This article studies distributed algorithms to control self-organizing flying drones with massive MIMO networking capabilities - a network scenario referred to as mDroneNet . We attempt to answer the following fundamental question: what is the optimal way to provide spectrally-efficient wireless access to a multitude of ground nodes with mobile hotspots mounted on drones and endowed with a large number of antennas; when we can control the position of the drone hotspots, the association between the ground users and the drone hotspots, as well as the pilot sequence assignment and transmit power for the ground users? To the best of our knowledge, this is the first time that massive MIMO capabilities are considered in self-organizing flying drone networks. We first derive a mathematical formulation of the problem of joint power, association and movement control in mDroneNet, with the objective of maximizing the aggregate spectral efficiency of the ground users. It is shown that the resulting network control problem is a mixed integer nonlinear nonconvex programming (MINLP) problem. Then, a distributed solution algorithm with polynomial time complexity is designed by solving three closely-coupled subproblems: access association, joint pilot sequence assignment and power control, and drone movement control. As a performance benchmark, a globally-optimal but centralized solution algorithm is also designed based on a combination of the branch and bound framework and convex relaxation techniques. Results indicate that the distributed solution algorithm converges fast (within tens of iterations) and achieves a network spectral efficiency very close to the global optimum obtained by the centralized solution algorithm (over 90% in average).

中文翻译：

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大规模MIMO自组织飞行无人机的分布式联合动力，联合和飞行控制

本文研究了具有大规模MIMO网络功能的分布式算法，以控制自组织飞行无人机-这种网络场景称为 mDroneNet 。我们尝试回答以下基本问题：提供频谱效率高的无线接入到多个地面节点的最佳方法是什么，这些地面节点具有安装在无人机上并配备大量天线的移动热点；什么时候可以控制无人机热点的位置，地面用户与无人机热点之间的关联以及地面用户的导频序列分配和发射功率？据我们所知，这是首次在自组织飞行无人机网络中考虑大规模MIMO功能。我们首先导出mDroneNet中联合动力，关联和运动控制问题的数学公式，目的是最大化地面用户的总频谱效率。结果表明，网络控制问题是混合整数非线性非凸规划（MINLP）问题。然后，通过解决三个紧密耦合的子问题来设计具有多项式时间复杂度的分布式求解算法：访问关联，联合飞行员序列分配和功率控制以及无人机运动控制。作为性能基准，结合分支定界框架和凸松弛技术，设计了全局最优但集中化的求解算法。结果表明，分布式求解算法收敛迅速（在数十次迭代内），并且网络频谱效率非常接近于集中求解算法所获得的全局最优值（平均超过90％）。