大规模MIMO通信中无人机群的三维部署

IF 13.8 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Journal on Selected Areas in Communications Pub Date : 2020-09-21 DOI:10.1145/3411043.3412502
Ning Gao, Xiao Li, Shi Jin, M. Matthaiou
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引用次数: 14

摘要

研究了多天线地面站与无人机群之间的上行传输问题。将无人机作为智能体,探索其最佳三维(3-D)部署,以最大化多输入多输出(MIMO)系统的信道容量。具体来说,考虑到每架无人机获取网络全局信息的局限性,我们关注一种分散的控制策略,注意到群中的每架无人机只能利用局部信息来实现最优的三维部署。在这种情况下,优化问题可以根据秩函数分为几个优化子问题。由于秩函数的非凸性质和优化子问题是耦合的,原始问题是np困难的,因此不能用标准的凸优化解来求解。有趣的是,我们可以放宽每个子问题的约束条件,并通过制定的无人机信道容量最大化博弈来解决优化问题。我们根据所设计的奖励函数和势函数来分析这种博弈。然后,讨论了博弈中纯纳什均衡的存在性。为了实现MIMO系统的最佳纳什均衡,我们开发了一种分散学习算法,即分散无人机信道容量学习。给出了算法的细节,并分别分析了算法的收敛性、有效性和计算复杂度。在此基础上,结合实证和理论分析,提出了一些有见地的看法。大量的仿真结果表明,该学习算法可以利用局部信息优化三维无人机群部署,从而获得较高的MIMO信道容量。
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3-D Deployment of UAV Swarm for Massive MIMO Communications
We consider the uplink transmission between a multi-antenna ground station and an unmanned aerial vehicle (UAV) swarm. The UAVs are assumed as intelligent agents, which can explore their optimal three dimensional (3-D) deployment to maximize the channel capacity of the multiple input multiple output (MIMO) system. Specifically, considering the limitations of each UAV in accessing the global information of the network, we focus on a decentralized control strategy by noting that each UAV in the swarm can only utilize the local information to achieve the optimal 3-D deployment. In this case, the optimization problem can be divided into several optimization sub-problems with respect to the rank function. Due to the non-convex nature of the rank function and the fact that the optimization sub-problems are coupled, the original problem is NP-hard and, thus, cannot be solved with standard convex optimization solvers. Interestingly, we can relax the constraint condition of each sub-problem and solve the optimization problem by a formulated UAVs channel capacity maximization game. We analyze such game according to the designed reward function and the potential function. Then, we discuss the existence of the pure Nash equilibrium in the game. To achieve the best Nash equilibrium of the MIMO system, we develop a decentralized learning algorithm, namely decentralized UAVs channel capacity learning. The details of the algorithm are provided, and then, the convergence, the effectiveness and the computational complexity are analyzed, respectively. Moreover, we give some insightful remarks based on the proofs and the theoretical analysis. Also, extensive simulations illustrate that the developed learning algorithm can achieve a high MIMO channel capacity by optimizing the 3-D UAV swarm deployment with the local information.
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来源期刊
CiteScore
30.00
自引率
4.30%
发文量
234
审稿时长
6 months
期刊介绍: The IEEE Journal on Selected Areas in Communications (JSAC) is a prestigious journal that covers various topics related to Computer Networks and Communications (Q1) as well as Electrical and Electronic Engineering (Q1). Each issue of JSAC is dedicated to a specific technical topic, providing readers with an up-to-date collection of papers in that area. The journal is highly regarded within the research community and serves as a valuable reference. The topics covered by JSAC issues span the entire field of communications and networking, with recent issue themes including Network Coding for Wireless Communication Networks, Wireless and Pervasive Communications for Healthcare, Network Infrastructure Configuration, Broadband Access Networks: Architectures and Protocols, Body Area Networking: Technology and Applications, Underwater Wireless Communication Networks, Game Theory in Communication Systems, and Exploiting Limited Feedback in Tomorrow’s Communication Networks.
期刊最新文献
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