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

NSF-SNSF：在纯射频和多模态传感器数据上使用机器学习实现大规模 MIMO 的快速波束成形

• 批准号: 2401047
• 负责人: Kaushik Chowdhury
• 金额: $ 40万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2028-02-29
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2401047&HistoricalAwards=false
• 关键词: NSF; SNSF; Rapid; Beamforming; Massive

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.

大规模多输入多输出（mMIMO）天线系统将是用于未来移动的电信网络的重要技术，以满足高数据传输速率、有保证的可靠性和减少的延迟，所有这些都将实现许多新的和令人兴奋的应用。mMIMO的关键特征是通过适当地协调大量天线元件的设置来进行定向传输的能力，这需要仔细地改变每个元件的相位和幅度的信号特性。与瑞士伯尔尼大学的Torsten Braun合作，该项目旨在解决使用强化学习和联邦学习（FL）的概念计算真实的时间正确配置这些大规模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，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有关应用机器学习的简短视频教程，并在媒体共享平台上发布。最后，研究活动的所有成果，包括立场/愿景论文，将在网络和通信领域的顶级同行评审会议和期刊上传播。mMIMO中定向传输的波束成形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，并最大限度地减少对其他用户的干扰。然而，信道估计过程（用于设置在多天线系统上发送的预编码数据比特的先决条件步骤）可能是计算密集的和耗时的。该项目考虑了mMIMO系统中与波束成形相关的挑战，大大增加了经典MIMO的计算开销。即使计算技术突飞猛进，经典处理也无法跟上实时配置mMIMO系统的需求，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。它旨在使用（i）分布式和联邦学习以及（ii）多模态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），该研究将解决由于干扰而导致的导频信号污染的挑战以及基于共享知识的用于信道估计的个性化FL的设计。对于目标（ii），该研究将利用多模态传感器数据，迁移学习和基于注意力的Transformer神经网络，以最大限度地减少模型训练成本和延迟。开发的概念，方法和算法将在现实世界的实验和模拟中进行验证，这些实验和模拟基于NSF Colosseum和空中测试平台上收集的真实数据。数据集、为模型和算法开发的代码将可供独立验证和重复使用。该提案是作为NSF-瑞士NSF牵头机构主动提案机会（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知识价值和更广泛的影响审查标准进行评估，被认为值得支持。