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

Abstract

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.

Keywords

• Sixth generation (6G)
• Intelligent reflecting surface (IRS)
• imperfect phase
• discrete phase noise
• quantization
• symbol error rate (SER)