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Smart city article details
| Title | Dynamic Spectrum Optimization For Internet-Of-Vehicles With Deep-Learning-Based Mobility Prediction |
|---|---|
| ID_Doc | 21411 |
| Authors | Li F.; Sun Z.; Lam K.-Y.; Sun L.; Shen B.; Peng B. |
| Year | 2024 |
| Published | Wireless Personal Communications, 137, 1 |
| DOI | http://dx.doi.org/10.1007/s11277-024-11449-w |
| Abstract | Internet-of-Vehicles (IoV) plays an important part in Intelligent Transportation Systems and is widely regarded as one of the most strategic applications in smart city development. Smart spectrum resource management has received much attention from the research community as it is believed to be a promising approach for solving the spectrum resource challenge of IoV and Intelligent Transportation Systems. In this article, we propose a dynamic spectrum optimization technique based on a deep learning method for user mobility prediction. For this purpose, based on the Exploration and Preferential Return (EPR) model which can be used to investigate the movement trend and aggregation behavior of the target, we adopt the D-Exploration and Preferential Return (D-EPR) model as a deep learning technique to train a Long-Short Term Memory (LSTM) recurrent neural network (RNN) in order to predict the future locations of IoV nodes. With the predicted user’s mobility, a graph theoretic algorithm is then applied to achieve spectrum reuse and optimization. Besides, our proposed deep-learning-based user mobility prediction is able to identify the user position. This paper then compares the performances of mobility prediction by traditional methods and our proposal. The outcomes of spectrum efficiency and network capacity are also provided to show the effectiveness of the proposed solution. Numerical results are provided to evaluate the prediction accuracy and network utility of our proposal with various memory histories, user distribution mechanisms and node dwell time. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. |
| Author Keywords | Deep learning; Internet of Vehicles; Mobility prediction; Spectrum optimization |
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