Weighted error support algorithm for accelerated convergence channel equalizer

Abstract

To accelerate the convergence speed of adaptive equalizers and improve the robustness against inter-symbol interference and noise, a new weighted-error-based adjustable stepsize is proposed. The algorithm intends to achieve this acceleration while maintaining a comparable steady-state error to other related approaches. The key innovation is in leveraging two patterns of gradient, named forced-change gradients, to adjust the stepsize. These are the gradient of error and the gradient of coefficients, in which we consider the difference between their estimated values at the current samples and a scaled version of their estimated values from previous samples. Combining these gradients, we create a weighted-error parameter used for adjusting the stepsize. The motivation of the proposed approach is to exploit the combination of these two gradients to accelerate the convergence speed as well as to reduce the steady-state error. The algorithm delivers distinguished outcomes, especially in terms of bit-error-rate, with fewer training symbols compared with others. Furthermore, the algorithm shows stable performance under various levels of the signal-to-noise ratio. The superiority in attenuating the inter-symbol interference and accelerating convergence is demonstrated through the simulation results. Computer simulations are also provided to support theoretical findings.