论文标题
基于无人机的无线多播网络中的联合放置优化和RNC
Joint Placement Optimization and RNC in UAV-based Wireless Multicast Networks
论文作者
论文摘要
随机网络编码（RNC）是一种有效的编码方案，旨在提高宽带网络的性能，特别是对于在5G网络中流行的多媒体应用程序。但是，由于时间限制和广泛的频段要求，传输实时媒体数据是一项具有挑战性的工作。此外，由于用户的移动，网络的拓扑变化，导致大型无线网络区域中的巨大频道异质性。在这种情况下，固定的宏基站（BS）或接入点可能不符合实时用户分布。因此，具有高移动性的基于无人机的BS可以通过根据用户的位置调整位置以适合网络的动态拓扑来提供灵活的服务。因此，在本文中，我们提出了一个基于无人机的自适应RNC（UARNC）方案，该方案共同优化了无人机的位置和RNC数据包计划，以最大程度地提高多播网络中的吞吐量，同时保证瓶颈用户的服务质量。该问题被称为优化问题，采用贪婪调度技术和粒子群优化（PSO）算法来解决它。最后，仿真结果证明了所提出的方案的有效性。
Random network coding (RNC) is an efficient coding scheme to improve the performance of the broadband networks, especially for multimedia applications which are popular in 5G network. However, it is a challenging work to transmit the real time media data because of the time limitation and wide band requirement. Moreover, the topology of the network changes due to users' movement, causing huge channel heterogeneity in large wireless network area. In this case, the fixed macro base station (BS) or access point may not fit the real-time user distributions. Accordingly, the UAV-based BS with high mobility can provide flexible service by adjusting it position according to users' locations to fit the dynamic topology of the network. Therefore, in this paper, we propose a UAV-based adaptive RNC (UARNC) scheme that jointly optimizes the UAV's location and RNC packet scheduling to maximize the throughput in a multicast network while guaranteeing the service quality of the bottleneck users. This problem is formulated as an optimization problem, and the greedy scheduling techniques and particle swarm optimization (PSO) algorithm are adopted to solve it. Finally, the simulation results prove the effectiveness of the proposed scheme.
