In this paper, we study an unmanned aerial vehicle (UAV) enabled data collection system, where an intelligent reflecting surface (IRS) is deployed to assist in the communication from a cluster of Internet-of-Things (IoT) devices to a UAV in the presence of a jammer. We aim to improve the energy efficiency (EE) via the joint design of UAV trajectory, IRS passive beamforming, device power allocation, and communication scheduling. However, the formulated non-linear fractional programming problem is challenging to solve due to its non-convexity and coupled variables. To overcome the difficulty, we propose an alternating optimization based algorithm to solve it sub-optimally by leveraging Dinkelbach's algorithm, successive convex approximation (SCA) technique, and block coordinate descent (BCD) method. Extensive simulation results show that the proposed design can significantly improve the anti-jamming performance. In particular, for the remote jammer case, the proposed design can largely shorten the flight path and thus decrease the energy consumption via the signal enhancement; while for the local jammer case, which is deemed highly challenging in conventional systems without IRS since the retreating away strategy becomes ineffective, our proposed design even achieves a higher performance gain owing to the efficient jamming signal mitigation.
翻译:在本文中,我们研究了无人驾驶航空飞行器(无人驾驶飞行器)的数据收集系统,在该系统中,部署了一个智能反射表面(IRS),以协助在出现干扰器的情况下从一组因特网反射装置(IoT)设备向无人驾驶飞行器进行通信。我们的目标是通过联合设计无人驾驶飞行器轨道、IRS被动波束成形、装置动力配置和通信时间安排来提高能效。然而,由于非线性分数编程的配置问题不兼容和各种变量,因此难以解决。我们建议采用基于智能反射表面(IRS)的交替优化算法,在出现干扰器的情况下,利用Dinkelbach的算法、连续的螺旋近似(SCA)技术以及区协调下行方法,以亚于最理想的方式加以解决。我们的目的是通过联合设计无人驾驶飞行器、IRS被动波束、装置动力分配和通信时间安排来提高能效。特别是就远程干扰器案而言,拟议的设计可以大大缩短飞行路径,从而通过信号增强减少能源消耗。为了克服这一困难,对于当地干扰器的情况,我们甚至认为,由于常规系统不具有高度挑战性地进行后退步战略,因此,因此,在常规系统上取得了更高的干扰性改进后取得更高的性能。