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

5G演进和未来的6G蜂窝网络的目标是更高的毫米波和亚太赫兹(亚太赫兹)频段的运营，这是因为有了大的信道带宽，以进一步提高数据速率、延迟、服务质量和可靠性。然而，将这些频段用于移动无线电接入带来了巨大的技术挑战，包括电子设备的质量、成本和能效、极端路径损耗和传播特性，以及为室内和室外网络覆盖提供移动性支持的总体部署成本。考虑到这些挑战，该项目将调查机器学习算法的用途，这些算法已经成功地解决了各个领域的复杂问题，用于设计用户设备和基站都涉及的物理层技术和网络管理程序，旨在提高移动性下连接的健壮性和可靠性。该项目的预期贡献是在新兴的6G标准和现代机器学习工具在高频无线通信中的应用方面处于领先地位。研究将解决三个关键问题：i)推力1将开发机器学习辅助和数据驱动的方法，用于用户设备波束训练和跟踪，使用基于压缩感知的信道探测来实现低延迟和对相控阵天线损伤的稳健性。此外，它还将使用深度强化学习来根据环境特征和用户轨迹优化波束探测策略，从而加快基站侧的波束训练和跟踪。这一推力的结果将是在存在移动性的情况下大幅降低波束管理开销；ii)推力2将使用一种新颖的接收器处理架构，其中信号路径在时间和频率域中利用卷积神经网络层来补偿宽带正交频分多路(Ofdm)接收器中功率放大器非线性和相位噪声的影响，同时考虑到频率选择性多径通道效应。这一推力的结果将是发射机功率效率、覆盖范围和误码率的改善。Iii)Struts 3将开发智能切换算法，通过分布式深度强化学习利用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，减少基站之间的测量报告和共享，从而将用户连接中断降至最低。研究方法将依赖于使用毫米波试验台的广泛测量和数据集生成来开发和评估拟议的解决方案。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。

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.