Antenna Selection and Device Grouping for Spectrum-Efficient UAV-Assisted IoT Systems
Antenna Selection and Device Grouping for Spectrum-Efficient UAV-Assisted IoT Systems
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DOI：
10.1109/jiot.2022.3229592
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发表时间：
2023-05
影响因子：
10.6
通讯作者：
D. Do;Chi‐Bao Le;Alireza Vahid;S. Mumtaz
中科院分区：
文献类型：
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作者：
D. Do;Chi‐Bao Le;Alireza Vahid;S. Mumtaz
从运输到军事监视已经实施了无人飞行器（无人机）辅助物联网（IoT）系统，并且被证明值得在下一代无线协议中融合。尽管无人机具有巨大的潜力，但在实施现实世界方面，它们具有主要的缺点，例如能源容量，信号质量损失和频谱限制。为了克服这些挑战，已经提出了无人机与光谱效率技术的整合，包括认知无线电（CR）和非正交多重访问（NOMA）。在本文中，我们将传输 - 安特纳纳选择（TAS）纳入了认知无线电Noma网络中，该网络通过在UAV上采用多种抗凝聚力方法来提供额外的好处，目的是更好地为地面NOMA设备提供服务。从理论上讲，与Multiantenna无人机相关的链接被认为会体验到中Nakagamii-$ m $褪色的分布。我们还强调了在NOMA设备上解码信号时，由于连续干扰取消（SIC）导致的降级性能降解。提出的模型的封闭形式表达式得出以评估两个主要性能指标，即中断概率和ergodic能力。进行蒙特卡洛模拟以分析在不同情况下系统的性能。我们观察到，组中设备的功率分配因子和无人机的高度对系统性能产生了明显影响。此外，无人机天线数量的增加可以补充这些效果并进一步改善系统性能。
Unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) systems have been implemented for over a decade, from transportation to military surveillance, and is proven worthy of integration in the next generation of wireless protocols. Though UAVs have immense potential, they have major drawbacks when it comes to real-world implementation, such as energy capacity, loss of signal quality, and spectrum limitations. To overcome these challenges, integration of UAVs with spectrum-efficient techniques, including cognitive radio (CR) and nonorthogonal multiple access (NOMA) has been proposed. In this article, we incorporate transmit-antenna selection (TAS) into an underlay cognitive radio NOMA network, which provides additional benefits through employing multiple-antenna-selection approach at the UAV with the goal of better serving the ground NOMA devices. The links associated with the multiantenna UAV are theoretically assumed to experience Nakagami- $m$ fading distribution. We also emphasize the degraded performance caused by imperfect successive interference cancelation (SIC) when decoding signals at the ground NOMA devices. The closed-form expressions for the proposed model are derived to evaluate two main performance metrics, namely, the outage probability and the ergodic capacity. Monte Carlo simulations are performed to analyze the performance of the system in different scenarios. We observe that the power allocation factors for the devices in a group and the altitude of UAV have a noticeable impact on the performance of the system. Furthermore, the increase in the number of antennas at the UAV can complement these effects and further improve the system performance.