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Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices.

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Summary
This summary is machine-generated.

This study shows Received Signal Strength Indicator (RSSI) distribution can identify different wireless technologies like Wi-Fi and LTE, even at lower sampling rates. This enables efficient spectrum sharing on devices with limited resources.

Keywords:
NGWNRSSIconstrained devicesexperimental studymulti-modal distributiontechnology recognition

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Area of Science:

  • Wireless Communication
  • Signal Processing
  • Spectrum Management

Background:

  • The increasing demand for wireless communication necessitates efficient spectrum sharing among diverse technologies.
  • Current systems struggle to identify concurrent signals due to complex algorithms and high sampling rate requirements.
  • Recognizing signals from different technologies is crucial for effective dynamic spectrum access.

Purpose of the Study:

  • To demonstrate that Received Signal Strength Indicator (RSSI) distribution patterns can differentiate various wireless technologies.
  • To propose a method for technology identification using RSSI features, suitable for resource-constrained devices.
  • To validate the effectiveness of RSSI analysis for concurrent technology recognition in dynamic spectrum access.

Main Methods:

  • Analyzing the multi-model distribution of RSSI, correlating it with modulation schemes and medium access.
  • Deriving features like packet duration from RSSI or directly using RSSI's probability distribution.
  • Conducting experimental studies using sub-Nyquist sampling rates to capture RSSI data from Wi-Fi, LTE, DVB-T, and Bluetooth signals.

Main Results:

  • RSSI distribution exhibits distinctive features that allow differentiation between wireless technologies.
  • Even at sub-Nyquist sampling rates, RSSI provides sufficient features to distinguish between Wi-Fi, LTE, DVB-T, and Bluetooth.
  • An experimental evaluation achieved over 92% accuracy in technology recognition using RSSI distribution-based features.

Conclusions:

  • RSSI distribution analysis is a straightforward and resource-efficient method for identifying concurrent wireless technologies.
  • This approach is highly valuable for enabling dynamic spectrum access on devices with constrained hardware.
  • The findings support the development of simpler, more effective signal recognition systems for future wireless networks.