The computational requirement to implement the optimal Bayesian symbol-decision equaliser using RBF network (1) can be very high as the full RBF Bayesian solution usually requires a large number of centres. To reduce the implacementation complexity, we propose to use a subset number of the full RBF network's centres to generate a subset equaliser. The centres to be selected for the subset equaliser are those that have their Euclidean distance close to the equaliser's current input vector. Our results show that the number of centres can be greatly reduced without significant degradation in classification performance.
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