Soft-output decision-feedback equalization with a priori information

Soft-output equalizers that exploit a priori information on the channel inputs play a central role in turbo equalization. Such equalizers are traditionally implemented with the forward-backward or BCJR algorithm, whose complexity is prohibitive for channels with large memory. Many reduced-complexity alternatives to the BCJR algorithm have been proposed that use a linear equalizer and use the a priori information to perform soft intersymbol interference cancellation. In this work, we propose a soft-feedback equalizer (SFE) that combines the equalizer output and the a priori information to improve interference cancellation. Also, by assuming a statistical model for the a priori information and the SFE output, we obtain an equalizer with linear complexity, as opposed to the quadratic complexity of some similar structures. Simulation results show that the SFE may perform within 1 dB of a system based on an BCJR equalizer, within 0.3 dB of quadratic complexity schemes, and consistently outperforms other linear complexity schemes.