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Resource Allocation for 5G-Enabled Vehicular Networks in Unlicensed Frequency Bands
IEEE Transactions on Vehicular Technology ( IF 6.8 ) Pub Date : 2020-11-01 , DOI: 10.1109/tvt.2020.3030322
Ping Li , Lining Han , Shaoyi Xu , Dapeng Oliver Wu , Peng Gong

In this paper, a resource allocation problem is formulated to maximize the throughput of vehicular user equipments (VUEs) in both licensed, and unlicensed frequency bands under constraints of reliability, and latency for vehicular communications as well as the Quality of Service (QoS) for WiFi network based on a network system with coexisting VUEs, and WiFi user equipments (WUEs). In the system, the VUEs are able to access to the unlicensed frequency bands, and the interference among the VUEs, and WUEs are mitigated by the listen-before-talk (LBT) scheme or the duty cycle scheme. Due to the mixed integer nonlinear programming (MINLP) objective in the optimization problem, the problem is hard to be solved directly. Instead, we solve the problem by two steps. The number of VUEs offloaded to unlicensed frequency bands as well as the time factor for duty cycle scheme is firstly determined, and then, the optimization problem is converted into a convex optimization problem, which is solved by the proposed Lagrange Duality Method (LDM). Numerical results show the efficiency of our proposed application scenario compared to case with only LTE system or only WiFi system. Moreover, compared to traditional Greedy algorithm, the proposed algorithm performs better in terms of throughput with a guaranteed QoS of WUEs.

中文翻译:

未授权频段中支持 5G 的车载网络的资源分配

在本文中,提出了一个资源分配问题,以在车辆通信的可靠性、延迟以及服务质量 (QoS) 的约束下最大化许可和未许可频段中车辆用户设备 (VUE) 的吞吐量。基于VUE和WiFi用户设备(WUE)共存的网络系统的WiFi网络。在该系统中,VUE可以接入非授权频段,VUE和WUE之间的干扰通过先听后谈(LBT)方案或占空比方案来缓解。由于优化问题中的混合整数非线性规划(MINLP)目标,该问题难以直接求解。相反,我们分两步解决问题。首先确定卸载到未授权频段的 VUE 数量以及占空比方案的时间因子,然后将优化问题转化为凸优化问题,并通过提出的拉格朗日对偶法 (LDM) 求解。与只有 LTE 系统或只有 WiFi 系统的情况相比,数值结果显示了我们提出的应用场景的效率。此外,与传统的 Greedy 算法相比,该算法在保证 WUE QoS 的情况下在吞吐量方面表现更好。与只有 LTE 系统或只有 WiFi 系统的情况相比,数值结果显示了我们提出的应用场景的效率。此外,与传统的 Greedy 算法相比,该算法在保证 WUE QoS 的情况下在吞吐量方面表现更好。与只有 LTE 系统或只有 WiFi 系统的情况相比,数值结果显示了我们提出的应用场景的效率。此外,与传统的 Greedy 算法相比,该算法在保证 WUE QoS 的情况下在吞吐量方面表现更好。
更新日期:2020-11-01
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