Data-Driven Energy Efficient Predictive Resource Allocation in Internet of Vehicles
Permanent link to this recordhttp://hdl.handle.net/10754/667197
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AbstractIn Internet of Vehicles (IoV), the high mobility of vehicles aggravates the uneven and dynamic spatial-temporal distribution of wireless traffic, leading to low resource utilization. To improve the wireless resource utilization efficiency of IoV, this paper investigates predictive resource allocation strategy by exploiting vehicle mobility information. To characterize vehicle's speed distribution, we adopt a kernel density estimation method to analyze the real trajectory dataset. Based on this analysis, we propose an iterative predictive resource allocation scheme considering different mobility patterns and channel distribution information. Simulation results demonstrate that our proposed scheme converges well and can obtain considerable performance gains over non-predictive resource allocation schemes.
CitationXue, N., Zhang, H., Zhang, C., Li, T., & Yuan, D. (2020). Data-Driven Energy Efficient Predictive Resource Allocation in Internet of Vehicles. 2020 International Conference on Wireless Communications and Signal Processing (WCSP). doi:10.1109/wcsp49889.2020.9299813
SponsorsThis work was supported in part by Project of International Cooperation and Exchanges NSFC under Grant No. 61860206005, and in part by the National Natural Science Foundation of China under Grant No. 61671278.
Conference/Event name12th International Conference on Wireless Communications and Signal Processing, WCSP 2020