| Abstract: | The identification of the type of wireless propagation channel (e.g., Line of Sight (LOS) or Non Line of Sight (NLOS)) 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. The main contribution of this paper is to propose a novel approach for channel identification based on an improvement of VMD called Improved Variational Mode Decomposition (IVMD), where the optimal values of the hyper-parameters of VMD are automatically identified on the basis of the Shannon entropy of the signal output from the channel. Then, various features are extracted from the modes generated by IVMD and a sequential feature selection algorithm is applied to select the optimal features. This paper applies the proposed approach with IVMD to a data set generated by the authors with a wireless channel emulator, where 6 different propagation scenarios (including no fading conditions) are created for WiFi 802.11g signals, where only the preamble is used for channel identification. Even if channel identification based on the normalized preamble is a challenging classification problem, the proposed IVMD is able to outperform significantly the application of basic VMD, EMD and the time and frequency domain representations (as commonly done in literature) of the WiFi signals. |
