Unmanned aerial vehicles (UAVs) are expected to be an integral part of
wireless networks. In this paper, we aim to find collision-free paths for
multiple cellular-connected UAVs, while satisfying requirements of connectivity
with ground base stations (GBSs) in the presence of a dynamic jammer. We first
formulate the problem as a sequential decision making problem in discrete
domain, with connectivity, collision avoidance, and kinematic constraints. We,
then, propose an offline temporal difference (TD) learning algorithm with
online signal-to-interference-plus-noise ratio (SINR) mapping to solve the
problem. More specifically, a value network is constructed and trained offline
by TD method to encode the interactions among the UAVs and between the UAVs and
the environment; and an online SINR mapping deep neural network (DNN) is
designed and trained by supervised learning, to encode the influence and
changes due to the jammer. Numerical results show that, without any information
on the jammer, the proposed algorithm can achieve performance levels close to
that of the ideal scenario with the perfect SINR-map. Real-time navigation for
multi-UAVs can be efficiently performed with high success rates, and collisions
are avoided.

本研究旨在针对移动干扰器存在的情况下，寻找多个航空无人机与地面基站的连通性且避开碰撞的路径。我们提出了一种利用离线时间差异学习算法和在线信噪比映射解决该问题的方案。结果表明，在没有干扰器信息的情况下，我们提出的算法可以高效地避免碰撞并实现实时的多无人机导航任务。

多个无人机的鲁棒路径规划：基于深度强化学习

Jamming-Resilient Path Planning for Multiple UAVs via Deep Reinforcement  Learning

Integrating the unmanned aerial vehicles (UAVs) into the cellular network is
envisioned to be a promising technology to significantly enhance the
communication performance of both UAVs and existing terrestrial users. In this
paper, we first provide an overview on the two main paradigms in cellular UAV
communications, i.e., cellular-enabled UAV communication with UAVs as new
aerial users served by the ground base stations (GBSs), and UAV-assisted
cellular communication with UAVs as new aerial communication platforms serving
the terrestrial users. Then, we focus on the former paradigm and study a new
UAV trajectory design problem subject to practical communication connectivity
constraints with the GBSs. Specifically, we consider a cellular-connected UAV
in the mission of flying from an initial location to a final location, during
which it needs to maintain reliable communication with the cellular network by
associating with one GBS at each time instant. We aim to minimize the UAV's
mission completion time by optimizing its trajectory, subject to a
quality-of-connectivity constraint of the GBS-UAV link specified by a minimum
receive signal-to-noise ratio target. To tackle this challenging non-convex
problem, we first propose a graph connectivity based method to verify its
feasibility. Next, by examining the GBS-UAV association sequence over time, we
obtain useful structural results on the optimal UAV trajectory, based on which
two efficient methods are proposed to find high-quality approximate trajectory
solutions by leveraging graph theory and convex optimization techniques. The
proposed methods are analytically shown to be capable of achieving a flexible
trade-off between complexity and performance, and yielding a solution that is
arbitrarily close to the optimal solution in polynomial time. Finally, we make
concluding remarks and point out some promising directions for future work.

这篇论文提出了一种新的无人机轨迹设计问题，考虑优化其航迹以最小化任务完成时间，并在保证最低接收信噪比的前提下与地面基站进行可靠通信，通过图论和凸优化技术，提出了两种高质量的近似轨迹解决方案。