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Actor-critic learning-based energy optimization for UAV access and backhaul networks.

Yaxiong Yuan1, Lei Lei1, Thang X Vu1

  • 1Interdisciplinary Center for Security, Reliability and Trust, University of Luxembourg, 1855 Kirchberg, Luxembourg, Luxembourg.

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Summary
This summary is machine-generated.

This study minimizes energy consumption in drone-assisted networks by developing novel deep reinforcement learning algorithms. The proposed methods significantly reduce computation time and improve energy efficiency for wireless communication systems.

Keywords:
Actor-criticBackhaul power allocationDeep reinforcement learningEnergy optimizationUAVUser scheduling

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Area of Science:

  • * Wireless Communication Networks
  • * Artificial Intelligence in Telecommunications
  • * Optimization and Control Theory

Background:

  • * Unmanned Aerial Vehicle (UAV) networks utilize aerial base stations for data acquisition and ground user service.
  • * Energy minimization is critical for UAV-assisted networks due to limited power for backhaul and access links.
  • * Solving non-convex, combinatorial energy minimization problems presents significant computational challenges.

Purpose of the Study:

  • * To investigate and solve the energy minimization problem in UAV-assisted networks with power constraints.
  • * To develop efficient algorithms for real-time decision-making in UAV communication systems.
  • * To benchmark novel learning-based approaches against traditional optimization methods.

Main Methods:

  • * Development of offline optimal and heuristic algorithms using piecewise linear approximation and relaxation.
  • * Proposal of two Actor-Critic Deep Reinforcement Learning (AC-DRL) schemes: AC-based user group scheduling and backhaul power allocation (ACGP), and joint AC-based user group scheduling and optimization-based backhaul power allocation (ACGOP).
  • * Comparison of proposed AC-DRL methods with conventional AC-DRL and offline algorithms.

Main Results:

  • * ACGP and ACGOP demonstrated a tenfold to hundredfold reduction in computation time compared to offline methods.
  • * ACGOP achieved superior energy savings compared to ACGP.
  • * The proposed learning solutions effectively guaranteed feasibility and minimized system energy more than conventional AC-DRL.

Conclusions:

  • * Novel AC-DRL based algorithms (ACGP and ACGOP) offer significant improvements in computational efficiency for UAV-assisted networks.
  • * The ACGOP approach provides the best performance in terms of energy savings.
  • * These learning-based solutions represent a superior alternative for real-time energy minimization in dynamic UAV communication environments.