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Multi-objective Placement of Unmanned Aerial Vehicles as Communication Relays Based on Clustering Method

机译:基于聚类的无人机作为通信中继的多目标布置

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This work presents a clustering based relay node placement method for using unmanned aerial vehicles as communication relays to from a two-tiered communication network for multiple agents in the operation region. Network performance and relay UAV number are jointly considered at the same level, which turns the problem to be multi-objective relay node placement (MORNP) problem with NP-hard computational complexity. Instead of finding Pareto solutions to the multi-objective optimization problem, this work attempts to achieved balanced network performance and relay UAV number. A one-step approach (OSA) algorithm is proposed, which contains two main aspects: optimal relay position seeking and channel association optimization. A set of initial relay positions are generated by finding the local density peaks of discretization positions in the mission area. It has proved that the channel association matrix is unique with specified relay positions, and the optimal relay positions could be given by solving a nonlinear programming problem. Then the optimal placement result could be achieved by iterating the channel association and optimal relay position seeking progresses. Simulation results validate the feasibility of the proposed OSA algorithm, and comparisons results show that the OSA algorithm requires less computational time than K-means strategy based algorithm, and less relay UAV numbers than the fast search and find density of peaks algorithm.
机译:这项工作提出了一种基于群集的中继节点放置方法,该方法用于将无人飞行器用作两级通信网络的通信中继,以用于操作区域中的多个代理。在同一级别上共同考虑网络性能和中继UAV数量,这使问题变成具有NP难计算复杂性的多目标中继节点放置(MORNP)问题。这项工作没有找到针对多目标优化问题的Pareto解决方案,而是尝试实现平衡的网络性能和中继UAV数量。提出了一种单步法(OSA)算法,该算法包含两个主要方面:最优中继位置寻找和信道关联优化。通过找到任务区域中离散化位置的局部密度峰值来生成一组初始中继位置。已经证明,信道关联矩阵在指定的中继位置处是唯一的,并且可以通过解决非线性规划问题来给出最佳中继位置。然后,可以通过迭代信道关联和最佳中继位置搜索进度来获得最佳放置结果。仿真结果验证了所提出的OSA算法的可行性,比较结果表明,与基于K-means策略的算法相比,OSA算法所需的计算时间更少,与峰值快速搜索和发现密度算法相比,所需的中继UAV数量更少。

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