MIMO Multipath Component Clustering using k-Deep Autoencoder

In modern wireless communication systems, accurate characterization of wireless propagation channels remains a significant challenge. In multiple-input multiple-output (MIMO) wireless systems, a double-directional channel can be achieved by utilizing multipath components' spatial and temporal properties (MPCs). Grouping the MPC can simplify the parameters as a trade-off between complexity and accuracy. This paper implements k-Deep Auto Encoder (k-DAE), AE+k-means, and K Power Means (KPM) clustering approaches and compares their performance in clustering wireless propagation multipaths in indoor and outdoor scenarios. The results show that AE+k-means performs better than k-DAE in indoor scenarios by 25.48%, while k-DAE performs 24.60 % better in outdoor scenarios. The KPM algorithm performs best in all indoor scenarios among the three algorithms, with a significant increase of 4.38% and 11.062% to AE+k-means and k-DAE, respectively. However, both k-DAE and AE+k-means have quite similar performance in outdoor scenarios. The study also highlights the first use of autoencoders in clustering the MPCs. The results indicate that k-DAE can be used as an alternative clustering method in channel modeling. Future works envisioned applying the approach to other wireless channel models.