论文标题
无人机和EV涉及的网络中充电基础架构共享的性能分析
Performance Analysis of Charging Infrastructure Sharing in UAV and EV-involved Networks
论文作者
论文摘要
电动汽车(EV)和无人机(UAV)分别在现代运输和通信网络中显示出巨大的潜力。但是,随着对此类技术的需求不断增长,有限的能源基础设施成为其未来增长的瓶颈。所有操作员的成本可能高昂,每个运营商都有自己的专用能源基础设施,例如充电站。在本文中,我们分析了无线电网络中的无线充电基础架构共享策略。我们考虑一种场景,无人机可以在电动汽车充电站收费并支付共享费用。在电动汽车方面,共享基础设施可以赚取额外的利润,但是他们的服务质量(例如等待时间)可能会稍微降低。在无人机的一方,如果租用电动汽车充电站可以实现可接受的系统性能,例如覆盖范围较高的概率,同时考虑成本,则可能不需要建造专用的充电站。在这种情况下,我们使用从随机几何形状的工具来对位置进行建模,并提出一个优化问题,以捕获上述成本或利润和服务质量之间的权衡。我们的数值结果表明,共享基础设施稍微增加了电动汽车的等待时间,例如5美元以内,但大大减少了无人机的等待时间,例如超过50美元的最低限度,并且部署更多充电站的确可以实现更好的性能,但是所有这些更好的性能预计会增加成本更高。
Electric vehicles (EVs) and unmanned aerial vehicles (UAVs) show great potential in modern transportation and communication networks, respectively. However, with growing demands for such technologies, the limited energy infrastructure becomes the bottleneck for their future growth. It might be of high cost and low energy efficiency for all the operators to each have their own dedicated energy infrastructure, such as charging stations. In this paper, we analyze a wireless charging infrastructure sharing strategy in UAV and EV-involved networks. We consider a scenario where UAVs can charge in EV charging stations and pay for the sharing fee. On the EVs' side, sharing infrastructure can earn extra profit but their service quality, such as waiting time, might slightly reduce. On the UAVs' side, if renting EV charging stations can achieve an acceptable system performance, say high coverage probability, while considering the cost, they may not need to build their dedicated charging stations. In this case, we use tools from stochastic geometry to model the locations and propose an optimization problem that captures the aforementioned trade-offs between cost or profit and quality of service. Our numerical results show that sharing infrastructure slightly increases the waiting time of EVs, say within $5$ min, but dramatically decreases the waiting time of drones, say more than $50$ min, and deploying more charging stations do achieve better performances, but all these better performances are expected to cost more.