • Research Paper
  • Special Focus on Machine-Type Communications
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Abstract

Machine-type communications (MTC) are gaining significant research attention as one of the most promising technologies for the fifth generation (5G) mobile networks. A critical issue handled by MTC is support for massive numbers of connections, which is a growing problem that will become increasingly challenging as MTC share spectrum resources with cellular communication. Here, not only the number of connections but also the data rate requirements of cellular users (CUEs) need to be considered. Given these issues, in this paper, we formulate a group-based joint signaling and data resource optimization model constrained by network resource and data rate requirements in order to maximize the number of connections. We also note that this problem is nonconvex and that obtaining an optimal solution is computationally complex for MTC with massive numbers of users (UEs). Therefore, we decompose the problem into group-based data aggregation and resource allocation subproblems. To solve these two subproblems, we develop an adaptive group head selection algorithm and a joint signaling and data resource allocation algorithm that satisfy both the data rate requirement and resource constraints, respectively. Our simulation results show that our proposed algorithms significantly improve the number of connections when compared with other classic methods. Furthermore, our results reveal that the limiting factor on the number of connections changes with the ratio of the number of MTC UEs to that of CUEs and the ratio of data requirement of MTC UEs to that of CUEs. Finally, we note that our proposed group-based resource allocation algorithm can effectively improve the number of connections, especially when more MTC UEs and a small amount of MTC data are present.

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References

  1. Boswarthick D, Elloumi O, Hersent O. M2M Communications: A Systems Approach. Hoboken: John Wiley & Sons, 2012

    Book  Google Scholar 

  2. Lucero S. Maximizing Mobile Operator Opportunities in M2M: the Benefits of an M2M-Optimized Network. ABI Research. 2010

    Google Scholar 

  3. Malm A, Tobias R. Wireless M2M and Mobile Broadband Services. Berg Insight. 2007

    Google Scholar 

  4. Taleb T, Kunz A. Machine type communications in 3GPP networks: potential, challenges, and solutions. IEEE Commun Mag, 2012, 50: 178–184

    Article  Google Scholar 

  5. Ksentini A, Hadjadj-Aoul Y, Taleb T. Cellular-based machine-to-machine (M2M): overload control. IEEE Netw, 2012, 26: 54–60

    Article  Google Scholar 

  6. Lien S Y, Liau T H, Kao C Y, et al. Cooperative access class barring for machine-to-machine communications. IEEE Trans Wirel Commun, 2012, 11: 27–32

    Article  Google Scholar 

  7. Oh C Y, Hwang D, Lee T J. Joint access control and resource allocation for concurrent and massive access of M2M devices. IEEE Trans Wirel Commun, 2015, 14: 4182–4192

    Article  Google Scholar 

  8. Laya A, Alonso L, Alonso-Zarate J. Is the random access channel of LTE and LTE-A suitable for M2M communications? A survey of alternatives. IEEE Commun Surv Tut, 2014, 16: 4–16

    Article  Google Scholar 

  9. 3GPP. Study on RAN improvements for machine type communications. TR 37.868 V0.5.1. 2010. http://www.3gpp. org/ftp/Specs/archive/37 series/37.868/

  10. Zheng K, Ou S, Alonso-Zarate J, et al. Challenges of massive access in highly dense LTE-advanced networks with machine-to-machine communications. IEEE Wirel Commun, 2014, 21: 12–18

    Article  Google Scholar 

  11. Li X, Li D, Bai Y. Distributed differential admission control algorithm for delay-tolerant machine-to-machine devices. IET Commun, 2015, 9: 1230–1239

    Article  Google Scholar 

  12. Utkovski Z, Eftimov T, Popovski P. Random access protocols with collision resolution in a noncoherent setting. IEEE Wirel Commun Lett, 2014, 4: 445–448

    Article  Google Scholar 

  13. Ksentini A, Taleb T, Ge X, et al. Congestion-aware MTC device triggering. In: Proceedings of IEEE International Conference on Communications, Sydney, 2014. 294–298

    Google Scholar 

  14. Si P, Yang J, Chen S, et al. Adaptive massive access management for QoS guarantees in M2M communications. IEEE Trans Veh Technol, 2015, 64: 3152–3166

    Google Scholar 

  15. Farhadi G, Ito A. Group-based signaling and access control for cellular machine-to-machine communication. In: Proceedings of the 78th Vehicular Technology Conference, Las Vegas, 2013

    Google Scholar 

  16. Huq R M, Moreno K P, Zhu H, et al. On the benefits of clustered capillary networks for congestion control in machine type communications over LTE. In: Proceedings of the 24th International Conference on Computer Communication and Networks, Las Vegas, 2016

