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Reinforcement Learning Based Preamble Resource Allocation Scheme for Access Control in Machine-to-Machine Communication

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Communications and Networking (ChinaCom 2022)


With the rapid development of Internet of Things (IoT) technology, the number of large numbers of Machine Type Communication (MTC) devices involved in M2M has increased dramatically. When large scale MTC devices access the base station at the same time in a short period of time, this can cause traffic overload and lead to a sharp drop in the success rate of access of MTC devices. 3GPP has proposed the access class barring (ACB) scheme to defer access requests from certain activated MTC devices to avoid congestion at the base station (BS). In this paper, we propose a dynamic ACB scheme for grouping MTC devices and a resource allocation scheme for preamble. First, MTC devices are classified into two categories according to their characteristics: delay-sensitive and energy-constrained. The two categories use separate preamble resources, and a temporary ACB factor is calculated for each time slot based on the current preamble resources and the number of devices. The preamble resources are reallocated based on this temporary ACB factor using reinforcement learning methods, and then the ACB factor is dynamically adjusted according to the new preamble resources. Simulation results show that the solution improves the access success rate of M2M devices, reducing the total service time of delay-sensitive devices by 40\(\%\) compared to the traditional solution, while reducing the access collision rate of energy-constrained devices by 30\(\%\).

Supported by the National Key R &D Program of China under Grant 2020YFB1806702.

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  1. Nguyen, D.C., Ding, M., Pathirana, P.N., Seneviratne, A., Li, J., Niyato, D., Dobre, O., Poor, H.V.: 6g internet of things: A comprehensive survey. IEEE Internet Things J. 9(1), 359–383 (2022).

    Article  Google Scholar 

  2. Wu, H., Zhu, C., La, R.J., Liu, X., Zhang, Y.: Fasa: Accelerated s-aloha using access history for event-driven m2m communications. IEEE/ACM Trans. Netw. 21(6), 1904–1917 (2013).

    Article  Google Scholar 

  3. 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. Tutor. 16(1), 4–16 (2014).

    Article  Google Scholar 

  4. Zhang, D., Liu, J., Zhou, W.: Acb scheme based on reinforcement learning in m2m communication. In: GLOBECOM 2020–2020 IEEE Global Communications Conference, pp. 1–6 (2020).

  5. Pacheco-Paramo, D., Tello-Oquendo, L.: Adjustable access control mechanism in cellular mtc networks: A double q-learning approach. In: 2019 IEEE Fourth Ecuador Technical Chapters Meeting (ETCM), pp. 1–6 (2019).

  6. Haider Shah, S.W., Riaz, A.T., Iqbal, K.: Congestion control through dynamic access class barring for bursty mtc traffic in future cellular networks. In: 2018 International Conference on Frontiers of Information Technology (FIT), pp. 176–181 (2018).

  7. Bui, A.T.H., Pham, A.T.: Deep reinforcement learning-based access class barring for energy-efficient mmtc random access in lte networks. IEEE Access 8, 227657–227666 (2020).

    Article  Google Scholar 

  8. Chen, Z., Smith, D.B.: Heterogeneous machine-type communications in cellular networks: Random access optimization by deep reinforcement learning. In: 2018 IEEE International Conference on Communications (ICC), pp. 1–6 (2018).

  9. Toor, W.T., Jin, H.: Combined access barring for energy and delay constrained machine type communications. In: 2018 International Conference on Information and Communication Technology Convergence (ICTC), pp. 130–132 (2018).

  10. Duan, S., Shah-Mansouri, V., Wang, Z., Wong, V.W.S.: D-acb: Adaptive congestion control algorithm for bursty m2m traffic in lte networks. IEEE Trans. Veh. Technol. 65(12), 9847–9861 (2016).

    Article  Google Scholar 

  11. Zhao, X., Wang, C., Wang, W.: Dynamic preamble grouping and access control scheme in machine-to-machine communication. In: 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP), pp. 1–6 (2019).

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Correspondence to Hongyu Liu .

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Liu, H., Liu, B., Gao, H., Xu, X., Su, X. (2023). Reinforcement Learning Based Preamble Resource Allocation Scheme for Access Control in Machine-to-Machine Communication. In: Gao, F., Wu, J., Li, Y., Gao, H. (eds) Communications and Networking. ChinaCom 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 500. Springer, Cham.

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  • Print ISBN: 978-3-031-34789-4

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