A variational inference framework for soft-in soft-out detection in multiple-access channels
IEEE Transactions on Information Theory
| Hi-index | 754.90 |
This correspondence investigates iterative soft-in-soft-out (SISO) detection in coded multiple access channels, with Gray-coded M-ary quadrature amplitude modulation (QAM) for the channel symbols. The proposed solution may be summarized as a generic iterative detection scheme called bit-level equalization and soft detection (BLESD), which is an extension of a unified variational inference framework for binary SISO detection proposed in our prior work. This new strategy fundamentally differs from the conventional symbol detector, in that data symbols are transparent to the new detector. Rather, soft estimates of the bits that make up the symbols are directly and naturally obtained by the detector in terms of posterior probabilities given the channel observation, facilitating efficient message-passing in joint detection and decoding. Case studies that illustrate the applications of the proposed scheme are presented for turbo multiuser detection (MUD) for multiple-access interference (MAI) channels and turbo equalization for inter-symbol interference (ISI) channels.