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Energy-efficient framework based on optimal antenna selection in S-NOMA supported UAV IoT networks.

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Summary

This study introduces an energy-efficient framework for Unmanned Aerial Vehicle (UAV) assisted Internet-of-Things (IoT) networks using spatial Non-Orthogonal Multiple Access (S-NOMA). The proposed system significantly enhances data rates and energy efficiency for sustainable IoT connectivity.

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Area of Science:

  • Wireless Communication Networks
  • Internet of Things (IoT)
  • Sustainable Energy Systems

Background:

  • The expanding Internet-of-Things (IoT) necessitates energy-efficient solutions due to high emissions and energy consumption.
  • Unmanned Aerial Vehicle (UAV) assisted networks offer a promising approach to enhance IoT connectivity.
  • Achieving energy sustainability in heterogeneous terrestrial and non-terrestrial IoT networks is a critical challenge.

Purpose of the Study:

  • To present an energy-efficient framework for UAV-assisted IoT networks utilizing spatial Non-Orthogonal Multiple Access (S-NOMA).
  • To develop an antenna selection algorithm for user fairness and optimize power consumption models.
  • To analyze and improve the energy efficiency and data rate performance of S-NOMA in UAV-IoT systems.

Main Methods:

  • Development of a spatial Non-Orthogonal Multiple Access (S-NOMA) framework for UAV-assisted IoT.
  • Proposal of an antenna selection algorithm to ensure user fairness.
  • Formulation of air-to-ground communication links and a power consumption model (including transmit, circuit, and UAV hovering power).

Main Results:

  • The proposed S-NOMA framework with optimal antenna selection demonstrated superior data rate and energy efficiency compared to conventional NOMA and random schemes.
  • At 30 dB SNR, the proposed method achieved a data rate of 15.2 bps/Hz, significantly outperforming conventional NOMA (6.4 bps/Hz).
  • Energy efficiency improved by 14.4% at 25 dBm transmit power with the proposed antenna selection over random selection, attributed to enhanced spatial gain and power-aware selection.

Conclusions:

  • The proposed S-NOMA framework with optimal antenna selection offers a sustainable solution for UAV-assisted IoT networks.
  • Enhanced spatial gain and power-aware antenna selection are key factors in improving energy efficiency and data rates.
  • The framework effectively addresses the challenge of energy sustainability in expanding heterogeneous IoT networks.