Transmission Optimization for Mobile Scenarios in 5G NB-IoT Networks

Zheng, Tongyi; Ye, Qingsong; Zhang, Runzhou; Jin, Fan; Ning, Lei

doi:10.1007/978-981-19-0390-8_101

Cited by 1 publication

(1 citation statement)

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“…Caso G. et al predicted the success of random access of NB-IoT and long-term evolution (LTE) networks based on the network status and adjusted the power of random access using the predicted results [27]. We use machine-learning methods to analyze the collected data and propose a UDP-XGB algorithm [28]. is study further improves the performance of the UDP-XGB algorithm, and enriches and improves the experimental simulation.…”

Section: Related Workmentioning

confidence: 93%

An XGB-Based Reliable Transmission Method in the mMTC Scenarios

Zheng

Ning

et al. 2021

Security and Communication Networks

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Massive machine-type communications (mMTCs) for Internet of things are being developed thanks to the fifth-generation (5G) wireless systems. Narrowband Internet of things (NB-IoT) is an important communication technology for machine-type communications. It supports many different protocols for communication. The reliability and performance of application layer communication protocols are greatly affected by the retransmission time-out (RTO) algorithm. In order to improve the reliability and performance of machine-type communications, this study proposes a novel RTO algorithm UDP-XGB based on the user datagram protocol (UDP) and NB-IoT. It combines traditional algorithms with machine learning. The simulation results show that real round-trip time (RTT) is close to the RTO, which is obtained by this algorithm, and the reliability and performance of machine-type communications have improved.

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Section: Related Workmentioning

confidence: 93%