A multi-stage blind clustering algorithm is proposed for equalisation of M-QAM channels. A navel hierarchical decomposition divides the overall task of equalising a highorder QAM channel into much simpler sub-tasks. Each subtask can be accomplished fast and reliably using a blind clustering algorithm derived originally for 4-QAM signals. The well-known constant modulus algorithm (CMA) is used as a benchmark to assess this novel multi-stage blind equaliser and it is demonstrated that the new blind adaptive algorithm achieves much faster convergence. This multi-stage clustering equaliser only quires slightly more computations than the very simple CMA and, like the latter, its computational complexity does not increase as the levels of digital symbols increase.
This paper examines the application of the radial basis function (RBF) network to the modelling of the Bayesian equaliser. In particular, we study the effects of delay order d on decision boundary and attainable bit error rate (BFR) performance. To determine the optimum delay parameter for minimum BER performance, a simple BER estimator is proposed. The implementation complexity of the RBF network grows exponentially with respect to the number of input nodes. As such, the full implementation of the RBF network to realise the Bayesian solution may not be feasible. To reduce some of the implementation complexity, we propose an algorithm to perform subset model selection. Our results indicate that it is possible to reduce model size without, significant degradation in BER performance. Indexing Term: Bayesian equaliser, neural networks, RBF network, RER.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.