On the Performance of IRS-Assisted IoT-NTN With Joint Imperfect Phase Estimation and Quantization

Intelligent reflecting surface (IRS)-assisted communications technology is currently considered a key enabler for various wireless applications. The maximum gain of IRS is achieved when the phases of the reflected signals are optimally selected to maximize signal-to-noise ratio (SNR). However, pract...

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Bibliographic Details
Published in:IEEE open journal of the Communications Society 2024, Vol.5, p.256-275
Main Authors: Siddig, Ali A., Al-Dweik, Arafat, Al-Rimawi, Ashraf, Iraqi, Youssef, Pandey, Anshul, Giacalone, Jean-Pierre
Format: Article
Language:English
Subjects:
Addition theorem
Autonomous aerial vehicles
discrete phase noise
Error analysis
imperfect phase
Intelligent reflecting surface (IRS)
Internet of Things
Optimization
outage probability (OP)
Phase estimation
Probability density function
Probability density functions
quantization
Quantization (signal)
Receivers
Signal to noise ratio
Sixth generation (6G)
symbol error rate (SER)
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Summary:Intelligent reflecting surface (IRS)-assisted communications technology is currently considered a key enabler for various wireless applications. The maximum gain of IRS is achieved when the phases of the reflected signals are optimally selected to maximize signal-to-noise ratio (SNR). However, practical hurdles such as imperfect phase estimation and hardware limitations such as phase quantization can reduce the potential gain of the IRS deployment. Internet of Things applications are more vulnerable to such limitations due to restrictions on device size, energy, cost, and computational power. Therefore, this work evaluates the joint impact of quantization and imperfect phase estimation where the probability density function (PDF) of the estimated and quantized phase is derived. Then, using the sinusoidal addition theorem, the PDF of the received signal envelope is derived and used to derive exact analytic expressions for the symbol error rate and outage probability. The analytical and simulation results obtained show that the impact of the joint estimation and quantization imperfections depends on the SNR and number of IRS elements. In particular, it is shown that increasing the number of IRS elements can effectively mitigate the impact of phase estimation and quantization problems. Furthermore, the results show that the impact of phase quantization increases as the accuracy of phase estimation decreases.
ISSN:2644-125X
2644-125X
DOI:10.1109/OJCOMS.2023.3345357