An ML Approach for Decoding Collision Slots

Konferenz: Smart SysTech 2014 - European Conference on Smart Objects, Systems and Technologies
01.07.2014 - 02.07.2014 in Dortmund, Deutschland

Tagungsband: ITG-Fb. 251: Smart SysTech 2014

Seiten: 2Sprache: EnglischTyp: PDF

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Schantin. Andreas; Ruland, Christoph (University of Siegen, Hoelderlinstr. 3, 57068 Siegen, Germany)

The tag inventory process in an EPCglocal Class-1 Generation-2 (EPCglobal Gen2) long-range Radio Frequency Identification (RFID) system is based on Framed Slotted ALOHA (FSA). Collisions between tags are inevitable in an FSA-based system and limit its maximal throughput. In this work we describe a simple Maximum Likelihood (ML) scheme for jointlydecoding R tag replies in a collision-slot, allowing the reader to decode all of the colliding tag replies and greatly increasing the probability of decoding at least on tag reply correctly.