On the Performance of IRS-Assisted Multi-Layer UAV Communications with Imperfect Phase Compensation
KAUST DepartmentElectrical and Computer Engineering Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/671256
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AbstractThis work presents the symbol error rate (SER) and outage probability analysis of multi-layer unmanned aerial vehicles (UAVs) wireless communications assisted by intelligent reflecting surfaces (IRS). In such systems, the UAVs may experience high jitter, making the estimation and compensation of the end-to-end phase for each propagation path prone to errors. Consequently, the imperfect phase knowledge at the IRS should be considered. The phase error is modeled using the von Mises distribution and the analysis is performed using the Sinusoidal Addition Theorem (SAT) to provide accurate results when the number of reflectors L ≤ 3, and the Central Limit Theorem (CLT) when L ≥ 4. The achieved results show that accurate phase estimation is critical for IRS based systems, particularly for a small number of reflecting elements. For example, the SER at 10-3 degrades by about 5 dB when the von Mises concentration parameter κ = 2 and L = 30, but the degradation for the same κ surges to 25 dB when L = 2. The air-to-air (A2A) channel for each propagation path is modeled as a single dominant line-of-sight (LoS) component, and the results are compared to the Rician channel. The obtained results reveal that the considered A2A model can be used to accurately represent the A2A channel with Rician fading.
CitationAl-Jarrah, M., Al-Dweik, A., Alsusa, E., Iraqi, Y., & Alouini, M.-S. (2021). On the Performance of IRS-Assisted Multi-Layer UAV Communications with Imperfect Phase Compensation. IEEE Transactions on Communications, 1–1. doi:10.1109/tcomm.2021.3113008
SponsorsThis project has received funding from the European Union’s Horizon 2020 research and innovation Programme under Grant agreement No 812991. The work of A. Al-Dweik was supported by Khalifa University Competitive Internal Research Award, grant number CIRA-2020-056.