Abstract
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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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. https://doi.org/10.1007/978-3-031-34790-0_4
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