Research Article
Reinforcement Learning Based Preamble Resource Allocation Scheme for Access Control in Machine-to-Machine Communication
@INPROCEEDINGS{10.1007/978-3-031-34790-0_4, author={Hongyu Liu and Bei Liu and Hui Gao and Xibin Xu and Xin Su}, title={Reinforcement Learning Based Preamble Resource Allocation Scheme for Access Control in Machine-to-Machine Communication}, proceedings={Communications and Networking. 17th EAI International Conference, Chinacom 2022, Virtual Event, November 19-20, 2022, Proceedings}, proceedings_a={CHINACOM}, year={2023}, month={6}, keywords={Machine-to-machine communications Random Access Control Q-learning}, doi={10.1007/978-3-031-34790-0_4} }- Hongyu Liu
Bei Liu
Hui Gao
Xibin Xu
Xin Su
Year: 2023
Reinforcement Learning Based Preamble Resource Allocation Scheme for Access Control in Machine-to-Machine Communication
CHINACOM
Springer
DOI: 10.1007/978-3-031-34790-0_4
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(\%).
