Accelerated PARAFAC-Based Channel Estimation for Reconfigurable Intelligent Surface-Assisted MISO Systems.

Area of Science:

• Wireless communication
• Signal processing
• Optimization algorithms

Background:

• Reconfigurable intelligent surfaces (RIS) offer enhanced wireless communication by controlling signal propagation.
• Accurate channel estimation is critical for the performance of RIS-assisted MISO systems.
• Existing methods like Alternating Least Squares (ALS) can be computationally intensive.

Purpose of the Study:

• To propose a novel, accelerated algorithm for fast and accurate channel estimation in RIS-MISO systems.
• To improve the efficiency of channel estimation without sacrificing accuracy.
• To provide insights into receiver design for unique channel estimation.

Main Methods:

• Developed an Accelerated Bilinear Alternating Least Squares (ABALS) algorithm.
• Utilized parallel factor decomposition to model the received signal as a tensor.
• Transformed the channel estimation into a cost function problem solvable via iterative optimization and linear interpolation.
• Derived receiver design strategies based on feasibility conditions.

Main Results:

• The ABALS algorithm demonstrates a faster estimation speed compared to the standard ALS algorithm.
• ABALS requires fewer iteration steps than ALS for convergence.
• The accuracy of ABALS is comparable to that of the ALS algorithm.
• The proposed receiver design strategy ensures the uniqueness of the channel estimation.

Conclusions:

• The ABALS algorithm provides a significant improvement in speed and efficiency for channel estimation in RIS-MISO systems.
• The method offers a practical solution for real-time applications requiring rapid channel estimation.
• The study contributes to the advancement of efficient signal processing techniques for intelligent wireless environments.