A Machine Learning Approach to Predict Channel Stability Using Channel Quality Index

Channel quality feedback is crucial for the operation of 4G and 5G radio networks, as it allows to control User Equipment (UE) connectivity, transmission scheduling, and the modulation and rate of the data transmitted over the wireless link. However, when such feedback is frequent and the number of UEs in a cell is large, the channel may be overloaded by signalling messages, resulting in lower throughput and data loss. Optimizing this signaling process thus represents a key challenge. In this paper, a focus on Channel Quality Indicator (CQI) reports that are periodically sent from a UE to the base station, and propose mechanisms to optimize the reporting process with the aim of reducing signaling overhead and avoiding the associated channel overloads, particularly when channel conditions are stable. To this end, we apply machine learning mechanisms to predict channel stability, which can be used to decide if the CQI of a UE is necessary to be reported, and in turn to control the reporting frequency. For this purpose,four machine learning models,namely Support Vector Machines (SVM),K-Nearest Neighbour (KNN),Random Forest and Neural Networks (ANN). Simulation results shows that both provide a high prediction accuracy, with ANN consistently outperforming, especially as CQI reporting frequency reduces.