Balancing Energy Efficiency and Communication Quality in UAV Cargo Delivery Systems

In this paper, we investigate the trade-off issue between energy efficiency and communication quality in the unmanned aerial vehicle (UAV) enabled cargo delivery system. For a cellular-connected cargo UAV delivering parcels from the warehouse to each user’s location, minimizing both the energy consumption and expected outage time is essential. However, a trade-off exists between these two factors, optimizing one aspect is bound to diminished performance in the other. To jointly reduce the UAV’s energy consumption and expected outage time, we formulate an optimization problem with the objective function to minimize the weighted sum of UAV’s energy consumption and expected outage time. With the aid of radio map, a hybrid deep reinforcement learning (HDRL) algorithm, consisting of an improved ant colony optimization algorithm and the dueling double deep Q network algorithm, is proposed to solve the formulated problem. The delivery sequence and the flight trajectory of the UAV are then jointly optimized by solving the problem with the HDRL algorithm. Numerical results demonstrate that the proposed algorithm effectively reduces both energy consumption and outage time, while achieving a performance improvement of approximately 6% to 50% compared to the comparisons. Moreover, the communication quality of the UAV improves with an increased weight factor, yet gives rise to a higher energy consumption.