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Performance analysis of channel estimation techniques for IRS assisted MIMO.

Alelign Ewinetu Baye¹

¹Department of Electrical and Computer Engineering, Woldia University, Woldia, Ethiopia. alelign.ew@wldu.edu.et.

Scientific Reports

|August 21, 2023

Summary

Researchers are exploring beyond 5G wireless communication using Terahertz (THz) frequencies and intelligent reflecting surfaces (IRS). Compressive sensing techniques, even with imperfect IRS elements, show promise for efficient channel estimation.

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Area of Science:

Wireless Communication
Signal Processing
Metamaterials

Background:

The demand for low latency and high data rates in wireless communication exceeds current 5G capabilities.
Terahertz (THz) frequencies offer large bandwidths, while intelligent reflecting surfaces (IRS) can mitigate high-frequency signal blockage.
Channel estimation is crucial for wireless systems, and compressive sensing reduces pilot symbol requirements.

Purpose of the Study:

To investigate channel estimation techniques for beyond 5G wireless systems utilizing Terahertz (THz) frequencies and intelligent reflecting surfaces (IRS).
To analyze the impact of imperfect IRS elements on channel estimation performance.
To compare the effectiveness of different channel estimation algorithms under varying signal-to-noise ratio (SNR) and pilot lengths.

Main Methods:

Channel estimation using compressive sensing techniques, specifically Orthogonal Matching Pursuit (OMP).
Modeling imperfect IRS elements by introducing amplitude perturbations.
Performance analysis using Normalized Mean Square Error (NMSE) and Spectral Efficiency (SE) metrics.
Comparison of Least Squares (LS), OMP, and Oracle algorithms.

Main Results:

The Orthogonal Matching Pursuit (OMP) algorithm demonstrates superior performance compared to the Least Squares (LS) algorithm, especially with fewer pilot symbols.
Compressive sensing is effective for channel estimation in sparse mmWave and THz signals, even with imperfect IRS.
Performance is analyzed across different Signal-to-Noise Ratios (SNR) and pilot lengths (T).

Conclusions:

OMP offers a more efficient approach to channel estimation in THz-IRS systems compared to traditional LS methods.
The study validates the feasibility of compressive sensing for future wireless networks operating at THz frequencies.
Imperfect IRS elements can be accounted for, providing a more realistic assessment of channel estimation performance.