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Mobility-Aware Resource Allocation in IoRT Network for Post-Disaster Communications with Parameterized Reinforcement

Homayun Kabir1, Mau-Luen Tham1, Yoong Choon Chang1

  • 1Department of Electrical and Electronic Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Sungai Long Campus, Kajang 43000, Malaysia.

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Summary
This summary is machine-generated.

This study introduces a new algorithm, the multi-pass deep Q network (MP-DQN), to optimize energy efficiency and throughput for user equipment in post-disaster networks. The MP-DQN algorithm enhances communication services for robotic things during natural disasters.

Keywords:
deep reinforcement learning (DRL)internet of robotic things (IoRT)movable and deployable resource units (MDRU)multi-pass deep Q network (MP-DQN)parameterized action spacepost disaster communication

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Area of Science:

  • Network Engineering
  • Artificial Intelligence
  • Disaster Management

Background:

  • Natural disasters are increasing due to climate change, disrupting communication infrastructure.
  • Post-disaster management relies on user equipment (UE) via the Internet of Robotic Things (IoRT) for critical operations.
  • Maintaining Quality of Service (QoS) and optimizing power consumption in damaged networks is a significant challenge.

Purpose of the Study:

  • To optimize energy efficiency, UE throughput, and serving cell (SC) throughput in IoRT-enabled networks for post-disaster scenarios.
  • To address the complexities of nonconvex and NP-hard optimization problems involving power allocation and user association.
  • To develop a novel algorithm for dynamic network optimization without prior environmental knowledge.

Main Methods:

  • Formulation of an optimization problem combining emitting power allocation and user association.
  • Development of a model-free, hybrid action space algorithm: multi-pass deep Q network (MP-DQN).
  • Consideration of both stationary and movable user equipment within a two-tier heterogeneous network (HetsNet) framework.

Main Results:

  • The proposed MP-DQN algorithm significantly outperforms existing methods like P-DQN and traditional algorithms.
  • Demonstrated improvements in reward, average energy efficiency, UE throughput, and SC throughput.
  • Effective optimization for both motionless and moveable user equipment in challenging post-disaster conditions.

Conclusions:

  • MP-DQN offers a superior solution for optimizing complex network parameters in disaster-stricken environments.
  • The algorithm provides enhanced communication reliability and efficiency for IoRT applications during emergencies.
  • This research contributes to more resilient and effective post-disaster management strategies through advanced network optimization.