Decomposition Based Congestion Analysis of the Communication in B5G/6G TeraHertz High-Speed Networks

Abstract

The New MAC mechanism plays a key role in achieving the needed requirements of the B5G/6G radio technology and helps to avoid high-speed frequency issues and limitations. With the help of the ns-3 simulator, we generated 42 different cases for the purpose of analyzing the impact of the network load on the overall effective transmission rate. Therefore, the use of the data-adaptive decomposition method the Empirical Mode Decomposition (EMD) on our non-stationary system benefits in the extraction of the important meaningful components. However, due to the highlighted direction dependency finding of EMD, Ensembled EMD (EEMD) being direction independent shows better performance on our data series. The extracted trend based on the proposed method matches the fitting curve, while the fitting curve parameters can be clusterized into 2 main clusters congested and non-congested cases of the radio channel throughput signal.