UAV enabled communications and networking can enhance wireless connectivity and support emerging services. However, this would require system-level understanding to modify and extend the existing terrestrial network infrastructure. In this paper, we integrate UAVs both as user equipment and base stations into existing LTE-Advanced heterogeneous network (HetNet) and provide system-level insights of this three-tier LTE-Advanced air-ground HetNet (AG-HetNet). This AG-HetNet leverages cell range expansion (CRE), ICIC, 3D beamforming, and enhanced support for UAVs. Using system-level understanding and through brute-force technique and heuristics algorithms, we evaluate the performance of AG-HetNet in terms of fifth percentile spectral efficiency (5pSE) and coverage probability. We compare 5pSE and coverage probability, when aerial base-stations (UABS) are deployed on a fixed hexagonal grid and when their locations are optimized using genetic algorithm (GA) and elitist harmony search algorithm based on genetic algorithm (eHSGA). Our simulation results show the heuristic algorithms outperform the brute-force technique and achieve better peak values of coverage probability and 5pSE. Simulation results also show that trade-off exists between peak values and computation time when using heuristic algorithms. Furthermore, the three-tier hierarchical structuring of FeICIC provides considerably better 5pSE and coverage probability than eICIC.

中文翻译：

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在多层 LTE-Advanced 公共安全 HetNet 中联合优化 FeICIC 和无人机位置的启发式方法

无人机支持的通信和网络可以增强无线连接并支持新兴服务。然而，这需要系统级的理解来修改和扩展现有的地面网络基础设施。在本文中，我们将无人机作为用户设备和基站集成到现有的 LTE-Advanced 异构网络 (HetNet) 中，并提供此三层 LTE-Advanced 空地 HetNet (AG-HetNet) 的系统级洞察。该 AG-HetNet 利用了小区范围扩展 (CRE)、ICIC、3D 波束成形和对无人机的增强支持。使用系统级理解并通过蛮力技术和启发式算法，我们根据第五个百分位频谱效率 (5pSE) 和覆盖概率来评估 AG-HetNet 的性能。我们比较了 5pSE 和覆盖概率，当空中基站 (UABS) 部署在固定的六边形网格上，并使用遗传算法 (GA) 和基于遗传算法的精英和谐搜索算法 (eHSGA) 优化其位置时。我们的模拟结果表明启发式算法优于蛮力技术，并实现了更好的覆盖概率和 5pSE 峰值。仿真结果还表明，在使用启发式算法时，峰值和计算时间之间存在权衡。此外，FeICIC 的三层层次结构提供了比 eICIC 更好的 5pSE 和覆盖概率。我们的模拟结果表明启发式算法优于蛮力技术，并实现了更好的覆盖概率和 5pSE 峰值。仿真结果还表明，在使用启发式算法时，峰值和计算时间之间存在权衡。此外，FeICIC 的三层层次结构提供了比 eICIC 更好的 5pSE 和覆盖概率。我们的模拟结果表明启发式算法优于蛮力技术，并实现了更好的覆盖概率和 5pSE 峰值。仿真结果还表明，在使用启发式算法时，峰值和计算时间之间存在权衡。此外，FeICIC 的三层层次结构提供了比 eICIC 更好的 5pSE 和覆盖概率。