Reduced-state soft-input/soft-output algorithms for complexity reduction in iterative and non-iterative data detection

Soft-input/soft-output (SISO) algorithms have been widely used for iterative detection in various applications since this technique was introduced for decoding turbo codes. However, the complexity of the SISO algorithms is a major concern in the detector implementation. A novel way to simplify the SISO algorithms is proposed based on the concept of state reduction via decision feedback. The resulting complexity reduction is exponential in the number of feedback taps. The proposed low-complexity SISO algorithm can be applied directly in place of the standard SISO (e.g., a full-state forward-backward algorithm). Additionally, thresholding the soft-outputs of the reduced-state (RS) SISO can provide a robust and effective alternative to RS sequence detectors. Simulation results are also provided to illustrate the advantages of the RS-SISO