Joint UAVs' Load Balancing and UEs' Data Rate Fairness Optimization by Diffusion UAV Deployment Algorithm in Multi-UAV Networks.
Joint UAVs' Load Balancing and UEs' Data Rate Fairness Optimization by Diffusion UAV Deployment Algorithm in Multi-UAV Networks.
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DOI：
10.3390/e23111470
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发表时间：
2021-11-07
期刊：
影响因子：
--
通讯作者：
Chen B
中科院分区：
文献类型：
--
作者：
Luan Z;Jia H;Wang P;Jia R;Chen B
无人机（UAV）可以被部署为基站（BS），用于5G/6 G网络中的用户设备（UE）的紧急通信。在多无人机通信网络中，无人机的负载均衡和用户的数据速率公平性是两个具有挑战性的问题，可以通过无人机部署策略来优化。在这项工作中，我们发现，这两个问题是由相同的性能指标，这使得有可能同时优化这两个问题。针对这一联合优化问题，提出了一种基于虚拟力场法的无人机扩散部署算法。首先，根据唯一的性能指标，我们定义了两个新的虚拟力量，这是无人机-无人机部队和UE-UAV部队分别由FU和FV定义。FV是负载平衡和UE的数据速率公平性的主要贡献者，并且FU有助于微调UE的数据速率公平性性能。其次，我们提出了一种扩散控制策略来更新无人机-无人机力量，以分布式的方式优化FV。在这种扩散策略中，每个无人机通过与相邻无人机交换信息来优化局部参数，从而以分布式方式实现全局负载平衡。第三，我们采用逐次凸优化方法来更新FU，这是一个非凸问题。用FV和FU的合力来控制无人机的运动。仿真结果表明，该算法在无人机负载均衡和UE数据速率公平性方面优于基线算法。
Unmanned aerial vehicles (UAVs) can be deployed as base stations (BSs) for emergency communications of user equipments (UEs) in 5G/6G networks. In multi-UAV communication networks, UAVs’ load balancing and UEs’ data rate fairness are two challenging problems and can be optimized by UAV deployment strategies. In this work, we found that these two problems are related by the same performance metric, which makes it possible to optimize the two problems simultaneously. To solve this joint optimization problem, we propose a UAV diffusion deployment algorithm based on the virtual force field method. Firstly, according to the unique performance metric, we define two new virtual forces, which are the UAV-UAV force and UE-UAV force defined by FU and FV, respectively. FV is the main contributor to load balancing and UEs’ data rate fairness, and FU contributes to fine tuning the UEs’ data rate fairness performance. Secondly, we propose a diffusion control stratedy to the update UAV-UAV force, which optimizes FV in a distributed manner. In this diffusion strategy, each UAV optimizes the local parameter by exchanging information with neighbor UAVs, which achieve global load balancing in a distributed manner. Thirdly, we adopt the successive convex optimization method to update FU, which is a non-convex problem. The resultant force of FV and FU is used to control the UAVs’ motion. Simulation results show that the proposed algorithm outperforms the baseline algorithm on UAVs’ load balancing and UEs’ data rate fairness.
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