B.B. Saidov

Annotation:

This paper explores the possibility of improving the efficiency of fifth-generation communication networks through the use of intelligent forecasting methods. The aim of the study is to apply a nonlinear autoregressive neural network (NARNN) for the analysis and prediction of changes in radio signal parameters in 5G networks, followed by proactive radio resource management. Time series of RSRP and SINR indicators characterizing the state of the radio channel are used as input data. The modeling is carried out using a neural network architecture with time delays trained on experimental data. The forecasting accuracy is evaluated using standard error metrics. The results demonstrate that the application of NARNN makes it possible to detect signal quality degradation in advance and adapt network parameters, thereby ensuring more stable and reliable operation of fifth-generation networks.
Keywords: fifth-generation networks, nonlinear autoregressive neural network, signal forecasting, time series, radio resource management, communication quality.