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An energy-aware distributed open market model for UAV-assisted communications

机译:无人机辅助通信的能源感知分布式开放市场模型

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Unmanned aerial vehicles (UAVs) have opened up numerous opportunities in terms of connectivity, especially in the context of realizing the vision of ubiquitous connectivity for 5G and beyond (B5G). The ease of mobility makes the UAV base stations (UAV-BSs) a viable candidate for providing ‘on demand’ services to the users. Moreover, viewing the spectrum crunch experienced by traditional cellular networks, the UAV-BSs can share the burden of providing connectivity. UAV-BSs can open up several business opportunities for mobile network operators (MNOs). In this paper, we propose an open market model, where a UAV-BS has the opportunity to establish a link with a terrestrial BS (TBS) of an MNO that provides the best connectivity and offers a lower price. A distributed model is considered where the decision making power lies with the UAV-BS. The TBS-selection problem is modeled as an integer linear programming problem, where we compare the performance of the Greedy heuristic algorithm (GHA) and the backtracking algorithm (BA) to solve our selection problem. We also incorporate an energy prediction model which impacts the selection criteria. We analyze the performance GHA and BA algorithm by presenting a tradeoff between the two algorithms in terms of accuracy of TBS selection and convergence time.
机译:无人飞行器(UAV)在连接性方面开辟了许多机会,尤其是在实现5G及更高版本(B5G)普遍连接的愿景的背景下。便捷的移动性使UAV基站(UAV-BS)成为向用户提供“按需”服务的可行候选者。此外,考虑到传统蜂窝网络经历的频谱紧缩,UAV-BS可以分担提供连接的负担。 UAV-BS可以为移动网络运营商(MNO)开辟一些商机。在本文中,我们提出了一个开放市场模型,其中,UAV-BS有机会与MNO的地面BS(TBS)建立链接,从而提供最佳的连接性并提供较低的价格。在UAV-BS拥有决策权的地方,可以考虑采用分布式模型。 TBS选择问题被建模为整数线性规划问题,在这里我们比较贪婪启发式算法(GHA)和回溯算法(BA)的性能来解决我们的选择问题。我们还纳入了影响选择标准的能量预测模型。通过在TBS选择的准确性和收敛时间方面提出两种算法之间的权衡,我们分析了GHA和BA算法的性能。

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