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Ασύγχρονη εκτίμηση πολλαπλών χρηστών με αλγόριθμους συμπερασμού

Tsetis Ioannis

Πλήρης Εγγραφή

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Περίληψη

This work highlights state-of-the-art probabilistic graphical models and infer-ence algorithms that exploit rather than avoid the symbol-level asynchronyin multi-user wireless communications. More specically, the recently pro-posed inference algorithm SigSag is studied, where multiple users transmittheir data packets at the same time and frequency channels, with howeverrandom 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) derivesthe message passing equations, b) studies initialization and c) complementswith 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 inherentlyloopy nature of the crafted PGM for small packet lengths. On the contrary,bit error rate (BER) decreases with increasing packet length, at the expenseof convergence time. Moreover, reduced convergence time results to higherBER.

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