Complex Adaptive LMS Algorithm Employing the Conjugate Gradient Principle for Channel Estimation and Equalization
Abbreviated Journal Title
Circuits Syst. Signal Process.
Adaptive filter; Conjugate gradient; Optimal stepsize; LMS; Channel; estimation; Equalization; CONVERGENCE ANALYSIS; RECEIVERS; Engineering, Electrical & Electronic
The Complex Block Least Mean Square (LMS) technique is widely used in adaptive filtering applications because of its simplicity and efficiency from a theoretical and implementation standpoint. However, the limitations of the Complex Block LMS technique are slow convergence and dependence on the proper choice of the stepsize or convergence factor. Moreover, its performance degrades significantly in time-varying environments. In this paper, a novel adaptive LMS technique named the Complex Block Conjugate LMS algorithm, CBC-LMS, is presented. Based on the Conjugate Gradient Principle, the proposed technique searches orthogonal directions to update the filter coefficients instead of the negative gradient directions used in the Complex Block LMS algorithm. In addition, the CBC-LMS algorithm derives optimal stepsizes to adjust the adaptive system coefficients at each iteration. As a result, the developed method overcomes the inherent limitations of the existing Complex Block LMS algorithm. The performance of the CBC-LMS technique is tested in wireless channel estimation and equalization applications, using both computer simulations and laboratory experiments. Furthermore, the developed technique is compared to the Complex Block LMS method and a recently proposed method, which is called Complex Optimal Block Adaptive LMS (OBA-LMS). The experimental and simulation results confirm that the proposed CBC-LMS technique achieves faster convergence with comparable accuracy and reduced computational complexity, relative to the existing techniques.
Circuits Systems and Signal Processing
"Complex Adaptive LMS Algorithm Employing the Conjugate Gradient Principle for Channel Estimation and Equalization" (2012). Faculty Bibliography 2010s. 2957.