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Related Experiment Video

Updated: Jul 23, 2025

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

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Published on: September 8, 2023

Reinforcement-Learning-Based Robust Resource Management for Multi-Radio Systems.

James Delaney¹, Steve Dowey¹, Chi-Tsun Cheng¹

¹Manufacturing, Materials and Mechatronics, School of Engineering, STEM College, RMIT University, 124 La Trobe St., Melbourne, VIC 3000, Australia.

Sensors (Basel, Switzerland)

|July 11, 2023

Summary

Intelligent radio selection using multi-objective reinforcement learning enhances wireless communication robustness for the Internet of Things (IoT). Adaptive exploration strategies improve performance by 20% compared to traditional methods.

Keywords:

adaptive systems multi-radio reinforcement learning resource management

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Area of Science:

Wireless communication systems
Internet of Things (IoT)
Adaptive radio technologies

Background:

Increased demand for sensing devices with multiple wireless transceivers in IoT.
Need for adaptive systems to ensure robust communication under dynamic channel conditions.
Focus on wireless links between deployed personnel devices and access-point infrastructure.

Purpose of the Study:

To apply a multi-objective reinforcement learning (MORL) framework to multi-radio selection and power control.
To manage trade-offs between power consumption and bit rate using independent reward functions.
To develop and evaluate an adaptive exploration strategy for robust policy learning.

Main Methods:

Utilized multi-radio platforms with diverse transceiver technologies.
Implemented a MORL framework with independent reward functions for conflicting objectives.
Proposed an extension to the multi-objective SARSA algorithm incorporating an adaptive exploration strategy.

Main Results:

Achieved robust and reliable links through adaptive control of transceivers.
Demonstrated effective management of power consumption and bit rate trade-offs.
Showcased a 20% increase in F1 score with adaptive exploration compared to decayed exploration policies.

Conclusions:

MORL framework effectively addresses multi-radio selection and power control challenges.
Adaptive exploration strategy enhances the robustness and performance of wireless communication systems.
The proposed extended multi-objective SARSA algorithm offers significant improvements in communication reliability.