Recently, Unmanned Aerial Vehicles (UAVs) have attracted the attention of
researchers in academia and industry for providing wireless services to ground
users in diverse scenarios like festivals, large sporting events, natural and
man-made disasters due to their advantages in terms of versatility and
maneuverability. However, the limited resources of UAVs (e.g., energy budget
and different service requirements) can pose challenges for adopting UAVs for
such applications. Our system model considers a UAV swarm that navigates an
area, providing wireless communication to ground users with RIS support to
improve the coverage of the UAVs. In this work, we introduce an optimization
model with the aim of maximizing the throughput and UAVs coverage through
optimal path planning of UAVs and multi-RIS phase configurations. The
formulated optimization is challenging to solve using standard linear
programming techniques, limiting its applicability in real-time
decision-making. Therefore, we introduce a two-step solution using deep
reinforcement learning and particle swarm optimization. We conduct extensive
simulations and compare our approach to two competitive solutions presented in
the recent literature. Our simulation results demonstrate that our adopted
approach is 20 \% better than the brute-force approach and 30\% better than the
baseline solution in terms of QoS.

无人机在提供无线服务方面具有优势，但资源限制给应用带来挑战。本论文介绍了一种优化模型，通过无人机路径规划和多重反射面支持，最大化吞吐量和覆盖范围，通过深度强化学习和粒子群算法解决了线性规划技术的限制，并通过大量仿真实验证明在服务质量方面相比其他方法有较大的提升。

多无人机多 RIS 关注 QoS 的空中通信系统使用 DRL 和 PSO

Multi-UAV Multi-RIS QoS-Aware Aerial Communication Systems using DRL and  PSO

In this work, we optimize the 3D trajectory of an unmanned aerial vehicle
(UAV)-based portable access point (PAP) that provides wireless services to a
set of ground nodes (GNs). Moreover, as per the Peukert effect, we consider
pragmatic non-linear battery discharge for the battery of the UAV. Thus, we
formulate the problem in a novel manner that represents the maximization of a
fairness-based energy efficiency metric and is named fair energy efficiency
(FEE). The FEE metric defines a system that lays importance on both the
per-user service fairness and the energy efficiency of the PAP. The formulated
problem takes the form of a non-convex problem with non-tractable constraints.
To obtain a solution, we represent the problem as a Markov Decision Process
(MDP) with continuous state and action spaces. Considering the complexity of
the solution space, we use the twin delayed deep deterministic policy gradient
(TD3) actor-critic deep reinforcement learning (DRL) framework to learn a
policy that maximizes the FEE of the system. We perform two types of RL
training to exhibit the effectiveness of our approach: the first (offline)
approach keeps the positions of the GNs the same throughout the training phase;
the second approach generalizes the learned policy to any arrangement of GNs by
changing the positions of GNs after each training episode. Numerical
evaluations show that neglecting the Peukert effect overestimates the air-time
of the PAP and can be addressed by optimally selecting the PAP's flying speed.
Moreover, the user fairness, energy efficiency, and hence the FEE value of the
system can be improved by efficiently moving the PAP above the GNs. As such, we
notice massive FEE improvements over baseline scenarios of up to 88.31%,
272.34%, and 318.13% for suburban, urban, and dense urban environments,
respectively.

通过采用深度强化学习控制策略以最大化公平能效（FEE）指标来优化无人机提供给一组地面节点的无线服务，从而显着提高了城市、郊区和密集市区实验中的用户公平性、能量效率和整个系统的公平能效值。

基于公平性的便携式接入点能效 3D 路径规划：一种深度强化学习方法

Fairness Based Energy-Efficient 3D Path Planning of a Portable Access Point: A Deep Reinforcement Learning Approach

Unmanned Aerial Vehicle mounted base stations (UAV-BSs) can provide wireless
services in a variety of scenarios. In this letter, we propose an optimal
placement algorithm for UAV-BSs that maximizes the number of covered users
using the minimum transmit power. We decouple the UAV-BS deployment problem in
the vertical and horizontal dimensions without any loss of optimality.
Furthermore, we model the UAV-BS deployment in the horizontal dimension as a
circle placement problem and a smallest enclosing circle problem. Simulations
are conducted to evaluate the performance of the proposed method for different
spatial distributions of the users.

本文提出了一种基于优化算法的无人机基站布局方案，通过在垂直和水平空间上分别部署无人机基站在尽可能减少发送功率的情况下最大化覆盖用户数量，同时通过模拟验证了该方案的性能。