An ML Approach for Decoding Collision Slots

-. Schantin. Andreas, C. Ruland
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Abstract

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.
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碰撞槽译码的ML方法
epclocal Class-1 Generation-2 (EPCglobal Gen2)远程RFID (Radio Frequency Identification, RFID)系统的标签盘点过程基于框架开槽ALOHA (Framed Slotted ALOHA)。在基于fsa的系统中,标签之间的冲突是不可避免的,并且限制了其最大吞吐量。在这项工作中,我们描述了一个简单的最大可能性(ML)方案,用于在冲突槽中联合解码R标签回复,允许阅读器解码所有冲突标签回复,并大大提高解码至少一个标签回复的概率。
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