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.
Similar content being viewed by others
References
Boswarthick D, Elloumi O, Hersent O. M2M Communications: A Systems Approach. Hoboken: John Wiley & Sons, 2012
Lucero S. Maximizing Mobile Operator Opportunities in M2M: the Benefits of an M2M-Optimized Network. ABI Research. 2010
Malm A, Tobias R. Wireless M2M and Mobile Broadband Services. Berg Insight. 2007
Taleb T, Kunz A. Machine type communications in 3GPP networks: potential, challenges, and solutions. IEEE Commun Mag, 2012, 50: 178–184
Ksentini A, Hadjadj-Aoul Y, Taleb T. Cellular-based machine-to-machine (M2M): overload control. IEEE Netw, 2012, 26: 54–60
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
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
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
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/
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
Li X, Li D, Bai Y. Distributed differential admission control algorithm for delay-tolerant machine-to-machine devices. IET Commun, 2015, 9: 1230–1239
Utkovski Z, Eftimov T, Popovski P. Random access protocols with collision resolution in a noncoherent setting. IEEE Wirel Commun Lett, 2014, 4: 445–448
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
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
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
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
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
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
Kwon T, Cioffi J M. Random deployment of data collectors for serving randomly-located sensors. IEEE Trans Wirel Commun, 2011, 12: 2556–2565
Niyato D, Wang P, Dong I K. Performance modeling and analysis of heterogeneous machine type communications. IEEE Trans Wirel Commun, 2014, 13: 2836–2849
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
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
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
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
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
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
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
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
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
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
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
Mitchell T M. Machine Learning. New York: McGraw-Hill Education Ltd, 1997
Foschini G J, Miljanic Z. A simple distributed autonomous power control algorithm and its convergence. IEEE Trans Veh Tech, 1993, 42: 641–646
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
Acknowledgments
This work was supported by National Natural Science Foundation of China (Grant Nos. 61461136002, 61631005) and Fundamental Research Funds for the Central Universities.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11432-017-9162-9