Advancements in AI-driven Techniques for MIMO-OFDM Systems: A Comparative Analysis

Abstract

This study systematically reviews and compares various AI-driven approaches employed in MIMO-OFDM systems, including machine learning algorithms, neural networks, and optimization techniques. It delves into each method’s strengths, limitations, and unique contributions, offering insights into their effectiveness in addressing specific challenges such as channel estimation, interference mitigation, and spectral efficiency improvement. Furthermore, the paper discusses the impact of AI on system adaptability and robustness in dynamic communication environments. Through a comparative lens, the research aims to identify trends, best practices, and areas of improvement in AI applications for MIMO-OFDM systems. The findings contribute to a deeper understanding of the evolving landscape of AI-driven enhancements in communication systems, offering valuable insights for researchers, engineers, and practitioners in the field.