A rule-based channel equalizer with learning capability

The problem of channel equalization is concerned with reconstructing binary signal being transmitted through a dispersive communication channel and then corrupted by additive noise. With the aid of fuzzy concepts and neural-like learning, this paper presents a rule-based approach to this problem. A self-organizing algorithm consisting of learning, pruning, and refining processes is developed aiming at building the rule-base from labeled observations. The rule-based equalizer makes the decision on the basis of measuring the similarity between the current observation and the obtained rule prototypes. The simulation studies on linear and nonlinear channels were used to demonstrate the performance of the proposed approach.