A Noise-Adaptive Machine Learning Framework for Optimizing User Grouping in Dynamic IM-OFDMA Systems
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Communications"
DOI: 10.1109/tcomm.2024.3455243
Abstract: 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… read more here.
Keywords: ofdma systems; machine learning; user grouping; optimizing user ... See more keywords
Optimizing User Satisfaction of Mobile Workloads Subject to Various Sources of Uncertainties
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Mobile Computing"
DOI: 10.1109/tmc.2018.2883619
Abstract: The success of mobile devices and applications is directly linked to a user's satisfaction of the quality of service—a metric used to denote the user's perception of the quality of an application. The first and… read more here.
Keywords: user satisfaction; optimizing user; workloads subject; satisfaction mobile ... See more keywords