NSF-AoF: CNS Core: Small: Machine Learning Based Physical Layer and Mobility Management Solutions Towards 6G

NSF-AoF：CNS 核心：小型：面向 6G 的基于机器学习的物理层和移动管理解决方案

• 批准号: 2224322
• 负责人: Danijela Cabric
• 金额: $ 39.27万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2224322&HistoricalAwards=false
• 关键词: NSF; AoF; CNS; Core; Small

5G evolution and future 6G cellular networks are targeting operations at higher millimeter wave and sub-Tera Hertz (sub-THz) bands due to the availability of large channel bandwidths to further improve data rate, latency, quality-of-service, and reliability. However, the use of these bands for mobile radio access imposes substantial technical challenges, including the quality, cost- and energy-efficiency of the electronics, the extreme path loss and propagation characteristics, and the overall deployment costs to provide indoor and outdoor network coverage with mobility support. Considering these challenges, this project will investigate the utility of machine learning algorithms, that have been successful in solving complex problems in various domains, for designing physical layer technologies and network management procedures, involved in both user equipment and base stations, that aim to improve robustness and reliability of connectivity under mobility. The project’s expected contributions are at the forefront of emerging 6G standard and applications of modern machine learning tools in wireless communications at high frequency bands.The research will address three key thrusts: i) Thrust 1 will develop machine learning assisted and data driven approaches for user equipment beam training and tracking using compressive sensing-based channel probing for low latency and robustness to phased antenna array impairments. In addition, it will accelerate beam training and tracking on the base station side by using deep reinforcement learning for optimizing beam probing strategies based on environment characteristics and user trajectories. The outcome of this thrust will be significant reduction in beam management overhead in the presence of mobility; ii) Thrust 2 will use a novel receiver processing architecture where the signal path exploits convolutional neural network layers in both time and frequency domains to compensate the effects of the power amplifier nonlinearity and phase noise in a wideband orthogonal frequency division multiplexing (OFDM) receiver, while accounting for frequency-selective multipath channel effects. The outcome of this thrust will be improvement in transmitter power efficiency, coverage and bit error probability. iii) Thrust 3 will develop intelligent handover algorithms to minimize disruptions in user connectivity by exploiting position estimates and beam-level reference signal power measurements with distributed deep reinforcement learning while considering user rate requirements and reducing measurement reporting and sharing overheard between base stations. The research methodology will rely on extensive measurements and data set generations using millimeter wave testbeds for the development and evaluation of the proposed solutions.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.

5G 演进和未来 6G 蜂窝网络的目标是在更高毫米波和亚太赫兹 (sub-THz) 频段上运行，因为可以使用大通道带宽来进一步提高数据速率、延迟、服务质量和可靠性。然而，使用这些频段进行移动无线接入带来了巨大的技术挑战，包括电子设备的质量、成本和能源效率、极端路径损耗和传播特性以及提供室内和室外网络覆盖的总体部署成本和考虑到这些挑战，该项目将研究机器学习算法的实用性，这些算法已成功解决各个领域的复杂问题，用于设计涉及用户设备和基站的物理层技术和网络管理程序。该项目的预期贡献是新兴 6G 标准和现代机器学习工具在高频段无线通信中的应用的前沿。该研究将解决三个关键目标： i) 主旨 1将开发机器学习辅助和数据驱动的使用基于压缩感知的信道探测进行用户设备波束训练和跟踪的方法，可实现低延迟和对相控天线阵列损伤的鲁棒性。此外，它将通过使用深度强化学习来优化波束探测，从而加速基站侧的波束训练和跟踪。基于环境特征和用户轨迹的策略将在存在移动性的情况下显着减少波束管理开销；ii) Thrust 2 将使用一种新颖的接收器处理架构，其中信号路径利用卷积神经网络层。时域和频域，以补偿宽带正交频分复用 (OFDM) 接收器中功率放大器非线性和相位噪声的影响，同时考虑频率选择性多径信道效应。这一推动的结果将是发射机功率的提高。 iii) Thrust 3 将开发智能切换算法，通过利用分布式深度强化学习的位置估计和波束级参考信号功率测量，同时考虑用户速率要求并减少对用户连接的干扰。该研究方法将依赖于使用毫米波测试台进行的广泛测量和数据集生成，以开发和评估所提出的解决方案。该奖项反映了 NSF 的法定使命，并被认为值得通过评估获得支持。利用基金会的智力优势和更广泛的影响审查标准。