Asynchronous multi-user detection using inference algorithmsAsynchronous multi-user detection using inference algorithmsΑσύγχρονη εκτίμηση πολλαπλών χρηστών με αλγόριθμους συμπερασμού
Διπλωματική Εργασία
Diploma Work
2017-11-132017enThis work highlights state-of-the-art probabilistic graphical models and infer-
ence algorithms that exploit rather than avoid the symbol-level asynchrony
in multi-user wireless communications. More specically, the recently pro-
posed inference algorithm SigSag is studied, where multiple users transmit
their data packets at the same time and frequency channels, with however
random delays; assuming channel state information (CSI) at the receiver,
linear equations are formed, which produce a probabilistic graphical model
(PGM), amenable to inference algorithms. This work implements the sum-
product belief propagation algorithm on the crafted PGM and a) derives
the message passing equations, b) studies initialization and c) complements
with CSI estimation, using linear minimum mean squared error (LMMSE)
estimator. Performance was tested for 2 or 3 users. It was found that per-
formance was sensitive to initialization, as expected, due to the inherently
loopy nature of the crafted PGM for small packet lengths. On the contrary,
bit error rate (BER) decreases with increasing packet length, at the expense
of convergence time. Moreover, reduced convergence time results to higher
BER.http://creativecommons.org/licenses/by/4.0/Πολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών ΥπολογιστώνTsetis_Ioannis_Dip_2017.pdfChania [Greece]Library of TUC2017-11-13application/pdf609.7 kBfree
Tsetis Ioannis
Τσετης Ιωαννης
Bletsas Aggelos
Μπλετσας Αγγελος
Paterakis Michalis
Πατερακης Μιχαλης
Karystinos Georgios
Καρυστινος Γεωργιος
Πολυτεχνείο Κρήτης
Technical University of Crete
Networking
Inference theory
Probabilistic graphical models
Detection & estimation theory