The heterogeneous cloud radio access network (H-CRAN) is considered a promising solution to expand the coverage and capacity required by fifth-generation (5G) networks. UAV, also known as wireless aerial platforms, can be employed to improve both the network coverage and capacity. In this paper, we integrate small drone cells into a H-CRAN. However, new complications and challenges, including 3D drone deployment, user association, admission control, and power allocation, emerge. In order to address these issues, we formulate the problem by maximizing the network throughput through jointly optimizing UAV 3D positions, user association, admission control, and power allocation in H-CRAN networks. However, the formulated problem is a mixed integer nonlinear problem (MINLP), which is NP-hard. In this regard, we propose an algorithm that combines the genetic convex optimization algorithm (GCOA) and particle swarm optimization (PSO) approach to obtain an accurate solution. Simulation results validate the feasibility of our proposed algorithm, and it outperforms the traditional genetic and K-means algorithms.

中文翻译：

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简明而翔实的文章标题：通过联合用户协会和无人飞行器集成H-CRAN的功率分配，可实现吞吐量最大化

异构云无线电接入网络（H-CRAN）被认为是扩大第五代（5G）网络所需的覆盖范围和容量的有前途的解决方案。无人机，也称为无线空中平台，可以用来改善网络覆盖范围和容量。在本文中，我们将小型无人机细胞整合到H-CRAN中。但是，出现了新的复杂性和挑战，包括3D无人机部署，用户关联，准入控制和功率分配。为了解决这些问题，我们通过共同优化UAV 3D位置，用户关联，准入控制和H-CRAN网络中的功率分配来最大化网络吞吐量，从而解决了这一问题。但是，公式化的问题是一个混合整数非线性问题（MINLP），它是NP-困难的。在这方面，我们提出了一种结合遗传凸优化算法（GCOA）和粒子群优化（PSO）方法的算法，以获得准确的解决方案。仿真结果验证了该算法的可行性，其性能优于传统的遗传算法和K-means算法。