Skip to Main Content
A low complexity soft-input soft-output block decision feedback equalization (BDFE) algorithm is presented for Turbo equalization. Based on minimum mean square error criterion, the feedforward filter and feedback filter of BDFE are adaptively formulated by extracting symbol statistics from soft a priori information. The adoption of a priori information during filter design greatly benefit system performance. Unlike most other low complexity equalization algorithms with symbol-based detection, the proposed algorithm adopts a sub-optimum sequence-based method to evaluate an approximation of data symbol a posteriori probability (APP). The sequence-based APP approximation is enabled by hard a priori information from previous iteration, and it outperforms symbol-based detection method adopted by most other low complexity algorithms.