Optimal Power Allocation for Full-Duplex Underwater Relay Networks with Energy Harvesting: A Reinforcement Learning Approach

Ranning Wang, Animesh Yadav, Esraa A. Makled, Octavia A. Dobre, Ruiqin Zhao, Pramod K. Varshney

Research output: Contribution to journalArticle

Abstract

In this letter, we study the optimal power allocation problem where the goal is to maximize the long-term end-to-end sum rate of an underwater full-duplex energy harvesting relay network. The problem is formulated as an online sequential decision-making problem where the relay adapts the transmit power in each time-slot based on past and current information of harvested energy, battery level, channel state information, and interference level. The optimal transmission policy is obtained through the reinforcement learning framework. Simulation results show that the optimal online power allocation policy achieves a higher sum rate than the computationally-efficient sub-optimal online greedy power allocation policy, especially under insufficient harvested energy.

Original languageEnglish (US)
Article number8882231
Pages (from-to)223-227
Number of pages5
JournalIEEE Wireless Communications Letters
Volume9
Issue number2
DOIs
StatePublished - Feb 2020

Keywords

  • energy harvesting
  • full-duplex relay networks
  • reinforcement learning
  • Underwater acoustic communication

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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