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首页> 外文期刊>IEEE transactions on wireless communications >Joint Cache Placement, Flight Trajectory, and Transmission Power Optimization for Multi-UAV Assisted Wireless Networks
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Joint Cache Placement, Flight Trajectory, and Transmission Power Optimization for Multi-UAV Assisted Wireless Networks

机译:联合缓存放置,飞行轨迹和多UAV辅助无线网络的传输功率优化

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摘要

It is well known that unmanned aerial vehicles (UAVs) can help terrestrial base stations (BSs) offload data traffic from crowded areas to improve coverage and boost throughput. However, the limited backhaul capacity cannot cope with the ever-increasing data demands, for which caching is introduced to relieve the backhaul bottleneck. In this paper, we focus on a multi-UAV assisted wireless network, and target to fully utilize the benefits of wireless caching and UAV mobility for multiuser content delivery. By taking into account the limited storage, our goal is to maximize the minimum throughput among UAV-served users by jointly optimizing cache placement, UAV trajectory, and transmission power in a finite period. The resultant problem is a mixed-integer non-convex optimization problem. To facilitate solving this problem, an alternating iterative algorithm is proposed by adopting the block alternating descent and successive convex approximation methods. Specifically, this problem is split into three subproblems, namely cache placement optimization, trajectory optimization, and power allocation optimization. Then these subproblems are solved alternately in an iterative manner. We show that the proposed algorithm can converge to the set of stationary solutions of this problem. Besides, we further analyze the computational complexity of this algorithm. Numerical results show that great throughput enhancement is achieved by applying our proposed joint design in comparison with other benchmarks without trajectory design and power control.
机译:众所周知,无人驾驶航空公司(无人机)可以帮助地面基站(BSS)卸载来自拥挤区域的数据流量,以提高覆盖率和促进吞吐量。然而,有限的回程容量无法应对不断增加的数据需求,以便引入缓存以缓解回程瓶颈。在本文中,我们专注于多UAV辅助无线网络,并且目标是充分利用无线缓存和无人机移动性的优势,以实现多用户内容交付。通过考虑到有限的存储,我们的目标是通过在有限期中共同优化高速缓存放置,UAV轨迹和传输功率来最大限度地提高无人机服务用户之间的最小吞吐量。结果问题是混合整数非凸优化问题。为了便于解决这个问题,通过采用块交替的下降和连续凸起近似方法提出了一种交替的迭代算法。具体地,该问题被分成三个子问题,即高速缓存放置优化,轨迹优化和功率分配优化。然后,这些子问题以迭代方式交替解决。我们表明该算法可以收敛到这个问题的静止解决方案集。此外,我们进一步分析了该算法的计算复杂性。数值结果表明,通过应用所提出的联合设计与没有轨迹设计和功率控制的其他基准相比,通过应用我们提出的联合设计来实现了大量的吞吐量增强。

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