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M. Rupp, M. Bueno Delgado, C. Angerer, S. Schwarz:
"ML Estimation of Population Size when Observing Multiple Fill Levels in Slotted Aloha";
Talk: IEEE Int. Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brisbane; 04-19-2015 - 04-24-2015; in: "ICASSP 2015", (2015), 5525 - 5529.

An open problem in slotted Aloha protocols is to optimally estimate
the number of participants as such knowledge is crucial to select the
optimal frame length. First results are known in literature based on
observing the slot fill levels in case of empty slots and single occupancies
(singleton slots). Advances in signal processing allow now
also to decode successfully slots with higher fill levels, for example,
due to multiple antennas. In this paper we derive the maximum
likelihood estimator when arbitrary occupancies up to a maximal fill
level R have been observed. Due to our novel approach, the derivation
is rather simple and its implementation is of low complexity.

RFID tags, ML-estimation, collision mitigation

http://publik.tuwien.ac.at/files/PubDat_239376.pdf

Project Head Christoph Mecklenbräuker:
Christian Doppler Lab "Funktechnologien für nachhaltige Mobilität"

Project Head Markus Rupp:
Signal and Information Processing in Science and Engineering II: Theory and Implementation of Distributed Algorithms