Blind Equalization under Noisy Environment using Bias-compensated RLS method

In this paper, a new blind adaptive equalization algorithm under noisy environment is proposed. We consider a practical case where the noise of each transmission channel is unknown. By oversampling the channel output at twice the symbol rate, a single-input double-output channel can be obtained. We apply the recursive-least-squares (RLS) to tackle the blind equalization problem. With the noise-induced bias, RLS algorithm is biased. In order to eliminate the bias, we present a bias-compensated RLS (BCRLS) algorithm that can estimate the unknown additive noise online and the noise-induced bias can be therefore removed. The unbiased estimate of the channel characteristics obtained can be used for channel equalization. Simulations results are presented to demonstrate the performance of the proposed algorithm.