CNS Core: Medium: When Next Generation Wireless Networks Meet Machine Learning

对无线网络的需求正以指数速度增长，而频谱资源受到严重限制。毫米波（mmWave）频谱提供比微波频谱高几个数量级的带宽，并成为下一代无线网络标准（如802.11ad/ay和5G）的组成部分。由于高传播路径损耗，毫米波通信使用聚焦定向波束。不幸的是，信号阻塞和动态移动性破坏波束对准并限制频谱的有效使用。该项目的目标是建立一个基于学习的信道估计和跟踪，网络资源分配和优化方案的毫米波网络在一个高度动态的，甚至是非平稳的环境中运行的新范式。该项目有可能改变下一代无线网络的运行方式，实现更智能、自适应和环境感知的网络管理解决方案。该项目包括以下协同推进，以成功设计和实施毫米波通信网络，然后进行全面的系统级验证。第一个推力利用非凸优化的思想来快速检测无线信道并学习最佳波束。快速信道感测和波束跟踪方案即使在高速移动性下经历的高度动态条件下，也能以最少的测量恢复稀疏毫米波信道。第二个推力设计了基于在线学习的接入点（AP）扫描和关联方案，以实现无缝毫米波连接。提出了一种新的在线框架来学习AP的统计数据并对其进行相应的排名。该排名列表将使设备能够在连接断开之前快速识别具有高数据速率链路的合适AP，从而实现无处不在的连接。第三个重点是研究协作和分布式资源分配算法，实现在自动驾驶和工业机器人/物联网等破坏性应用中遇到的时变网络拓扑的动态数据共享。最后，将在各种环境设置和拓扑条件下对所设计的算法进行详尽的实验验证。该奖项反映了NSF的法定使命，并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，被认为值得支持。

The demand on wireless networks is increasing at an exponential pace whereas the spectrum resources are severely constrained. Millimeter-wave (mmWave) spectrum offers orders of magnitude higher bandwidth than the microwave spectrum and forms an integral part of next-generation wireless networking standards such as 802.11ad/ay and 5G. Because of high propagation path loss, mmWave communication uses focused directional beams. Unfortunately, signal blockage and dynamic mobility disrupt beam alignment and limit the efficient use of the spectrum. The goal of this project is to build a new paradigm of learning based channel estimation and tracking, network resource allocation, and optimization schemes for mmWave networks operating in a highly dynamic and even non-stationary environment. This project has the potential to transform the way how the next-generation wireless networks operate, enabling more intelligent, adaptive, and context-aware network management solutions.The project consists of the following synergistic thrusts for the successful design and implementation of mmWave communication networks, followed by a comprehensive system-level validation. The first thrust exploits ideas in non-convex optimization to quickly sense the wireless channels and learn optimal beams. The fast channel sensing and beam tracking schemes recover sparse mmWave channels with minimal measurements, even under highly dynamic conditions experienced under high-speed mobility. The second thrust designs online learning based access point (AP) scanning and association schemes for seamless mmWave connectivity. A novel online framework is proposed to learn the statistics of APs and rank them accordingly. This a ranked list will enable the device to identify a suitable AP with high data-rate link fast before the connection is dropped, thus achieving ubiquitous connectivity. The third thrust studies collaborative and distributed resource allocation algorithms, enabling dynamic data sharing with time-varying network topologies encountered in disruptive applications such as autonomous driving and industrial robotics/IoT. Finally, exhaustive experimental validation of the designed algorithms will be performed across various environmental settings and topological conditions.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.