IRS-Assisted UAV Communications with Imperfect Phase Compensation

Abstract

<div>This work presents a performance analysis on unmanned aerial vehicles (UAVs) assisted wireless</div><div>communications systems, where one of the UAVs supports intelligent reflecting surfaces (IRS). As the</div><div>estimation and compensation of the end-to-end phase for each propagation path is prone to errors, imperfect</div><div>phase compensation at the IRS is taken into consideration. The performance is derived in terms of symbol</div><div>error rate (SER) and outage probability, where the phase error is modeled using the von Mises distribution.</div><div>The air-to-air (A2A) channel for</div><div>each propagation path is modeled as a single dominant line-of-sight (LoS) component, and the results are</div><div>compared to the Rician channel model. The obtained results reveal that the considered A2A model can be</div><div>used to accurately represent the A2A channel with Rician fading.</div>

Nonterrestrial Communications Assisted by Reconfigurable Intelligent Surfaces

“…As far as UAV systems are concerned, channel variation occurs even when UAVs are hovering, making optimal phase compensation unrealistic. Al-Dweik et al [122] and Al-Jarrah et al [123], [124] studied the effect of the imperfect phase compensation error on the performances of ARIS-assisted systems. Under the assumption of a Von Mises distributed phase error, Al-Dweik et al [122] and Al-Jarrah et al [124] derived closed-form expressions for the SER and OP for less than three reflecting elements and obtained accurate approximations of them for a larger number of reflecting elements.…”

“…Al-Dweik et al [122] and Al-Jarrah et al [123], [124] studied the effect of the imperfect phase compensation error on the performances of ARIS-assisted systems. Under the assumption of a Von Mises distributed phase error, Al-Dweik et al [122] and Al-Jarrah et al [124] derived closed-form expressions for the SER and OP for less than three reflecting elements and obtained accurate approximations of them for a larger number of reflecting elements. The obtained results showed that a large number of reflecting elements confer robustness toward phase error.…”

“…Depending on the considered objectives and their associated constraints, researchers propose to adopt different algorithms for solving optimization problems with particular forms. 1) SCA: The SCA algorithm acts as one of the most popular methods for solving nonconvex optimization problem such as power control [54], [148], [154], RIS configuration [54], [98], [113], [147], [149], [151], and position/trajectory optimization of aerial platforms [49], [51], [54], [76], [83], [98], [100], [122], [133]- [135], [138], [144]- [150], [152], [153], [158]. Specifically, at each iteration, the original nonconvex functions are approximated by some convex upper bounds of them with the same first order behavior.…”