Exact Bayes´ theorem based probabilistic data association for iterative MIMO detection and decoding

In our previous work, it was shown that the conventional approximate Bayes´ theorem based probabilistic data association (PDA) algorithms output “nominal APPs”, which are unsuitable for the classic architecture of iterative detection and decoding (IDD) aided receivers. To circumvent this predicament, in this paper we propose an exact Bayes´ theorem based logarithmic domain PDA (EB-Log-PDA) method, whose output has similar characteristics to the true APPs, and hence it is readily applicable to the classic IDD architecture of multiple-input multiple-output (MIMO) systems using M-ary modulation. Furthermore, we demonstrate that introducing inner iterations into EB-Log-PDA, which is common practice in conventional-PDA aided uncoded MIMO systems, would actually degrade the IDD receiver´s performance, despite significantly increasing the overall computational complexity of the IDD receiver. Finally, we show that the EB-Log-PDA based IDD scheme operating without any inner PDA iterations has a similar performance to that of the optimal maximum a posteriori (MAP) detector based IDD receiver, while imposing a significantly lower computational complexity in the scenarios considered.