Low-complexity iterative decoding with decision-aided equalization for magnetic recording channels

Turbo codes are applied to magnetic recoding channels by treating the chann el as a rate-one convolutional code that requires a soft a posteriori proba bility (APP) detector for channel inputs. The complexity of conventional AP P detectors, such as the BCJR algorithm [2] or the soft-output Viterbi algo rithm (SOVA) [3], grows exponentially with the channel memory length, This paper derives a new APP module for binary intersymbol interference (ISI) ch annels based on minimum mean squared error (MMSE) decision-aided equalizati on (DAE), whose complexity grows linearly with the channel memory length, a nd it shows that the MMSE DAE is also optimal by the maximum a posteriori p robability (MAP) criterion. The performance of the DAE is analyzed, and an implementable turbo-DAE structure is proposed. The reduction of channel APP detection complexity reaches 95% for a five-tap ISI channel when the DAE i s applied. Simulations performed on partial response channels show close to optimum performance for this turbo-DAE structure. Error propagation of the DAE is also studied, and two fixed-delay solutions are proposed based on c ombining the DAE with the BCJR algorithm.