Channel Identification with Improved Variational Mode Decomposition

The identification of the wireless channel characteristics is an important function in the wireless communication design and deployment especially in rich propagation environments. The wireless channel characteristics can be quite specific not only between Line of Sight (LoS) and Non-Line-of-Sight (NLoS) wireless propagation conditions but also in different NLoS environments. In recent times, machine learning approaches have been increasingly used to differentiate and classify channel characteristics and this paper is part of this trend. In particular, this paper proposes the combination of machine learning with a recently proposed signal processing tool called Variational Mode Decomposition (VMD), which is a decomposition algorithm that decomposes a time series into several modes which have specific sparsity properties. VMD itself is a refinement of the Empirical Mode Decomposition (EMD) and demonstrated a superior performance to EMD for classification problems. One issue for the practical deployment of VMD in channel identification problems is the presence of hyper-parameters, which must be tuned for the applied context. This paper proposes a novel Improved Variational Mode Decomposition (IVMD) approach for channel identification, where the optimal values of the hyper-parameters are automatically identified on the basis of the Shannon entropy of the signal output.

BALDINI Gianmarco;  BONAVITACOLA Fausto; 

2022-09-27

ELSEVIER

JRC129000

1874-4907 (online),   

https://www.sciencedirect.com/science/article/pii/S1874490722001495,    https://publications.jrc.ec.europa.eu/repository/handle/JRC129000,   

10.1016/j.phycom.2022.101871 (online),