Abstract
Clustering of multipath components (MPC) simplifies the analysis of the wireless environment to produce the channel impulse response which leads to an effective channel model. Automatic clustering of the MPC has been utilized as a replacement to the traditional manual approach. The arbitrary nature of MPC that interacts with the surrounding environment still challenges wireless researchers to utilize algorithms that are fitted based on the measured data of the channel. For enhancing the clustering process, visualization plays a considerable part in inferring knowledge in the dataset and the clustering results. Hence, the combination of the automatic and manual approach in clustering enhances the process, leading to efficient and accurate extraction of the clusters using visualization. Self-Organizing Map (SOM) has been proven helpful in aiding the clustering and visualization in different fields process which can be combined to form a hybrid system in clustering problems. In this paper, the investigation of the effectiveness of SOM in visualizing the MPC extracted from the COST2100 channel model (C2CM) and visualize clustering tendencies of the dataset.
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Alejandrino, J. et al. (2022). Utilization of Self-organizing Maps for Map Depiction of Multipath Clusters. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing & Optimization. ICO 2021. Lecture Notes in Networks and Systems, vol 371. Springer, Cham. https://doi.org/10.1007/978-3-030-93247-3_41
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