A Variational Inference Framework for Soft-In-Soft-Out Detection in Multiple Access Channels

We propose a unified framework for deriving and studying soft-in soft-out (SISO) detection in multiple-access channels using the concept of variational inference. The proposed framework may be used in multiple-access interference (MAI), intersymbol interference (ISI), and multiple-input multiple-output (MIMO) channels. Without loss of generality, we will focus our attention on turbo multiuser detection, to facilitate a more concrete discussion. It is shown that, with some loss of optimality, variational inference avoids the exponential complexity of a posteriori probability (APP) detection by optimizing a closely related, but much more manageable, objective function called variational free energy. In addition to its systematic appeal, there are several other advantages to this viewpoint. First of all, it provides unified and rigorous justifications for numerous detectors that were proposed on radically different grounds, and facilitates convenient joint detection and decoding (utilizin...

• Type: Journal
• Year: 2008
• Where: CORR
• Authors: Darryl Dexu Lin, Teng Joon Lim
• Keywords: CORR 2008, Detection, Education, Variational Free Energy, Variational Inference