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.

人们对无线网络的需求呈指数级增长，而频谱资源却受到严重的限制。毫米波(毫米波)频谱提供比微波频谱高出数量级的带宽，并构成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，信号阻塞和动态迁移率扰乱了光束对准，限制了频谱的有效利用。该项目的目标是建立一种基于学习的信道估计和跟踪、网络资源分配和优化方案的新范例，用于在高度动态甚至非平稳的环境中运行的毫米波网络。该项目有可能改变下一代无线网络的运营方式，实现更智能、自适应和情景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，并随后进行全面的系统级验证。第一种推力利用非凸优化的思想来快速感知无线信道并学习最优波束。快速信道侦听和波束跟踪方案恢复稀疏的毫米波信道，只需最少的测量，即使在高速移动性下经历的高动态条件下也是如此。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，以实现无缝的毫米波连接。提出了一种新的在线框架来学习AP的统计数据并相应地对其进行排序。这一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。第三个推力研究协作和分布式资源分配算法，通过在自动驾驶和工业机器人/物联网等颠覆性应用中遇到的时变网络拓扑实现动态数据共享。最后，将在各种环境设置和拓扑条件下对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。

项目成果

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

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

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.

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

Reward Teaching for Federated Multi-armed Bandits

• DOI: 10.1109/isit54713.2023.10206444
• 发表时间: 2023-05
• 期刊: 2023 IEEE International Symposium on Information Theory (ISIT)
• 作者: Chengshuai Shi;Wei Xiong;Cong Shen;Jing Yang