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

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Document pages: 6 pages

Abstract: In this paper, we consider a wireless uplink transmission scenario in whichan unmanned aerial vehicle (UAV) serves as an aerial base station collectingdata from ground users. To optimize the expected sum uplink transmit ratewithout any prior knowledge of ground users (e.g., locations, channel stateinformation and transmit power), the trajectory planning problem is optimizedvia the quantum-inspired reinforcement learning (QiRL) approach. Specifically,the QiRL method adopts novel probabilistic action selection policy and newreinforcement strategy, which are inspired by the collapse phenomenon andamplitude amplification in quantum computation theory, respectively. Numericalresults demonstrate that the proposed QiRL solution can offer natural balancingbetween exploration and exploitation via ranking collapse probabilities ofpossible actions, compared to the traditional reinforcement learning approacheswhich are highly dependent on tuned exploration parameters.

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