Design of Criterion for Adaptively Scaled Belief in Iterative Large MIMO Detection

Takumi TAKAHASHI  Shinsuke IBI  Seiichi SAMPEI  

Publication
IEICE TRANSACTIONS on Communications   Vol.E102-B   No.2   pp.285-297
Publication Date: 2019/02/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.2018EBP3136
Type of Manuscript: PAPER
Category: Fundamental Theories for Communications
Keyword: 
multi-user multi-input multi-output (MU-MIMO),  Gaussian belief propagation (GaBP),  iterative detection,  soft interference cancellation,  adaptive belief scaling,  

Full Text: PDF(2.1MB)
>>Buy this Article


Summary: 
This paper proposes a new design criterion of adaptively scaled belief (ASB) in Gaussian belief propagation (GaBP) for large multi-user multi-input multi-output (MU-MIMO) detection. In practical MU detection (MUD) scenarios, the most vital issue for improving the convergence property of GaBP iterative detection is how to deal with belief outliers in each iteration. Such outliers are caused by modeling errors due to the fact that the law of large number does not work well when it is difficult to satisfy the large system limit. One of the simplest ways to mitigate the harmful impact of outliers is belief scaling. A typical approach for determining the scaling parameter for the belief is to create a look-up table (LUT) based on the received signal-to-noise ratio (SNR) through computer simulations. However, the instantaneous SNR differs among beliefs because the MIMO channels in the MUD problem are random; hence, the creation of LUT is infeasible. To stabilize the dynamics of the random MIMO channels, we propose a new transmission block based criterion that adapts belief scaling to the instantaneous channel state. Finally, we verify the validity of ASB in terms of the suppression of the bit error rate (BER) floor.