Low-Complexity Channel-Estimate Based Adaptive Linear Equalizer

In this letter, we propose a low-complexity channel-estimate based adaptive linear equalizer. The equalizer exploits coordinate descent iterations for computation of equalizer coefficients. The proposed technique has as low complexity as
${\cal O}(N_{u}(K+M))$
operations per sample, where
$K$
and
$M$
are the equalizer and channel estimator length, respectively, and
$N_{u}$
is the number of iterations such that
$N_u \ll K$
and
$N_u \ll M$
. Moreover, with dichotomous coordinate descent iterations, the computation of equalizer coefficients is multiplication-free and division-free, which makes the equalizer attractive for hardware design. Simulation shows that the proposed adaptive equalizer performs close to the minimum mean-square-error equalizer with perfect knowledge of the channel.