Cognitive carrier resource optimization for internet-of-vehicles in 5G-enhanced smart cities

• Main Authors: Li, Feng, Lam, Kwok-Yan, Ni, Zhengwei, Niyato, Dusit, Liu, Xin, Wang, Li
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Computer science and engineering; 5G Mobile Communication; Smart Cities
• Online Access: https://hdl.handle.net/10356/168037
• Institution: Nanyang Technological University

Internet-of-Vehicles (IoV), an important part of Intelligent Transportation Systems, is one of the most strategic applications in smart cities initiatives. The mMTC and URLLC functions of 5G are especially crucial for ensuring the connectivity and communication needs of rapidly moving IoVs. In this backdrop, network virtualization, cognitive computing along with smart spectrum resource management to the virtual networks will play a key role in solving the spectrum resource challenge. In this article, we propose a dynamic carrier resource allocation scheme for supporting IoV systems in smart cities enabled by cloud radio access networks (CRAN)-based 5G carriers. In CRAN-based 5G networks, the carrier resource allocated to the virtual networks can be centrally managed and shared to meet the dynamic demand of cell capacities caused by the rapid movement of IoVs, and the response to this dynamic allocation will become more time critical. The proposed cognitive carrier resource optimization is achieved by enhancing the ability to predict movement of IoVs, hence the dynamically changing demand for carrier resources. As an enhancement of the traditional Markov Model, our prediction model introduces vehicles' mobility analysis in order to allow the construction of a more precise flow transition matrix to improve the prediction result. Numerical results are provided to show the performance improvement of the proposed method.