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，这种飞行器有可能被用来增强和扩展蜂窝无线通信基础设施。作为人类，我们已经参与了使用无线通信时关于通信和激励的决定。如果我们觉得我们的连接不好，我们会采取行动，希望体验到手机和基站之间更好的无线通道。无线通信受到几种有害传播效应的影响，例如信号强度的随机波动、由于障碍物造成的信号阻塞以及由于信号从建筑物和其他大型物体反弹而产生的多径效应。移动基站可以通过移动到需要更多覆盖的地方来缓解这些影响，包括跟踪移动热点(在那里有额外的流量或拥堵)。为了实现无人机增强的通信基础设施，这个为期一年的项目试图回答一些关于合作和分散估计以及本地化的关键问题。在这个探索性项目中，设想了一组无人机协同工作。为此，将考虑无人机之间的集中式和分布式估计方法。在先前关于观测驱动调度的工作的基础上，将考虑未知感测信号的潜在分布的情况。特别是，将开发能够以分散方式隐含地学习这些分布的数据驱动方法。观察驱动的调度也将是完全分散的，将从理论上分析不同的基于共识的策略的性能。基于张量分解的非参数定位方法将更加实用。特别是，将制定一种确定要本地化的实地目标数量的方法；以前的工作假定这一数字是已知的。将发展基于特征映射和非参数张量分解的信道估计方法。将对基于要素地图的方法进行性能分析。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.