Set-membership adaptive constrained constant modulus reduced-rank algorithm for beamforming

In this work, we propose an adaptive set-membership (SM) reduced-rank filtering algorithm using the constrained constant modulus (CCM) criterion for beamforming. We develop a stochastic gradient (SG) type algorithm based on the concept of SM technique for adaptive implementation. The filter weights are updated only if the bounded constraint cannot be satisfied. In addition, we also propose a scheme of time-varying bound and incorporate parameter dependence to characterize the environment for improving the tracking performance of the proposed algorithm. Simulation results show that the proposed adaptive SM reduced-rank beamforming algorithm with dynamic bounds achieves superior performance to previously reported methods at a reduced update rate.