학술논문

Aerial IRS-Aided Vertical Backhaul FSO Networks over Fisher-Snedecor F Turbulence Channels
Document Type
Conference
Source
2022 IEEE Ninth International Conference on Communications and Electronics (ICCE) Communications and Electronics (ICCE), 2022 IEEE Ninth International Conference on. :133-138 Jul, 2022
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Power, Energy and Industry Applications
Signal Processing and Analysis
Measurement
Monte Carlo methods
Atmospheric modeling
Clouds
Bit error rate
Receivers
Probability density function
Vertical backhaul FSO networks
Fisher-Snedecor F turbulence
intelligent reflecting surface (IRS)
Language
Abstract
Free-space optics (FSO)-based, high-altitude plat form (HAP)-assisted backhaul network has recently attracted research efforts worldwide. Nevertheless, one of the critical concerns on HAP-assisted FSO links is cloud coverage, which may block the FSO connections completely. This paper explores a novel solution that uses multiple unmanned aerial vehicles (UAVs) equipped with an intelligent reflecting surface (IRS) array. These UAVs are deployed to diverse the FSO link from HAP-to-ground base station (BS) to avoid cloud coverage. We assume the Fisher-Snedecor $\mathcal{F}$ model for the atmospheric turbulence and use a selection combing (SC) receiver to obtain signals from multiple UAVs. We analytically derive the probability density function (PDF) of the received end-to-end signal-to-noise ratio (SNR) by employing the moment matching method, which can obtain an accurate approximation of PDF to the product of $\mathcal{F}$ variables. Using the derived statistics, we investigate different system performance metrics, including outage probability, outage capacity, and average bit error rate (BER). Finally, Monte Carlo simulations are provided to validate analytical results.