Abstract
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.
Highlights
In recent years, many research efforts have been devoted to the study of detection algorithms for digital signals transmitted over channels affected by random parametric uncertainty, like multipath fading channels and AWGN channels with phase jitter
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
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
Summary
Many research efforts have been devoted to the study of detection algorithms for digital signals transmitted over channels affected by random parametric uncertainty, like multipath fading channels and AWGN channels with phase jitter (see, e.g., [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13] and references therein) In this field the attention has been progressively shifting from maximum likelihood (ML) sequence detection [2, 3, 4] to maximum a posteriori (MAP) symbol detection techniques [5, 6, 7, 8, 9, 10, 11, 12, 13] producing a posteriori probabilities (APPs) on the possible data decisions.
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