K-harmonic means clustering based blind equalization in hostile environments

A novel blind clustering based equalizer suitable for channels with intersymbol interference (ISI), nonlinear distortions and cochannel interference (CCI) is proposed. Blind channel estimation is performed by partitioning the baseband data at the receiver into clusters that are identified using a new class of clustering algorithms known as K-harmonic means (KHM/sub p/). The KHM/sub p/ algorithms are insensitive to the initialization of the cluster centers, owing to a built-in boosting function, and provide reliable estimates of the cluster centers. The identified cluster representatives are then mapped to the corresponding combinations of input symbols using a discrete hidden Markov model formulation of the channel states and the mapping is used to compute the branch metrics in a cluster-based Viterbi detector. The performance of the proposed equalizer in hostile environments is illustrated with computer simulations.