A Noise-Adaptive Machine Learning Framework for Optimizing User Grouping in Dynamic IM-OFDMA Systems

This paper addresses the challenge of optimizing user grouping in Index Modulation-based Orthogonal Frequency-Division Multiple Access (IM-OFDMA) systems within dynamic and stochastic noise environments. Utilizing the eXtreme Gradient Boosting (XGBoost) machine learning algorithm, we devised a framework capable of accurately predicting the optimality of user groupings across varying Signal-to-Noise Ratio (SNR) levels. Six models corresponding to different noise conditions were created, showcasing adaptability to adjacent noise levels via distribution shift handling, thereby ensuring robust performance across a wide noise spectrum. To accurately identify the most appropriate optimality prediction model for dynamic environments, we introduced a specialized model for precisely estimating the system's internal noise power. The accuracy of this model is crucial for the selection process and was significantly enhanced by implementing sequential Bayesian updating, facilitating a more precise estimation of internal noise power. Following this, we introduced a provisional optimization algorithm designed to refine user groupings within dynamic IM-OFDMA systems. Simulation results highlight the algorithm's effectiveness in markedly improving system performance, evidenced by a significant decrease in bitrate errors. These findings illuminate the significant potential of applying machine learning strategies to wireless communication systems, providing insightful contributions to the enhancement of IM-OFDMA systems in practical settings.