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

CNS 核心：中：当下一代无线网络遇到机器学习时

• 批准号: 1956276
• 负责人: Jing Yang
• 金额: $ 80万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1956276&HistoricalAwards=false
• 关键词: CNS; Core; Medium; When; Next

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.

无线网络的需求正在以指数速度增长，而光谱资源受到严格限制。毫米波（mmwave）频谱的带宽比微波谱高的数量级，并形成了下一代无线网络标准的组成部分，例如802.11AD/AY和5G。由于高传播路径损失，MMWave通信使用聚焦的方向光束。不幸的是，信号阻塞和动态迁移率破坏了光束对齐，并限制了光谱的有效使用。该项目的目的是建立一个基于学习的渠道估计和跟踪，网络资源分配以及在高度动态甚至非平稳环境中运行的MMWave网络的优化方案的新范式。该项目有可能改变下一代无线网络的运作方式，从而实现更聪明，自适应和上下文感知的网络管理解决方案。该项目综合了以下协同设计和成功设计和实施MMWave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想法来快速感知无线通道并学习最佳光束。快速通道传感和光束跟踪方案即使在高速迁移率下经历的高度动态条件下，也以最小的测量恢复了稀疏的MMWave通道。第二个推力设计基于在线学习的访问点（AP）扫描和关联方案，以实现无缝MMWave连接性。提出了一个新颖的在线框架，以了解AP的统计数据并相应地对其进行排名。该排名列表将使设备能够在连接掉落之前快速识别具有高数据率链接的合适AP，从而实现无处不在的连接。第三个推力研究协作和分布式资源分配算法，使动态数据共享与随时间变化的网络拓扑结合，例如自动驾驶和工业机器人/IOT等颠覆性应用程序中遇到的时间变化。最后，将在各种环境环境和拓扑条件下对设计算法进行详尽的实验验证。该奖项反映了NSF的法定任务，并使用基金会的智力优点和更广泛的影响评估标准，认为值得通过评估来获得支持。

项目成果

Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization

• 发表时间: 2021-10
• 作者: Chengshuai Shi;Wei Xiong;Cong Shen;Jing Yang

Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints

• DOI: 10.48550/arxiv.2306.06265
• 发表时间: 2023-06
• 期刊: Mathematics
• 影响因子: 2.4
• 作者: Donghao Li;Ruiquan Huang;Cong Shen;Jing Yang

Thresholded Wirtinger Flow for Fast Millimeter Wave Beam Alignment

用于快速毫米波光束对准的阈值维廷格流

• DOI: 10.1109/ieeeconf51394.2020.9443380
• 发表时间: 2020
• 期刊: Systems and Computers
• 作者: Gan, Chao;Yang, Jing;Shen, Cong
• 通讯作者: Shen, Cong

Joint User Association and Wireless Scheduling with Smaller Time-Scale Rate Adaptation

联合用户关联和较小时标速率自适应的无线调度

• 发表时间: 2023
• 期刊: and Wireless Networks (WiOpt
• 作者: Wu, X.;Yang, J.;Zeng, H.;Li, B.
• 通讯作者: Li, B.

Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time

• 发表时间: 2021-07
• 作者: Yuyang Deng;M. Mahdavi