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Related Experiment Video

Updated: Oct 16, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

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Reinforcement Learning-Based Multihop Relaying: A Decentralized Q-Learning Approach.

Xiaowei Wang1, Xin Wang1

  • 1College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.

Entropy (Basel, Switzerland)
|October 23, 2021
PubMed
Summary

This study introduces a reinforcement learning approach for relay selection in multihop networks, balancing performance and cost. The new decentralized Q-learning scheme significantly reduces complexity and overhead while maintaining high data rates.

Keywords:
Q-learningmultihop networkreinforcement learningrelay selection

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Traditional relay selection in multihop networks faces a performance-cost trade-off.
  • Centralized optimal policies incur high computational complexity and signaling overhead.
  • Decentralized policies often result in significant performance degradation.

Purpose of the Study:

  • To develop a cost-effective relay selection strategy for multihop clustered networks.
  • To leverage reinforcement learning for improved relay selection performance.
  • To reduce computational complexity and signaling overhead in relay selection.

Main Methods:

  • Modeling the multihop relay selection as a Markov decision process (MDP).
  • Implementing a decentralized Q-learning scheme with a rectified update function.
  • Analyzing computational complexity and signaling overhead.

Main Results:

  • The proposed Q-learning scheme achieves near-optimal average end-to-end (E2E) data rates.
  • Demonstrated reduction in computation complexity compared to optimal methods.
  • Significant decrease in signaling overhead was observed.

Conclusions:

  • Reinforcement learning offers an effective solution for relay selection in multihop networks.
  • The decentralized Q-learning approach successfully balances performance and cost.
  • This method provides a practical alternative to conventional relay selection techniques.