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This study introduces a cognitive Low Earth Orbit (LEO) satellite system enabling Internet-of-Things (IoT) devices to share spectrum with legacy users. Resource allocation optimizes IoT performance while ensuring legacy system quality.

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Area of Science:

  • Satellite Communications
  • Wireless Networking
  • Internet-of-Things

Background:

  • Low Earth Orbit (LEO) satellite communication (SatCom) offers global coverage for Internet-of-Things (IoT).
  • Spectrum scarcity and high satellite costs hinder dedicated IoT satellite deployment.
  • Cognitive radio principles can enable opportunistic spectrum access for IoT.

Purpose of the Study:

  • To propose and analyze a cognitive LEO satellite system for facilitating IoT communications.
  • To investigate achievable rate analysis and resource allocation for cognitive satellite IoT.
  • To maximize the sum rate of IoT transmissions under performance constraints.

Main Methods:

  • Utilizing Code Division Multiple Access (CDMA) for cognitive satellite IoT.
  • Applying Random Matrix Theory (RMT) to analyze asymptotic Signal-to-Interference-plus-Noise Ratios (SINRs).
  • Jointly allocating power to maximize IoT sum rate, subject to legacy system requirements and power constraints.

Main Results:

  • Asymptotic SINRs and achievable rates derived for both legacy and IoT systems using RMT.
  • Demonstrated quasi-concavity of the IoT sum rate with respect to receive power.
  • Optimal receive power allocation derived for maximizing IoT performance.

Conclusions:

  • The proposed cognitive LEO satellite system effectively supports IoT communications by enabling cognitive spectrum sharing.
  • The developed resource allocation strategy optimizes IoT performance while respecting legacy system constraints.
  • Simulations validate the effectiveness of the proposed resource allocation scheme.