A regional multimodulus algorithm for blind equalization of QAM signals: Introduction and steady-state analysis

It is well known that constant-modulus-based algorithms present a large mean-square error for high-order quadrature amplitude modulation (QAM) signals, which may damage the switching to decision-directed-based algorithms. In this paper, we introduce a regional multimodulus algorithm for blind equalization of QAM signals that performs similar to the supervised normalized least-mean-squares (NLMS) algorithm, independently of the QAM order. We find a theoretical relation between the coefficient vector of the proposed algorithm and the Wiener solution and also provide theoretical models for the steady-state excess mean-square error in a nonstationary environment. The proposed algorithm in conjunction with strategies to speed up its convergence and to avoid divergence can bypass the switching mechanism between the blind mode and the decision-directed mode.

► We propose a regional multimodulus algorithm for blind equalization of QAM signals.
► It performs similarly to the NLMS algorithm, independently of the QAM order.
► It presents a relatively fast convergence and does not diverge.
► It can bypass the switching mechanism between the blind and decision-directed modes.
► We provide theoretical models for both stationary and nonstationary environments.