Application of Neural Network Methods for Radio Signal Forecasting in Intelligent 5g Communication Systems
Year: 2025
Type: article
Abstract: This study presents the modeling and investigation of artificial neural networks (ANNs) aimed at predicting the parameters of radio signals with different spectral densities. The architectures ADALINE, Focused Linear Network (FLN), and Nonlinear Autoregressive Network (NARX), implemented in the MATLAB Neural Network Toolbox environment, were used. A detailed analysis of one-step and multi-step forecasting was carried out using error functionals (RMS, MAE, MPE). Special attention is given to the application of the obtained results in intelligent data processing for fifth-generation (5G) wireless communication systems, where radio signal forecasting enhances adaptive resource allocation efficiency, reduces transmission delay, and optimizes modulation parameters. The simulation results show that the accuracy and stability of neural network forecasting depend on the spectral complexity of the signal and the parameters of the learning algorithm. The FLN architecture demonstrated the best balance between learning s
DOI: 10.65599/bcww3084
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7124580588