ABSTRACT Improved dependability, reduced latency, and great spectrum efficiency, especially in MIMO systems, are essential for 6G networks to be achieved in wireless communication. However, RF impairments, such as thermal noise, phase noise, and nonlinearity losses, severely hinder multiple‐input multiple‐output (MIMO) communication systems. These deficiencies restrict performance in high‐frequency 6G settings due to signal distortion, phase instability, and a poor signal‐to‐noise ratio (SNR). An Adaptive RF Compensation and Distortion Mitigation Framework (ARC‐DMF) is proposed here to resolve these issues. Dynamic compensation for the distortions inflicted by radio frequency interference is accomplished through the framework's adaptive filtering and machine learning‐based correction system, ensuring good signal transmission and enhanced data integrity. The ARC‐DMF approach employs digital pre‐distortion (DPD) techniques, deep learning‐based compensation modeling, and real‐time distortion estimation to minimize the effects of nonlinear distortion effects, phase noise variations, and thermal noise fluctuations. Bit error rate (BER), capacity enhancement, error vector magnitude (EVM), and spectral efficiency are some of the important performance metrics tested in several 6G operational scenarios through substantial simulations. The simulation findings show that ARC‐DMF substantially improves the performance of MIMO communication over traditional compensation methods, with reduced BER, better resilience to phase noise, and more resilient transmission. Enabling efficient and reliable wireless communication in high‐frequency RF situations, this study's findings offer insights into RF impairment mitigation for next‐generation 6G MIMO systems.
Alshnaikat et al. (Fri,) studied this question.