Robust Transceiver Design for IRS-Assisted Cascaded MIMO Communication Systems.

Area of Science:

• Wireless communication systems
• Information theory
• Optimization theory

Background:

• Intelligent reconfigurable surfaces (IRSs) offer passive solutions for wireless networks, but traditional relays excel in reliability and throughput.
• Future wireless networks face challenges in providing reliable communication.

Purpose of the Study:

• To investigate a robust transceiver design using IRS and decode-and-forward relays to enhance reliability in wireless networks.
• To address residual self-interference (RSI) and maximize throughput under channel uncertainty.

Main Methods:

• Developed a robust transceiver design optimizing throughput under self-interference channel uncertainty constraints.
• Proposed a novel mathematical method to establish a lower bound for IRS performance.
• Utilized majorization theory and singular value decomposition (SVD) for optimal transmitter and relay response to worst-case RSI.
• Implemented a multi-level water-filling algorithm for iterative, locally optimal solutions.

Main Results:

• IRS effectiveness is contingent on element count relative to interference channel size in worst-case scenarios.
• The proposed algorithm outperforms state-of-the-art methods in time complexity and robustness.
• Achievable rates increased twofold for small antenna arrays and sixfold for large antenna arrays.

Conclusions:

• The combined IRS and relay system effectively addresses reliability challenges in future wireless networks.
• The proposed robust transceiver design and optimization algorithms provide significant throughput improvements.
• The study establishes a benchmark for IRS performance and offers practical solutions for enhancing wireless communication systems.