Turbo equalization using probabilistic data association

We investigate turbo equalization using an algorithm called probabilistic data association (PDA). We first propose a general structure for PDA, which consists of a linear interference cancellation step followed by a probabilistic data association step in every iteration. Based on the general structure, we show that the original PDA belongs to one variation and it is computationally inefficient. We then unveil that the popular soft linear MMSE (SLMMSE) equalizer can be considered as one sweep within a generalized PDA. Such a connection implies that further performance improvement over the SLMMSE equalizer is possible if the PDA is applied instead in turbo equalization. We also provide a way for the PDA equalizer to incorporate the a priori probability, which makes the PDA readily applicable to turbo equalization.