  In cellular-connected unmanned aerial vehicle (UAV) network, a minimization
problem on the weighted sum of time cost and expected outage duration is
considered. Taking advantage of UAV's adjustable mobility, an intelligent UAV
navigation approach is formulated to achieve the aforementioned optimization
goal. Specifically, after mapping the navigation task into a Markov decision
process (MDP), a deep reinforcement learning (DRL) solution with novel
quantum-inspired experience replay (QiER) framework is proposed to help the UAV
find the optimal flying direction within each time slot, and thus the designed
trajectory towards the destination can be generated. Via relating experienced
transition's importance to its associated quantum bit (qubit) and applying
Grover iteration based amplitude amplification technique, the proposed DRL-QiER
solution commits a better trade-off between sampling priority and diversity.
Compared to several representative baselines, the effectiveness and supremacy
of the proposed DRL-QiER solution are demonstrated and validated in numerical
results.
