Abstract
Machine-type Communication (MTC) is one of the three typical scenarios of 5G cellular communication network and is supposed to support connection density of 1 million devices per square kilometer. Random access initiated by such a large amount of devices may lead to congestion, conflicts and packet loss. This work designed a Q-learning-based dynamic preamble allocation scheme to solve the overload problem caused by large-scale MTC devices. The core enhancement considered in this work is Early Data Transmission (EDT) mechanism. MTC devices were divided into two groups of delay-sensitive and delay-tolerant according to the maximum delay tolerance of the services. Some of the preamble resources were pre-reserved for Delay-Sensitive Devices (DSD). Each DSD selected a preamble randomly from the pre-reserved set and performed EDT. Each Delay-Tolerant Device (DTD) selected a preamble randomly from the rest of the preambles and performed normal 4-step random access procedure. The number of elements in the pre-reserved set was dynamically adjusted using Q-learning algorithm according to the successful access proportion of the two types of devices. And then the base station would update and broadcast the service types which were permitted to use EDT. Simulation results show that the scheme proposed in this work improves the successful access proportion and meet the delay requirement of DSDs, and guarantees the throughput of the system at the same time.
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This work was supported by the Science and Technology Project of State Grid Corporation of China under Grant No. SGSDDK00KJJS1900405.
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Yuan, W., Zang, Y., Zhang, L. (2021). Preamble Selection and Allocation Algorithm Based on Q Learning. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12736. Springer, Cham. https://doi.org/10.1007/978-3-030-78609-0_38
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