CIF: Small: Statistical Learning Methods for Communications, Sensing and Control in Actuated Wireless Networks

随着无人驾驶飞行器（UAV）或无人机的发展进步，这种飞行器有可能用于增强和扩展蜂窝无线通信基础设施。作为人类，我们在使用无线通信时已经参与了有关通信和驱动的决策。 如果我们觉得我们的连接是坏的，我们希望在我们的手机和基站之间体验一个更好的无线信道。 无线通信遭受几种有害的传播效应，例如信号强度的随机波动、由于障碍物引起的信号阻塞以及由于信号从建筑物和其它大物体反弹而引起的多径效应。 移动的基站可以通过移动到需要额外覆盖的地方来减轻这些影响，包括跟踪移动的热点（存在额外业务或拥塞的地方）。 为了实现这种无人机增强的通信基础设施，这个为期一年的项目旨在回答有关合作和分散估计以及本地化的一些关键问题。在这个探索性的项目中，一系列无人机被设想协同工作。 为此，将考虑在无人机之间进行估计的集中式和分布式方法。建立在以前的工作上的观察驱动调度，未知的潜在分布的情况下，感测信号将被考虑。 特别是，数据驱动的方法，使这些分布的隐式学习在一个分散的方式将被开发。 观察驱动的调度也将完全分散，不同的基于共识的策略将在理论上分析其性能。 基于张量分解的非参数定位方法将变得更加实用。特别是，将制定一种方法，用于确定在一个领域中的目标的数量进行定位;以前的工作假设，这个数字是已知的。 将开发基于特征图和非参数张量分解方法的信道估计。 该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

As the development of unmanned aerial vehicles (UAVs) or drones advances, there is potential for such aerial vehicles to be used to enhance and extend the cellular wireless communication infrastructure. As humans, we already engage in decisions about communication and actuation when using wireless communications. If we feel our connection is bad, we move in the hopes of experiencing a better wireless channel between our cellular phone and a base station. Wireless communications suffer from several deleterious propagation effects such as random fluctuations in signal strength, signal blockage due to obstructions and multipath effects which are due to signals bouncing off of buildings and other large objects. A mobile base station could serve to mitigate these effects by moving where additional coverage is needed, including the tracking of mobile hotspots (where there is extra traffic or congestion). To achieve this UAV enhanced communications infrastructure, this year-long project seeks to answer some key questions about cooperative and decentralized estimation as well as localization. In this exploratory project, a collection of UAVs is envisioned to work in concert. To this end, both centralized and distributed methods for estimation among the UAVs will be considered. Building on prior work on observation driven scheduling, the case of unknown underlying distributions of the sensed signal will be considered. In particular, data driven methods which enable the implicit learning of those distributions in a decentralized fashion will be developed. Observation driven scheduling will also be fully decentralized and different consensus-based strategies will be theoretically analyzed in terms of their performance. Tensor factorization-based non-parametric localization methods will be made more practical. In particular, a method for determining the number of targets in a field to be localized will be developed; prior work assumed that this number was known. Channel estimation based on feature maps and the non-parametric tensor factorization methods will be developed. A performance analysis of feature map-based methods will conducted.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.