Deep Reinforcement Learning-Based Spectrum Allocation Algorithm in Internet of Vehicles Discriminating Services
Zheng Guan; Yuyang Wang; Min He
With the rapid development of global automotive industry intelligence and networking, the Internet of Vehicles (IoV) service, as a key communication technology, has been faced with an increasing spectrum of resources shortage. In this paper, we consider a spectrum utilization problem, in which a number of co-existing cellular users (CUs) and prioritized device-to-device (D2D) users are equipped in a single antenna vehicle-mounted communication network. To ensure a business-aware spectrum access mechanism with delay granted in a complex dynamic environment, we consider optimizing a metric that maintains a trade off between maximizing the total capacity of vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) links and minimizing the interference of high priority links. A low complexity priority-based spectrum allocation scheme based on the deep reinforcement learning method is developed to solve the proposed formulation. We trained our algorithm using the deep Q-learning network (DQN) over a set of public bandwidths. Simulation results show that the proposed scheme can allocate spectrum resources quickly and effectively in a high dynamic vehicle network environment. Concerning improved channel transmission rate, the V2V link rate in this scheme is 2.54 times that of the traditional random spectrum allocation scheme, and the V2I link rate is 13.5% higher than that of the traditional random spectrum allocation scheme. The average total interference received by priority links decreased by 14.2 dB compared to common links, realized service priority distinction and has good robustness to communication noise.
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