Complex adaptive FIR digital filtering algorithm with time-varying independent convergence factors

The Complex Least Mean Square (Complex LMS) algorithm suffers from slow convergence and dependence on the choice of the convergence factor. In this paper, a novel Complex FIR Block Adaptive algorithm (Complex OBA-LMS) for digital filtering, which overcomes the inherent limitations of the Complex LMS, is presented. The proposed technique employs optimally derived convergence factors, updated at each block iteration, for independently adjusting the real and imaginary components of the Complex FIR adaptive filter coefficients. Simulation results confirm the performance improvement in terms of convergence speed and accuracy of the proposed method.