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Deep RL-assisted Energy Harvesting in CR-NOMA Communications for NextG IoT Networks
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  • Syed Asad Ullah ,
  • Shah Zeb ,
  • Syed Ali Hassan ,
  • Aamir Mahmood ,
  • Mikael Gidlund
Syed Asad Ullah
National University of Sciences and Technology (NUST)

Corresponding Author:[email protected]

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Syed Ali Hassan
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Aamir Mahmood
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Mikael Gidlund
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Abstract

Zero-energy radios in energy-constrained devices are envisioned as key enablers to realizing the next-generation Internet-of-things (NG-IoT) networks for ultra-dense sensing and monitoring. This paper presents analytical modeling and analysis of the energy-efficient uplink transmission of an energyconstrained secondary sensor operating opportunistically among several primary sensors. The considered scenario assumes that all primary sensors transmit in a round-robin, time division multiple access-based schemes, and the secondary sensor is admitted in the time slot of each primary sensor using a nonorthogonal multiple access technique, inspired by cognitive radio. The energy efficiency of the secondary sensor is maximized by exposing it to a deep reinforcement learning-based algorithm, recognized as a deep deterministic policy gradient (DDPG). Our results demonstrate that the DDPG-based transmission scheme outperforms the conventional random and greedy algorithms in terms of energy efficiency at different operating conditions.