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Indoor Large-Scale MIMO-Based RSSI Localization with Low-Complexity RFID Infrastructure.

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  • 1Institute of Digital Signal Processing, University of Duisburg-Essen, 47057 Duisburg, Germany.

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Summary
This summary is machine-generated.

This study introduces an improved indoor localization method using passive Radio Frequency Identification (RFID) and large-scale MIMO. The new algorithm enhances accuracy and robustness in challenging indoor environments.

Keywords:
RFIDRSSIlarge-scale MIMOlocalizationlocalization accuracypassive RFID

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Passive Radio Frequency Identification (RFID) systems offer low-complexity, energy-harvesting solutions for indoor localization.
  • Conventional Received Signal Strength Indicator (RSSI)-based localization suffers from multipath fading, leading to inaccuracies.
  • Millimeter-wave (mmWave) band communication with large-scale Multiple-Input Multiple-Output (MIMO) can mitigate small-scale fading effects.

Purpose of the Study:

  • To develop an accurate and robust indoor localization algorithm for passive RFID systems.
  • To leverage large-scale MIMO technology in the mmWave band to overcome multipath effects.
  • To enhance the performance of Internet of Things (IoT) applications requiring precise indoor positioning.

Main Methods:

  • Proposed an RSSI-based localization algorithm utilizing passive dielectric resonator (DR) tags and large-scale MIMO.
  • Employed mmWave band technology for its channel hardening properties to reduce fading.
  • Derived the Cramer-Rao lower bound to theoretically assess localization accuracy.
  • Evaluated algorithm performance across various network topologies and compared it with existing methods.

Main Results:

  • The proposed algorithm significantly improves localization accuracy compared to conventional methods.
  • Demonstrated enhanced robustness against channel modeling inaccuracies.
  • Validated effectiveness through numerical simulations across diverse network configurations.
  • Achieved superior performance metrics in terms of precision and reliability.

Conclusions:

  • The developed RSSI-based localization technique effectively utilizes passive RFID and large-scale MIMO for precise indoor positioning.
  • The integration of mmWave technology and DR tags provides a robust solution for challenging indoor environments.
  • This advancement holds significant potential for various IoT applications demanding accurate localization.