Mobile edge computing (MEC) is viewed as a promising technology to address the challenges of intensive computing demands in hotspots (HSs). In this article, we consider a unmanned aerial vehicle (UAV)-assisted backscattering MEC system. The UAVs can fly from parking aprons to HSs, providing energy to HSs via RF beamforming and collecting data from wireless users in HSs through backscattering. We aim to maximize the long-term utility of all HSs, subject to the stability of the HSs’ energy queues. This problem is a joint optimization of the data offloading decision and contract design that should be adaptive to the users’ random task demands and the time-varying wireless channel conditions. A deep reinforcement learning based contract incentive (DRLCI) strategy is proposed to solve this problem in two steps. First, we use deep Q-network (DQN) algorithm to update the HSs’ offloading decisions according to the changing network environment. Second, to motivate the UAVs to participate in resource sharing, a contract specific to each type of UAVs has been designed, utilizing Lagrangian multiplier method to approach the optimal contract. Simulation results show the feasibility and efficiency of the proposed strategy, demonstrating a better performance than the natural DQN and Double-DQN algorithms.

中文翻译：

---

针对无线供电和无人机辅助反向散射 MEC 系统的基于 DRL 的合同激励

移动边缘计算 (MEC) 被视为一项有前途的技术，可以解决热点 (HS) 中密集计算需求的挑战。在本文中，我们考虑一种无人机 (UAV) 辅助的后向散射 MEC 系统。无人机可以从停机坪飞往HS，通过射频波束成形为HS提供能量，并通过反向散射从HS中的无线用户收集数据。我们的目标是在 HS 能源队列稳定性的前提下，最大化所有 HS 的长期效用。该问题是数据卸载决策和合约设计的联合优化，应适应用户的随机任务需求和时变的无线信道条件。提出了一种基于深度强化学习的契约激励（DRLCI）策略来分两步解决这个问题。首先，我们使用深度Q网络（DQN）算法根据不断变化的网络环境更新HS的卸载决策。其次，为了激励无人机参与资源共享，设计了针对每种类型无人机的合约，利用拉格朗日乘子法逼近最优合约。仿真结果表明了该策略的可行性和效率，比自然DQN和Double-DQN算法具有更好的性能。