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Novel Three-Hierarchy Multiple-Tag-Recognition Technique for Next Generation RFID Systems
- Source :
- IEEE Transactions on Wireless Communications. 19:1237-1249
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- In this paper, we propose a novel hierarchical radio-frequency identification (RFID) tag-recognition method based on blind source separation (BSS), graph-based automatic modulation classification (AMC), and direct-sequence spread-spectrum (DSSS). In our proposed method, RFID tags can be modulated using different modulation schemes according to different scenarios (e.g., different users or different tag devices). For each modulation scheme, the direct-sequence spread-spectrum strategy is employed to allow simultaneous transmissions of multiple commands. In the signal separation phase, BSS is employed to separate different transmitted signals. Then in the first hierarchy of the recognition phase, different modulation types are adopted to distinguish different users, the graph-based AMC is built upon the periodicity of the modulated signals: the cyclic spectrum of the received signal is established; the graph representation is then constructed according to the cyclic spectrum. Ultimately, robust features are extracted from the graph representation. In the second hierarchy of the recognition phase, the DSSS scheme is utilized to differentiate the control or sensed data carried by individual tags; the signature sequence set with low cross-correlations can be generated from Kasami sequences. In the third hierarchy of the recognition phase, the information data are thus spread by these signature sequences. In our proposed new RFID framework, multiple tags can transmit signals simultaneously in the same frequency band where each tag signal can still be separated and identified and its carried information can be recovered. Monte Carlo simulation results demonstrate the promising performance of our proposed new RFID scheme.
- Subjects :
- Frequency band
Computer science
business.industry
Applied Mathematics
Monte Carlo method
020206 networking & telecommunications
Pattern recognition
02 engineering and technology
Direct-sequence spread spectrum
Blind signal separation
Computer Science Applications
Modulation
0202 electrical engineering, electronic engineering, information engineering
Graph (abstract data type)
Artificial intelligence
Electrical and Electronic Engineering
business
Subjects
Details
- ISSN :
- 15582248 and 15361276
- Volume :
- 19
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Wireless Communications
- Accession number :
- edsair.doi...........58bee107db4ef3a2b53bd96bbbe4274a
- Full Text :
- https://doi.org/10.1109/twc.2019.2952110