Abstract:
Powerline communication (PLC) channels are known to exhibit multipath propagation
behaviour. The authors present a model-based framework to address the challenge
of clustering multipath propagation components (MPCs) in PLC channels for indoor
low-voltage (LV) environments. The framework employs a range of finite-mixture models
(FMMs), including the gamma mixture model, the inverse gamma mixture model,
the Gaussian mixture model, the inverse Gaussian mixture model, the Nakagami
mixture model, the inverse Nakagami mixture model (INMM) and the Rayleigh mixture
model, to identify clusters of MPCs. A measurement campaign of an unknown indoor
LV PLC channel is conducted to obtain a channel response. From the channel response,
the delay and magnitude parameters of the MPCs are extracted using the spacealternating
generalised expectation maximisation algorithm adopted only for these
parameters. A maximum likelihood approach and the expectation–maximisation
algorithm are employed to fit the FMMs to the MPC delay-magnitude dataset to cluster
MPCs in the delay domain. The results of the model-fitting process are then evaluated
using the corrected Akaike information criterion (AICc), which enables a fair comparison
of the candidate models over the feasible and finite range of clusters. A novel
algorithm is introduced for estimating the feasible and finite range of clusters using
the extracted delay and magnitude MPC parameters. The AICc’s ranking results show
that the INMM model provides the best fit. Davies–Bouldin (DB) and Calinski–Harabasz
(CH) indexes are used to compare the model-based clustering approach to the conventional
distance-based clustering methods. Validation results show that CH and DB
indexes closely agree in the optimal number of MPC clusters for model-based clustering,
which corresponds to the most within-cluster compactness of MPCs and to the
most between-cluster separation in the delay domain.
