Cognitive Vehicular(认知车辆)研究综述
Cognitive Vehicular 认知车辆 - As the environment of cognitive vehicular and maritime networks is extremely dynamic, these networks suffer with a long delay because of intermittent links while providing services for different applications. [1]由于认知车辆和海事网络的环境非常动态，这些网络在为不同应用提供服务时会因为间歇性链路而遭受长时间的延迟。 [1]
cognitive vehicular network 认知车载网络
Finally, we use a special case to study how to design a secure and delay-sensitive content caching scheme in cognitive vehicular networks. [1] This paper presents a comprehensive model including a wireless backhaul as a cost-effective backhaul alternative to wired backhaul for vehicular networks, a heterogeneous underlay cognitive vehicular network with multiple mobile secondary transmitters acting as mobile small cells, a mobile secondary receiver and a mobile primary user. [2] In this paper, we study the channel allocation in the cognitive vehicular network (CVN), and we established a corresponding network model, channel model, service model and vehicle model for highway scenarios in CVN. [3] This paper explores cooperative, intelligent and cognitive vehicular networks and examines how intelligent transportation systems make more efficient transportation in urban environments and offers comfort and luxurious travel. [4] Based on this motivation, in this paper, we adopt a deep ${Q}$ -learning approach for designing an optimal data transmission scheduling scheme in cognitive vehicular networks to minimize transmission costs while also fully utilizing various communication modes and resources. [5] In this paper, we study the computation offloading in a cognitive vehicular network that reuses the TV white space (TVWS) bands. [6] The allocation of spectrum resources efficiently and equitably in dynamic cognitive vehicular networks is more challenging than static cognitive networks. [7] In this paper, we consider a cognitive vehicular network that uses the TVWS band, and formulate a dual-side optimization problem, to minimize the cost of VTs and that of the MEC server at the same time. [8] However, spectrum sensing becomes more challenging in Cognitive Vehicular Networks (CVNs) due to Secondary User’s (SU’s) mobility and often yields a detection performance loss as compared to static scenarios. [9] In order to solve this problem, cognitive radio (CR) technology has been used in vehicular networks, leading to cognitive vehicular networks (CVNs). [10]最后，我们使用一个特殊案例来研究如何在认知车辆网络中设计一种安全且延迟敏感的内容缓存方案。 [1] 本文提出了一个综合模型，包括无线回程作为车载网络有线回程的一种具有成本效益的回程替代方案、异构底层认知车载网络，其中多个移动辅助发射器充当移动小型蜂窝、移动辅助接收器和移动主用户. [2] 在本文中，我们研究了认知车辆网络（CVN）中的通道分配，并针对CVN中的高速公路场景建立了相应的网络模型、通道模型、服务模型和车辆模型。 [3] 本文探讨了协作、智能和认知的车辆网络，并研究了智能交通系统如何在城市环境中提高交通效率，并提供舒适和豪华的旅行。 [4] 基于此动机，在本文中，我们采用深度${Q}$-学习方法设计认知车载网络中的最优数据传输调度方案，以最大限度地降低传输成本，同时充分利用各种通信模式和资源。 [5] 在本文中，我们研究了重用电视空白 (TVWS) 频段的认知车辆网络中的计算卸载。 [6] 在动态认知车辆网络中高效、公平地分配频谱资源比静态认知网络更具挑战性。 [7] 在本文中，我们考虑使用 TVWS 频段的认知车载网络，并制定双边优化问题，以同时最小化 VT 和 MEC 服务器的成本。 [8] 然而，由于次要用户 (SU) 的移动性，认知车辆网络 (CVN) 中的频谱感知变得更具挑战性，并且与静态场景相比，通常会产生检测性能损失。 [9] 为了解决这个问题，认知无线电（CR）技术已被用于车载网络，从而产生了认知车载网络（CVNs）。 [10]