Semi-Persistent Resource Allocation Based on Traffic Prediction for Vehicular Communications

• Published in: IEEE transactions on intelligent vehicles Vol. 5; no. 2; pp. 345 - 355
• Main Authors: Chu, Ping, Zhang, J. Andrew, Wang, Xiaoxiang, Fang, Gengfa, Wang, Dongyu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.06.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bandwidth; Communications traffic; Computer architecture; Computer simulation; Delays; Dynamic scheduling; Network latency; Prediction algorithms; Radio equipment; Resource allocation; Resource management; Roads; Roadsides; short-term vehicular traffic prediction; Traffic congestion; Vehicle dynamics; Vehicular communications; water-filling algorithm
• ISSN: 2379-8858; 2379-8904
• DOI: 10.1109/TIV.2019.2955911

In cellular vehicular communications, high density and mobility of vehicles require frequent resource allocation, which can cause network congestion and large signalling and processing delay. To overcome this problem, we propose a novel semi-persistent resource allocation scheme based on a two-tier heterogeneous network architecture. The architecture includes a central macro base station (MBS) and multiple roadside units (RSU). In the proposed semi-persistent scheme, the MBS pre-allocates persistent resource to RSUs based on predicted traffic, and then allocates dynamic resource upon real-time requests from RSUs while vehicles simultaneously communicate using the pre-allocated resource. A simple Space-Time k-Nearest Neighbour (ST-kNN) method is developed for short-term traffic prediction, and a geometric water-filling algorithm is developed for minimizing the relative latency. Simulation results validate the effectiveness of the proposed semi-persistent scheme in comparison with two benchmark schemes.