Soft-in Soft-out detection using partial gaussian approximation

This paper concerns the soft-in soft-out detection in a coded communication system, where the

transmitted symbols are discrete valued, and the exact a posteriori probability (APP) detection often involves

prohibitive complexity. By using the properties of Gaussian functions, an approximate approach to the APP

detection is devised with the idea that, in the computation of the APP of each symbol, the remaining symbols

are distinguished based on their contributions to the APP of the concerned symbol, and the symbols with

less contributions are approximated as (continuous) Gaussian variables [hence the name partial Gaussian

approximation (PGA)] to reduce the computational complexity. The connection between the PGA detector

and the reduced dimension maximum a posteriori detector (RDMAP) is investigated. It is shown that, PGA is

equivalent to RDMAP, but it has a complexity much lower than that of RDMAP, i.e., PGA can be regarded as

an efcient implementation of RDMAP. In addition, the application of PGA in intersymbol interference (ISI)

channel equalization is also investigated.We show that PGA allows further signicant complexity reduction

by exploiting the circulant structure of the system transfer matrix, which makes PGA very attractive in

handling severe ISI channels with large memory length.