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.

识别无线传播信道的类型（例如，视距（LOS）或非视距（NLOS））是无线通信设计和部署中的重要功能，尤其是在丰富的传播环境中。无线信道特性不仅在视距 (LOS) 和非视距 (NLOS) 无线传播条件之间而且在不同的 NLOS 环境中都可以非常具体。

最近，机器学习方法越来越多地用于区分和分类渠道特征，本文就是这一趋势的一部分。特别是，本文提出将机器学习与最近提出的称为变分模式分解 (VMD) 的信号处理工具相结合，该工具是一种分解算法，可将时间序列分解为具有特定稀疏特性的几种模式。VMD 本身是经验模式分解 (EMD) 的改进，在分类问题上表现出优于 EMD 的性能。在通道识别问题中实际部署 VMD 的一个问题是超参数的存在，必须针对应用的上下文进行调整。本文的主要贡献是提出了一种新的基于 VMD 改进的信道识别方法，称为改进的变分模态分解 (IVMD)，其中 VMD 的超参数的最优值是基于 Shannon 自动识别的。从通道输出的信号的熵。然后，从 IVMD 生成的模式中提取各种特征，并应用顺序特征选择算法来选择最优特征。本文将所提出的 IVMD 方法应用于作者使用无线信道仿真器生成的数据集，其中为 WiFi 802.11g 信号创建了 6 种不同的传播场景（包括无衰落条件），其中仅前导码用于信道识别.