NSF-SNSF: Rapid Beamforming for Massive MIMO using Machine Learning on RF-only and Multi-modal Sensor Data

大规模多输入多输出(MMIMO)天线系统将是未来移动通信网络满足高数据传输速率、保证可靠性和降低时延的重要技术，所有这些都将使许多新的和令人兴奋的应用成为可能。MMIMO的一个关键特征是通过适当协调大量天线单元的设置来进行定向传输的能力，这需要仔细地改变每个单元的信号的相位和幅度特性。该项目与瑞士伯尔尼大学的Torsten Braun合作，旨在解决使用强化学习和联邦学习(FL)的概念实时计算正确配置这些巨型天线阵列所需的参数的挑战。该项目不仅将通过合作研究、双向访问和联合课程开发在美国和瑞士之间建立新的联系，而且还将在机器学习和无线社区之间建立新的联系。PI还将针对无线工程师提供关于应用机器学习的录制短视频教程，并在媒体共享平台上发布这些教程。最后，从研究活动中得出的所有结论，包括位置/愿景论文，都将在网络和通信领域的顶级同行评议会议和期刊上传播。在mMIMO中，定向传输的波束形成涉及调整传输信号的相位和幅度，以将信号引导到预期接收器，并将对其他用户的干扰降至最低。然而，作为设置在多天线系统上传输的预编码数据比特的先决条件步骤的信道估计过程可能是计算密集且耗时的。该项目考虑了与mMIMO系统中的波束形成相关的挑战，显著增加了与经典MIMO相比的计算开销。即使在计算技术上有了快速的进步，传统的处理也不能跟上实时配置mMIMO系统的要求，使得整个过程在信道相干时间内完成。该项目有两个主要的科学目标来应对这一挑战。它的目标是使用(I)分布式和联合学习和(Ii)多模式传感器数据来推进弹性和个性化信道估计的最新技术。对于目标(I)，研究将解决由于干扰而导致的导频信号污染的挑战，并基于共享的知识设计用于信道估计的个性化FL。对于目标(Ii)，研究将利用多模式传感器数据、迁移学习和基于注意力的变压器神经网络来最小化模型训练成本和延迟。开发的概念、方法和算法将在真实世界的实验和模拟中得到验证，这些实验和模拟基于在NSF竞技场和空中试验台上收集的真实数据。为模型和算法开发的数据集、代码将可用于独立验证和重复使用。这项建议是作为NSF-瑞士NSF牵头机构主动建议机会(NSF 23-049)的一部分授予的。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。

Massive Multiple Input Multiple Output (mMIMO) antenna systems will be an important technology for future mobile telecommunication networks to meet high data transfer rates, assured reliability, and reduced latency, all of which will enable many new and exciting applications. A key feature of mMIMO is the ability for directional transmissions by suitably coordinating the settings of large number of antenna elements, which requires careful alters the signal characteristics of phase and amplitude for each element. In collaboration with Torsten Braun at the University of Bern, Switzerland, this project aims to address the challenge of computing the parameters required to properly configure these massive antenna arrays in real time using the concepts of reinforcement learning and federated learning (FL). This project will forge new connections not only between the U.S. and Switzerland through collaborative research, bi-directional visits, and joint coursework development, but also between machine learning and wireless communities. The PIs will give also record short video tutorials on applied machine learning targeting wireless engineers, and release these on media-sharing platforms. Finally, all findings derived from the research activities, including position/vision papers, will be disseminated in top peer-reviewed conferences and journals in networking and communications.Beamforming for directional transmissions in mMIMO involves adjusting the phase and amplitude of the transmitted signals to direct the signal to the intended receiver and minimize interference with other users. However, the channel estimation process, a pre-requisite step for setting precoding data bits transmitted over a multi-antenna system, can be computationally intensive and time consuming. This project considers the challenges associated with beamforming in an mMIMO system, significantly increasing the computational overhead over classical MIMO. Even with rapid strides in computing technology, classical processing cannot keep up with the demands of configuring an mMIMO system in real-time, such that the entire process is completed within the channel coherence time. The project has two major scientific objectives to address this challenge. It aims to advance the state-of-the-art in resilient and personalized channel estimation using (i) distributed and federated learning and (ii) multi-modal sensor data. For objective (i), the research will address challenges of contamination of pilot signals due to interference and design of personalized FL for channel estimation based on shared knowledge among. For objective (ii), the research will leverage multimodal sensor data, transfer learning and attention-based transformer neural networks to minimize model training costs and delay. The concepts, approaches, and algorithms developed will be validated in real-world experiments and simulations based on realistic collected data on the NSF Colosseum and in over-the-air testbeds. The data sets, code developed for the models and algorithms will be available for independent validation and re-use. This proposal was awarded as part of the NSF-Swiss NSF Lead Agency Opportunity for unsolicited proposals (NSF 23-049).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.