Spectral efficient network and resource selection model in 5G networks

Abstrak

This work addresses the challenges in modern communication networks, emphasizing the need for improved efficiency, higher data transfer rates, and reduced delays. In 5G networks, advanced resource optimization, network selection, and relaying techniques are crucial for expanding multi-cellular coverage and enhancing network performance. However, implementing these techniques in mobile environments with high interference levels increases computational demands for radio resource management (RRM). Machine learning (ML) and deep learning (DL) are proposed as solutions to enhance consumer applications, reduce communication overhead, and improve RRM. Current ML/DL methods, however, struggle with identifying key features for network selection and balancing system throughput with spectral efficiency. This paper introduces the spectral efficient network and resource selection (SENRS) model for 5G multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) networks. Tested using the Stanford University Interim (SUI) channel fading model in a highway scenario, the SENRS model demonstrates superior performance compared to existing network and resource selection systems.