Intelligent Trajectory Planning in UAV-mounted Wireless Networks: A Quantum-Inspired Reinforcement Learning Perspective

TL;DR

This paper introduces a quantum-inspired reinforcement learning approach for UAV trajectory planning in wireless networks, enabling efficient uplink rate optimization without prior knowledge of ground user parameters.

Contribution

It proposes a novel QiRL method with quantum-inspired probabilistic policies and strategies, improving exploration-exploitation balance in UAV trajectory optimization.

Findings

QiRL outperforms traditional RL in uplink rate maximization

The approach balances exploration and exploitation effectively

Numerical results validate the method's efficiency

Abstract

In this paper, we consider a wireless uplink transmission scenario in which an unmanned aerial vehicle (UAV) serves as an aerial base station collecting data from ground users. To optimize the expected sum uplink transmit rate without any prior knowledge of ground users (e.g., locations, channel state information and transmit power), the trajectory planning problem is optimized via the quantum-inspired reinforcement learning (QiRL) approach. Specifically, the QiRL method adopts novel probabilistic action selection policy and new reinforcement strategy, which are inspired by the collapse phenomenon and amplitude amplification in quantum computation theory, respectively. Numerical results demonstrate that the proposed QiRL solution can offer natural balancing between exploration and exploitation via ranking collapse probabilities of possible actions, compared to the traditional reinforcement learning approaches which are highly dependent on tuned exploration parameters.