A noise constrained LMS algorithm for fading channel communications

In many identification and tracking algorithms, an accurate estimate of the measurement noise variance is available. A partially adaptive LMS-type algorithm is developed which can exploit this information while maintaining the simplicity and robustness of LMS. This noise constrained LMS (NCLMS) algorithm is a type of variable stepsize LMS algorithm, which is derived by adding constraints to the mean-square error optimization. The convergence and steady state performance are analyzed. Both the theoretical results and simulations show that NCLMS can dramatically outperform LMS and other variable step size LMS algorithms in a sufficiently noisy environment