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Hardware Impaired Self-Energized Bidirectional Sensor Networks over Complex Fading Channels.

Stefan R Panic1,2, Dushantha Nalin K Jayakody1,3, Sofiene Affes4

  • 1School of Computer Science and Robotics, National Research Tomsk Polytechnic University, 634050 Tomsk, Russia.

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|October 2, 2020
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Summary
This summary is machine-generated.

This study introduces time-switching energy harvesting for wireless sensor networks (WSN) to extend battery life. It analyzes system performance, including outage probability and throughput, over various fading channels.

Keywords:
Hoyt fading channelsRician-shadowed fading channelshalf-duplexhardware impairmentrelay networkstime-switchingwireless energy harvesting

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Area of Science:

  • Wireless Sensor Networks (WSN)
  • Energy Harvesting (EH)
  • Internet of Things (IoT)

Background:

  • Limited battery life in wireless sensor nodes is a major challenge for WSNs.
  • Efficient energy management is crucial for unlocking the full potential of WSNs in the IoT paradigm.
  • Energy harvesting techniques are vital for prolonging sensor node operational time.

Purpose of the Study:

  • To analyze the performance of a time-switching energy harvesting system for half-duplex bidirectional WSNs.
  • To investigate the impact of hardware impairments and fading channels on system efficiency.
  • To develop analytical expressions for key performance metrics.

Main Methods:

  • Developed rapidly converging infinite-series expressions for performance analysis.
  • Focused on outage probability (OP) and achievable throughput.
  • Analyzed symbol-error probability (SEP) and considered shadowing effects.

Main Results:

  • Provided comprehensive performance analysis for hardware-impaired systems over Hoyt fading channels.
  • Derived exact infinite-series expressions for OP and throughput over Rician-shadowed fading channels.
  • Investigated asymptotic behavior of performance measures.

Conclusions:

  • The proposed time-switching EH scheme offers a viable solution for prolonging WSN battery life.
  • The analytical framework provides accurate performance predictions for WSNs in diverse fading environments.
  • The study contributes to the efficient design of energy-aware WSNs for IoT applications.