Abstract
With the increasing number of intelligent connected vehicles, the problem of scarcity of communication resources has become increasingly obvious. It is a practical issue with important significance to explore a real‐time and reliable dynamic spectrum allocation scheme for the vehicle users, while improving the utilization of the available spectrum. However, previous studies have problems such as local optimum, complex parameter setting, learning speed, and poor convergence. Thus, in this paper, we propose a cognitive spectrum allocation method based on traveling state priority and scenario simulation in IoV, named Finder‐MCTS. The proposed method integrates offline learning with online search. This method mainly consists of two stages. Initially, Finder‐MCTS gives the allocation priority of different vehicle users based on the vehicle’s local driving status and global communication status. Furthermore, Finder‐MCTS can search for the approximate optimal allocation solutions quickly online according to the priority and the scenario simulation, while with the offline deep neural network‐based environmental state predictor. In the experiment, we use SUMO to simulate the real traffic flows. Numerical results show that our proposed Finder‐MCTS has 36.47%, 18.24%, and 9.00% improvement on average than other popular methods in convergence time, link capacity, and channel utilization, respectively. In addition, we verified the effectiveness and advantages of Finder‐MCTS compared with two MCTS algorithms’ variations.
Highlights
As a promising technology to serve the smart city, the internet of vehicle (IoV) has attracted the attention of governments and enterprises around the world
(ii) Combining with the above priority score, we propose a cognitive spectrum allocation method based on traveling state priority and different scenarios specially for IoV, named Finder-Monte-Carlo tree search algorithm (MCTS)
Our experiments are done by using the simulation of urban mobility (SUMO) simulator
Summary
As a promising technology to serve the smart city, the internet of vehicle (IoV) has attracted the attention of governments and enterprises around the world. In this paper, we propose a new cognitive spectrum allocation method based on traveling state priority and different scenarios specially for IoV in this paper. We consider the priority assignment based on vehicle state in spectrum allocation In this proposed new method, we choose the Monte-Carlo tree search algorithm (MCTS) to model our problem. According to the priority score, we allocate available spectrum resources from the highest priority to the lowest vehicle user, which can improve the allocation performance when doing dynamic spectrum allocation in IoV (ii) Combining with the above priority score, we propose a cognitive spectrum allocation method based on traveling state priority and different scenarios specially for IoV, named Finder-MCTS.
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