Adaptive AI-Powered CSI Methods for Wireless Networks: Harnessing Advanced Neural Estimation to overcome NLOS Challenges

초록

Channel state information (CSI) plays a pivotal role in optimizing wireless networks like LTE, 5G, and emerging 6G systems by offering critical insights into transmission dynamics, enabling adaptive modulation, coding, and enhancing communication reliability. However, CSI estimation in highly dynamic environments faces significant hurdles due to factors such as user mobility, interference, and complex non-linear channel conditions. Traditional approaches like linear minimum mean square error (LMMSE), are limited by their reliance on mathematical models that assume linear and stationary behaviors, particularly in non-line-of-sight (NLoS) conditions. The rise of artificial intelligence (AI) introduces a transformative solution where AI-driven models can dynamically learn and adapt to interference patterns in real time, offering more precise and adaptive CSI estimation by accommodating time-varying channels. It enhances accuracy and streamlines computational demands, providing superior performance in scenarios where conventional methods fall short.