Collision Detection and Signal Recovery for UHF RFID Systems

Tan, Xi; Wang, He; Fu, Lingzhi; Wang, Junyu; Min, Hao; Engels, Daniel W.

doi:10.1109/tase.2016.2614134

Cited by 29 publications

(3 citation statements)

References 23 publications

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“…The authors in [11] employs a MMSE receiver which is capable of recovering 2 colliding tags (M = 2, J = 2) and obtain a throughput value of 0.841. In [12], voltage clustering is applied for the constellation points with a throughput value of 0.85 under the simulation setup M = 3, J = 2. With the proposed approach, we show that it is possible to decode 4 tags out of 4 colliding ones (M = 4, J = 4) by improving throughput value to the 1.756.…”

Section: Numerical Resultsmentioning

confidence: 99%

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ML-Aided Collision Recovery for UHF-RFID Systems

Akyildiz¹,

Ku²,

Harder³

et al. 2022

Preprint

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We propose a collision recovery algorithm with the aid of machine learning (ML-aided) for passive Ultra High Frequency (UHF) Radio Frequency Identification (RFID) systems. The proposed method aims at recovering the tags under collision to improve the system performance. We first estimate the number of tags from the collided signal by utilizing machine learning tools and show that the number of colliding tags can be estimated with high accuracy. Second, we employ a simple yet effective deep learning model to find the experienced channel coefficients. The proposed method allows the reader to separate each tag's signal from the received one by applying maximum likelihood decoding. We perform simulations to illustrate that the use of deep learning is highly beneficial and demonstrate that the proposed approach boosts the throughput performance of the standard framed slotted ALOHA (FSA) protocol from 0.368 to 1.756, where the receiver is equipped with a single antenna and capable of decoding up to 4 tags.

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Section: Numerical Resultsmentioning

confidence: 99%

“…Later, they extend their work by also proposing a channel estimation technique using the post-preamble symbols in [11]. In [12], the number of tag estimation is coupled with a channel estimation technique for collision recovery and [13] studies code designs to recover tag collisions given the number of colliding tags.…”

Section: Introductionmentioning

confidence: 99%

ML-Aided Collision Recovery for UHF-RFID Systems

Akyildiz¹,

Ku²,

Harder³

et al. 2022

Preprint

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show abstract

“…It is independent of the sequence number and makes use of dynamic frame time slots. It is built on the concepts of probability and slotting [ 22 , 23 ]. As shown in Figure 13 , the specific flows of the Q algorithm are as follows:…”

Section: Methodsmentioning

confidence: 99%

Design of Meat Product Safety Information Chain Traceability System Based on UHF RFID

Qiao

Hao

Guo

2023

Sensors

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As a result of the current imperfection of the meat traceability system, there have been numerous food safety events with serious consequences. In this paper, a meat product information traceability system is designed to efficiently prevent such problems. This system develops an identification tag information reader based on ultra-high frequency (UHF) Radio Frequency Identification (RFID). It is compatible with LoRa wireless, USB serial port, RS485, and RJ45 Ethernet connection. Among them, the efficiency analysis of the Q-value algorithm finds that the recognition rate of the system reaches a maximum of about 0.367 when the number of tags n is about the frame length. The multi-tag anti-collision algorithm design based on the algorithm improves the efficiency of information collection in production and distribution links. The traceability code identification scheme is designed to effectively match various links, and the platform of system is built using LabVIEW2014 software, which has five sub-modules including user management, farm management, slaughter management, logistics management, and sales management. The system uses MySQL databases to store traceability information so that users can complete their queries by entering the traceability code on the system platform. The system not only has a low cost and a broad range of applications, but it also realizes the tracking record of meat product traceability information from breeding to selling, completes the function from information collection to information inquiry, and solves the problem of the incomplete traceability information chain. In addition, the system not only enhances the informational transparency of meat products in the product supply chain but also provides information for the regulatory authorities to effectively monitor safety.

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Improved Bayesian Method with Collision Recovery for RFID Anti-collision

Chu

2019

Lecture Notes in Computer Science

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Collision Detection and Signal Recovery for UHF RFID Systems

Cited by 29 publications

References 23 publications

ML-Aided Collision Recovery for UHF-RFID Systems

ML-Aided Collision Recovery for UHF-RFID Systems

Design of Meat Product Safety Information Chain Traceability System Based on UHF RFID

Improved Bayesian Method with Collision Recovery for RFID Anti-collision

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