Soft-In Soft-Output Detection in the Presence of Parametric Uncertainty via the Bayesian EM Algorithm | Zendy

AS Gallo | Zendy; Giorgio M. Vitetta | Zendy

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Soft-In Soft-Output Detection in the Presence of Parametric Uncertainty via the Bayesian EM Algorithm

Author(s) -

AS Gallo,

Giorgio M. Vitetta

Publication year - 2005

Publication title -

eurasip journal on wireless communications and networking

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.461

H-Index - 64

eISSN - 1687-1499

pISSN - 1687-1472

DOI - 10.1155/wcn.2005.100

Subject(s) - computer science , expectation–maximization algorithm , maximization , parametric statistics , algorithm , bayesian probability , detector , channel (broadcasting) , block (permutation group theory) , wireless , maximum likelihood , mathematical optimization , artificial intelligence , telecommunications , mathematics , statistics , geometry

We investigate the application of the Bayesian expectation-maximization (BEM) technique to the design of soft-in soft-out (SISO) detection algorithms for wireless communication systems operating over channels affected by parametric uncertainty. First, the BEM algorithm is described in detail and its relationship with the well-known expectation-maximization (EM) technique is explained. Then, some of its applications are illustrated. In particular, the problems of SISO detection of spread spectrum, single-carrier and multicarrier space-time block coded signals are analyzed. Numerical results show that BEM-based detectors perform closely to the maximum likelihood (ML) receivers endowed with perfect channel state information as long as channel variations are not too fast.