5G及以上车载网络的无人机小蜂窝辅助频谱管理

Hang Shen, Yilong Heng, Ning Shi, Tianjing Wang, Guangwei Bai
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引用次数: 0

摘要

随着蜂窝车联网(C- V2X)和无人机制造技术的进步,将无人机小蜂窝(DSCs)集成到地面蜂窝网络中是实现多样化车辆应用的一个很有前途的解决方案。在本文中,提出了一个多dsc辅助的动态频谱管理框架,以最大限度地提高5G及蜂窝车辆网络在服务质量(QoS)约束下的网络效用。将网络效用最大化问题表述为考虑车辆与基站之间关联模式和异构基站之间频谱划分的混合整数非线性规划问题。为了数学可追溯性,将频谱划分和车辆- DSC关联的联合优化问题转化为双凹优化问题。然后设计了一种替代搜索算法来确定车辆关联模式和频谱切片比。仿真结果表明,与现有方法相比,该方案在网络吞吐量和频谱利用率方面取得了显著的性能提升。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Drone-Small-Cell-Assisted Spectrum Management for 5G and Beyond Vehicular Networks
With advancements in cellular vehicle-to-everything (C- V2X) and drone manufacturing technologies, integrating drone-small-cells (DSCs) into terrestrial cellular networks is a promising solution to enabling diversified vehicle applications. In this paper, a multi-DSC-assisted dynamic spectrum management framework is presented to maximize the network utility under quality-of-service (QoS) constraints in 5G and beyond cellular vehicular networks. The network utility maximization problem is formulated as mixed-integer nonlinear programming regarding association patterns between vehicles and base stations (BSs) and spectrum partitioning among heterogeneous BSs. For mathe-matical tractability, the joint optimization problem for spectrum partitioning and vehicle- DSC associations is transformed as a biconcave optimization problem. An alternate search algorithm is then designed to determine vehicle association patterns and spec-trum slicing ratios. Our simulation demonstrates that compared with state-of-the-art methods, the proposed scheme achieves a significant performance improvement in network throughput and spectrum utilization.
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