We describe the orchestration of a decentralized swarm of rotary-wing
UAV-relays, augmenting the coverage and service capabilities of a terrestrial
base station. Our goal is to minimize the time-average service latencies
involved in handling transmission requests from ground users under Poisson
arrivals, subject to an average UAV power constraint. Equipped with rate
adaptation to efficiently leverage air-to-ground channel stochastics, we first
derive the optimal control policy for a single relay via a semi-Markov decision
process formulation, with competitive swarm optimization for UAV trajectory
design. Accordingly, we detail a multiscale decomposition of this construction:
outer decisions on radial wait velocities and end positions optimize the
expected long-term delay-power trade-off; consequently, inner decisions on
angular wait velocities, service schedules, and UAV trajectories greedily
minimize the instantaneous delay-power costs. Next, generalizing to UAV swarms
via replication and consensus-driven command-and-control, this policy is
embedded with spread maximization and conflict resolution heuristics. We
demonstrate that our framework offers superior performance with respect to
average service latencies and average per-UAV power consumption: 11x faster
data payload delivery relative to static UAV-relay deployments and 2x faster
than a deep-Q network solution; remarkably, one relay with our scheme
outclasses three relays under a joint successive convex approximation policy by
62%.

本文描述了一种使用无人机中继的去中心化群体编队协同技术，以增强地面基站的覆盖范围和服务能力，通过使用半马尔可夫决策过程和竞争群体优化对单个中继器进行了优化控制策略的导出，并通过复制和共识驱动的命令和控制泛化到 UAV 群体。我们演示了我们的框架相对于静态 UAV 中继部署的平均服务延迟和每个 UAV 功耗的优越性。

基于 SMDPs 的功耗受限无人机中继多尺度自适应调度和路径规划

Multiscale Adaptive Scheduling and Path-Planning for Power-Constrained UAV-Relays via SMDPs

The information contained in hierarchical topology, intrinsic to many
networks, is currently underutilised. A novel architecture is explored which
exploits this information through a multiscale decomposition. A dendrogram is
produced by a Girvan-Newman hierarchical clustering algorithm. It is segmented
and fed through graph convolutional layers, allowing the architecture to learn
multiple scale latent space representations of the network, from fine to coarse
grained. The architecture is tested on a benchmark citation network,
demonstrating competitive performance. Given the abundance of hierarchical
networks, possible applications include quantum molecular property prediction,
protein interface prediction and multiscale computational substrates for
partial differential equations.

研究了利用多尺度分解通过层次拓扑信息的新型体系结构，并通过 Girvan-Newman 层次聚类算法生成了一棵树，使得架构可以学习从粗到细的多尺度潜在空间表示，并在基准引文网络上表现出竞争力。

一个使用层次聚类的多尺度图卷积网络

A Multiscale Graph Convolutional Network Using Hierarchical Clustering

In many inverse problems, model parameters cannot be precisely determined
from observational data. Bayesian inference provides a mechanism for capturing
the resulting parameter uncertainty, but typically at a high computational
cost. This work introduces a multiscale decomposition that exploits conditional
independence across scales, when present in certain classes of inverse
problems, to decouple Bayesian inference into two stages: (1) a computationally
tractable coarse-scale inference problem; and (2) a mapping of the
low-dimensional coarse-scale posterior distribution into the original
high-dimensional parameter space. This decomposition relies on a
characterization of the non-Gaussian joint distribution of coarse- and
fine-scale quantities via optimal transport maps. We demonstrate our approach
on a sequence of inverse problems arising in subsurface flow, using the
multiscale finite element method to discretize the steady state pressure
equation. We compare the multiscale strategy with full-dimensional Markov chain
Monte Carlo on a problem of moderate dimension (100 parameters) and then use it
to infer a conductivity field described by over 10,000 parameters.

介绍了一种利用多尺度分解的贝叶斯推断方法，通过利用条件独立性将计算量高的贝叶斯推断问题分解为两个阶段，并利用最优输运映射来描述粗细尺度数据之间的非高斯联合分布。这种方法在地下水流反演问题上展现出了出色的性能。