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Intelligent Reflecting Surfaces Assisted UAV Communications for IoT Networks: Performance Analysis

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posted on 23.04.2021, 14:19 by sami muhaidat

The increasing demand for wireless connectivity and the emergence of the notion of the Internet of Everything require new communication paradigms that will ultimately enable a plethora of new applications and new disruptive technologies. In this context, the present contribution investigates the use of the recently introduced intelligent reflecting surface (IRS) concept in unmanned aerial vehicles (UAV) enabled communications aiming to extend the network coverage and improve the communication reliability as well as spectral efficiency of Internet of Things (IoT) networks. In particular, we first derive tractable analytic expressions for the achievable symbol error rate (SER), ergodic capacity, and outage probability of the considered set up. Following this, we also derive tight upper and lower bounds on the average signal-to-noise ratio (SNR). Our derivations are then compared with the corresponding asymptotic performance, based on the central limit theorem (CLT) assumption, which reveals that the asymptotic SNR falls within the area between derived bounds, and approaches either bound depending on the number of reflective elements (REs). We further show that the asymptotic SER becomes in a tight agreement with the corresponding exact simulation SER for > 16. In addition, the offered results demonstrate that the use of the IRS is significantly effective as they assist in improving the achievable SER by five orders of magnitude. We further demonstrate that, in terms of achievable ergodic capacity, IRS-assisted UAV communication systems can exhibit ten times higher capacity compared to conventional UAV communications. Based on the above, these results and related insights are anticipated to be useful in the design and deployment of IRS-assisted UAV systems in the context of beyond 5G communications, such as 6G communications.

History

Email Address of Submitting Author

samimuhaidat@cunet.carleton.ca

Submitting Author's Institution

Khalifa University

Submitting Author's Country

United Arab Emirates