4.8 Article

Optimal Deployment of Phased Array Antennas for RFID Network Planning Based on an Improved Chicken Swarm Optimization

期刊

IEEE INTERNET OF THINGS JOURNAL
卷 8, 期 19, 页码 14572-14588

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2021.3067013

关键词

Phased arrays; Dipole antennas; Radiofrequency identification; Optimization; Antennas; Directive antennas; Planning; Chicken swarm optimization (CSO); network planning; phased array antenna; radio-frequency identification (RFID)

资金

  1. Natural Science Foundation of Tianjin [19JCQNJC03300, 18JCQNJC70800]
  2. China Postdoctoral Science Foundation [2020M680883]

向作者/读者索取更多资源

Effective network planning plays a crucial role in improving the performance of the RFID system. This article proposes an optimal deployment strategy for phased array reader antennas in RFID network planning, which adjusts antenna gains and radiation directions through voltage states. The introduced AFN indicator reflects frequency selective fading disturbance caused by multipath effects, while the improved chicken swarm optimization algorithm effectively tackles the RNP problem and outperforms existing approaches in simulation and experiments.
Effective network planning improves performance in the radio-frequency identification (RFID) system. This article proposes an optimal deployment of phased array reader antennas for RFID network planning (RNP). For practical considerations, the RNP problem is analyzed and formulated based on a multipath propagation model, where each reader is equipped with a phased array antenna. The gains and radiation directions of the antennas are adjusted by voltage states instead of gestures, reducing the cumbersome antennas redeployment. An indicator called amplitude fluctuation under narrowband (AFN) is proposed to reflect the disturbance of frequency selective fading caused by the multipath effect. To effectively address the RNP problem, an improved chicken swarm optimization algorithm with two targeted strategies is developed. Simulation and experiment comparisons with the existing algorithms demonstrate the superiority of the proposed approach.

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