No abstract

Due to the large coverage of 5G NB-IoT networks, a more realistic mobility model for a macroscopic scene will greatly facilitate the development of optimal radio resource management algorithms. However, models devised for a random motion scene are no longer applicable in circumstances. Therefore, in this paper, a city-level mobility model is proposed based on the feature mining of the real trajectory of vehicles in the city of Shenzhen. The proposed model is separately designed in the motion trajectory to reduce the mutual influence between the time and spatial sequence. Simulation results show that it can better present specific node motions with the physical constraints of the city layout, which are motivated with a high degree of fit in terms of self-similarity, hotspots, and long-tail features.

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.