    Google Scholar 

  17. Taleb T, Ksentini A. An efficient scheme for MTC overload control based on signaling message compression. In: Proceedings of IEEE Global Communications Conference, Atlanta, 2013. 342–346

    Google Scholar 

  18. Hossain M I, Laya A, Militano F, et al. Reducing signaling overload: flexible capillary admission control for dense MTC over LTE networks. In: Proceedings of the 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, Hong Kong, 2015. 1305–1310

    Google Scholar 

  19. Kwon T, Cioffi J M. Random deployment of data collectors for serving randomly-located sensors. IEEE Trans Wirel Commun, 2011, 12: 2556–2565

    Article  Google Scholar 

  20. Niyato D, Wang P, Dong I K. Performance modeling and analysis of heterogeneous machine type communications. IEEE Trans Wirel Commun, 2014, 13: 2836–2849

    Article  Google Scholar 

  21. Ge X H, Chen J Q, Wang C X, et al. 5G green cellular networks considering power allocation schemes. Sci China Inf Sci, 2016, 59: 022308

    Article  Google Scholar 

  22. Zhang H, Huang S, Jiang C, et al. Energy efficient user association and power allocation in millimeter wave based ultra dense networks with energy harvesting base stations. IEEE J Sel Area Commun, 2017. doi: 10.1109/JSAC.2017.2720898

    Google Scholar 

  23. Abbas R, Shirvanimoghaddam M, Li Y, et al. Random multiple access for M2M communications with QoS guarantees. IEEE Trans Wirel Commun, 2017, 65: 2889–2903

    Article  Google Scholar 

  24. Dhillon H S, Huang H C, Viswanathan H, et al. On resource allocation for machine-to-machine (M2M) communications in cellular networks. In: Proceedings of IEEE Globalcom Workshops, Anaheim, 2012. 1638–1643

    Google Scholar 

  25. Pang Y C, Chao S L, Lin G Y, et al. Network access for M2M/H2H hybrid systems: a game theoretic approach. IEEE Commun Lett, 2014, 18: 845–848

    Article  Google Scholar 

  26. Zhang Z, Long K, Wang J, et al. On swarm intelligence inspired self-organized networking: its bionic mechanisms, designing principles and optimization approaches. IEEE Commun Surv Tut, 2014, 16: 513–537

    Article  Google Scholar 

  27. Zhang Z, Long K, Wang J. Self-organization paradigms and optimization approaches for cognitive radio technologies: a survey. IEEE Wirel Commun, 2013, 20: 36–42

    Article  Google Scholar 

  28. Zhang H, Jiang C, Mao X, et al. Interference-limited resource optimization in cognitive femtocells with fairness and imperfect spectrum sensing. IEEE Trans Veh Tech, 2016, 65: 1761–1771

    Article  Google Scholar 

  29. Zhang G, Wei H, Liang T, et al. A novel two-stage dynamic spectrum sharing scheme in cognitive radio networks. China Commun, 2016, 13: 236–248

    Article  Google Scholar 

  30. Zhang H, Jiang C, Beaulieu N C, et al. Resource allocation for cognitive small cell networks: a cooperative bargaining game theoretic approach. IEEE Trans Wirel Commun, 2015, 14: 3481–3493

    Article  Google Scholar 

  31. Lo A, Law Y W, Jacobsson M, et al. Enhanced LTE-advanced random-access mechanism for massive machine-tomachine (M2M) communications. In: Proceedings of the 27th Meeting of Wireless World Research Form (WWRF), Doha, 2011

    Google Scholar 

  32. Mitchell T M. Machine Learning. New York: McGraw-Hill Education Ltd, 1997

    MATH  Google Scholar 

  33. Foschini G J, Miljanic Z. A simple distributed autonomous power control algorithm and its convergence. IEEE Trans Veh Tech, 1993, 42: 641–646

    Article  Google Scholar 

  34. Lee N, Lin X, Andrews J G, et al. Power control for D2D underlaid cellular networks: modeling, algorithms, and analysis. IEEE J Sel Area Commun, 2013, 33: 1–13

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant Nos. 61461136002, 61631005) and Fundamental Research Funds for the Central Universities.

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Correspondence to Xuefei Zhang.

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Zhang, X., Wang, Y., Wang, R. et al. Group-based joint signaling and data resource allocation in MTC-underlaid cellular networks. Sci. China Inf. Sci. 60, 100304 (2017). https://doi.org/10.1007/s11432-017-9162-9

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  • DOI: https://doi.org/10.1007/s11432-017-9162-9

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