Collision alignment: an RFID anti-collision algorithm assisted by orthogonal signal detection and analogy principle
Jumin Zhao (),
Na Li (),
Deng-ao Li (),
Ruiqin Bai (),
Biaokai Zhu () and
Xinyi Liu ()
Additional contact information
Jumin Zhao: Taiyuan University of Technology
Na Li: Shanxi Co., Ltd., China Mobile Communication Corporation
Deng-ao Li: Taiyuan University of Technology
Ruiqin Bai: Taiyuan University of Technology
Biaokai Zhu: Taiyuan University of Technology
Xinyi Liu: Chang’an University
Telecommunication Systems: Modelling, Analysis, Design and Management, 2017, vol. 66, issue 1, No 10, 144 pages
Abstract:
Abstract The replenishment of resource management problem is tough and necessary in RFID system, which requires tags to report the existing information uninterruptedly for guaranteeing goods supply. However, the random or synchronous reply may lead to collision, further, make the system low efficiency. Based on the fusion method between orthogonal code compiling category ID and analogy principle, we present the collision tolerance protocol UACP to identify tags efficiently, which contains UCOM and ACAM mechanisms. UCOM relies on orthogonal signals’ correlation to realize the tolerance and accurate category identification by making a comparison between the collision and the whole elements. ACAM depends on the analogy principle to estimate the existing number from microcosmic to macrocosmic only using the singleton slots, and the tags can transmit one-bit affirming message after a series of ACK commands. UACP also analyses some factors, such as the permutation and combination of multiple sets orthogonal codes, asynchronous code offset, signal missing rate, signal misjudgment probability, and shows the advantages on slot utilization rate, identification rate and time efficiency compared with other algorithms. After adopting BPSK modulation and USRP platform, UACP is verified the time-efficiency increasing of 25–60%.
Keywords: Anti-collision; Orthogonal; Analogy; RFID (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11235-017-0274-7
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