Set-membership identification for adaptive equalization

This paper proposes employing set-membership identification for adaptive equalization. A novel feature of the set-membership identification (SMI) is selective update of the estimates for the channel parameters. This is in sharp contrast with conventional recursive schemes such as recursive least-squares (RLS) which updates continually regardless of the benefit of updates. Simulation results show that the SMI algorithm uses less than 20% of the data for parameter updates in the training mode and less than 10% of the data in the decision-directed mode, without much performance degradation in terms of bit error rate.