A new adaptive turbo equalizer with soft information classification

Linear turbo equalizers with/without channel estimation have been exploited due to their good performance with low complexity compared to a maximuma posteriori (MAP) turbo equalizer. Much work has focused on channel estimate-based minimum mean square error (MMSE) turbo equalizers. However, an MMSE turbo equalizer still requires higher complexity than an adaptive turbo equalizer such as with a normalized least mean square (NLMS) turbo equalizer. Even if adaptive turbo equalizers converge, there is often a performance loss compared to an MMSE turbo equalizer because the adaptive turbo equalizers treat soft decision data as stationary. In order to reduce this loss, we propose a new adaptive turbo equalizer that uses the soft decision data to switch among a set of K different equalizers to approximate the time varying MMSE behavior. Simulations show that the proposed switching-based NLMS turbo equalizer has better bit error rate (BER) performance than a conventional NLMS turbo equalizer by as much as 0.6dB.