论文标题
一种基于参与者的无人机BBSS部署方法，用于动态环境
An Actor-Critic-Based UAV-BSs Deployment Method for Dynamic Environments
论文作者
论文摘要
在本文中，研究了无人机网络的实时部署无人驾驶汽车（UAV）作为飞行基站（BSS），以优化移动用户的吞吐量。该问题被提出为随着时间变化的混合企业非凸编程（MINP）问题，这在短时间内通过常规优化技术在短时间内找到最佳解决方案具有挑战性。因此，我们提出了一种基于参与者的（基于AC的）深度强化学习（DRL）方法，以每时每刻找到近乎最佳的无人机位置。在提出的方法中，在特定时刻进行迭代迭代的过程的过程被建模为马尔可夫决策过程（MDP）。为了处理无限状态和行动空间并改善决策过程的鲁棒性，配置了两个强大的神经网络（NNS），以评估无人机位置调整并做出决策。与启发式算法，顺序最小二乘编程和固定无人机方法相比，模拟结果表明，所提出的方法在无人机网络中的每一刻都优于这三个基准。
In this paper, the real-time deployment of unmanned aerial vehicles (UAVs) as flying base stations (BSs) for optimizing the throughput of mobile users is investigated for UAV networks. This problem is formulated as a time-varying mixed-integer non-convex programming (MINP) problem, which is challenging to find an optimal solution in a short time with conventional optimization techniques. Hence, we propose an actor-critic-based (AC-based) deep reinforcement learning (DRL) method to find near-optimal UAV positions at every moment. In the proposed method, the process searching for the solution iteratively at a particular moment is modeled as a Markov decision process (MDP). To handle infinite state and action spaces and improve the robustness of the decision process, two powerful neural networks (NNs) are configured to evaluate the UAV position adjustments and make decisions, respectively. Compared with the heuristic algorithm, sequential least-squares programming and fixed UAVs methods, simulation results have shown that the proposed method outperforms these three benchmarks in terms of the throughput at every moment in UAV networks.
