Path Planning for Cellular-Connected UAV: A DRL Solution With Quantum-Inspired Experience Replay

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, a UAV navigation approach is formulated to achieve the aforementioned optimization goal. Conventional offline optimization techniques suffer from inefficiency in accomplishing the formulated UAV navigation task due to the practical consideration of local building distribution and directional antenna radiation pattern. Alternatively, after mapping the navigation task into a Markov decision process (MDP), a deep reinforcement learning (DRL)-aided solution 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. To help the DRL agent commit a better trade-off between sampling priority and diversity, a novel quantum-inspired experience replay (QiER) framework is proposed, via relating experienced transition's importance to its associated quantum bit (qubit) and applying Grover iteration based amplitude amplification technique. Compared to several representative DRL-related and non-learning baselines, the effectiveness and supremacy of the proposed DRL-QiER solution are demonstrated and validated in numerical results.

Automated summary

This paper considers a minimization problem in a cellular-connected unmanned aerial vehicle (UAV) network. The problem involves both the time cost and expected outage duration. A UAV navigation approach is proposed to optimize these factors by utilizing the UAV's adjustable mobility. The effectiveness of the proposed approach is demonstrated through numerical results.