Collaborative Research: Expedite CSI Processing with Lightweight AI in Massive MIMO Communication Systems

合作研究：在大规模 MIMO 通信系统中利用轻量级 AI 加速 CSI 处理

• 批准号: 2139569
• 负责人: Feng Ye
• 金额: $ 16.65万
• 依托单位: University of Dayton
• 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: 2022-03-01 至 2023-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2139569&HistoricalAwards=false
• 关键词: Collaborative; Research; Expedite; CSI; Processing

Next generation wireless communications will need to support heterogeneous devices with different capabilities on communications, computations, and power to deliver applications with various performance demands such as high data rate, low power consumption, and low latency. Massive multiple-input multiple output (MIMO) has been widely considered a compelling technology for achieving high capacity and high spectrum efficiency in the future wireless communication networks. To fully unleash the potential performance gains claimed by massive MIMO communication systems, it is of vital importance to have timely and accurate channel state information (CSI) at the transmitters, especially at the base station side. The main goal of this project is to explore a systematic approach that accelerates the CSI processing by orders of magnitude in massive MIMO communication systems. The project will lay a foundation to enhancing data rate and energy efficiency, spectral efficiency in the next-generation wireless communications. The research efforts associated with the project can have a significant impact on the lightweight artificial intelligence (AI) design for wireless communication systems, which will further improve many application domains, including beyond 5G wireless networks, autonomous machine-to-machine communications, vehicular networks, and Internet-of-Things. The outcomes of the project can foster the transition of our society into the intelligent wireless networking age, where wireless communication systems can provide seamless support to match many different wireless applications for massive network devices and support many services with high computation demands and quality of service needs. Moreover, the Principal Investigators are committed to integrating research and education by introducing emerging computing and lightweight AI in wireless communication systems into the current electrical and computer engineering curricula in the three participating universities. The project will also provide opportunities for students to learn, develop and apply advanced wireless communications, which they would not receive from a traditional B.S. or M.S. curriculum.Meeting the coherence time requirement in massive MIMO systems can be extremely difficult for CSI processing due to the complex traditional model as well as AI model development and inconsistent performance across environments. In this research project, theoretical analysis and performance evaluations will be obtained for novel algorithms designed for 1) optimization on the decompressed feature in the CSI reconstruction process, 2) simplifying the AI structures for multi-rate compression and reconstruction, and 3) autonomous CSI reconstruction performance evaluation and AI model update. The optimized features and simplified AI structures can significantly reduce the complexity in terms of floating point operations per second (FLOPs). Thus, the AI implementation can be accelerated by 1 to 2 orders of magnitude without losing reconstruction accuracy for timely CSI processing in massive MIMO communication systems. The systematic methodologies can be readily extended to facilitate many other applications that encounter the similar challenges and present similar needs on reducing latency and computation needs. Furthermore, this research project can greatly promote the understanding in AI-supported massive MIMO systems for better spectrum and power efficiency and will contribute fundamentally to the design of highly efficient machine-to-machine communications that require high level of autonomy.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.

下一代无线通信将需要支持具有不同通信、计算和功率能力的异构设备，以提供具有各种性能需求的应用程序，例如高数据速率、低功耗和低延迟。大规模多输入多输出（MIMO）被广泛认为是未来无线通信网络中实现高容量和高频谱效率的一项引人注目的技术。为了充分释放大规模 MIMO 通信系统的潜在性能增益，发射机（尤其是基站侧）及时、准确的信道状态信息（CSI）至关重要。该项目的主要目标是探索一种系统方法，可将大规模 MIMO 通信系统中的 CSI 处理速度提高几个数量级。该项目将为提高下一代无线通信的数据速率和能源效率、频谱效率奠定基础。与该项目相关的研究工作可以对无线通信系统的轻量级人工智能（AI）设计产生重大影响，这将进一步改善许多应用领域，包括超越5G的无线网络、自主机器对机器通信、车辆网络和物联网。该项目的成果可以促进我们的社会向智能无线网络时代的转变，无线通信系统可以为海量网络设备提供无缝支持，以匹配多种不同的无线应用，并支持许多具有高计算需求和服务质量需求的服务。此外，首席研究员致力于将无线通信系统中的新兴计算和轻量级人工智能引入到三所参与大学当前的电气和计算机工程课程中，从而将研究和教育结合起来。该项目还将为学生提供学习、开发和应用先进无线通信的机会，这是他们从传统理学学士学位中无法获得的。或硕士由于复杂的传统模型以及人工智能模型开发以及跨环境的性能不一致，满足大规模 MIMO 系统的相干时间要求对于 CSI 处理来说可能极其困难。在本研究项目中，将为新算法进行理论分析和性能评估，这些算法旨在：1）CSI 重建过程中解压缩特征的优化，2）简化多速率压缩和重建的 AI 结构，3）自主 CSI重建性能评估和AI模型更新。优化的功能和简化的人工智能结构可以显着降低每秒浮点运算（FLOP）的复杂性。因此，AI 实现可以加速 1 到 2 个数量级，而不会损失大规模 MIMO 通信系统中及时 CSI 处理的重建精度。系统方法可以很容易地扩展，以促进许多其他遇到类似挑战并在减少延迟和计算需求方面提出类似需求的应用程序。此外，该研究项目可以极大地促进对人工智能支持的大规模 MIMO 系统的理解，以实现更好的频谱和功率效率，并将从根本上为需要高度自治的高效机器对机器通信的设计做出贡献。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值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

Uplink-Aided Downlink Channel Estimation for a High-Mobility Massive MIMO-OTFS System

高移动性大规模 MIMO-OTFS 系统的上行链路辅助下行链路信道估计

• DOI: 10.1109/globecom48099.2022.10001420
• 发表时间: 2022-12
• 期刊: Proceedings of IEEE GLOBECOM 2022
• 作者: Ying, Daidong;Ye, Feng;Hu, Rose Qingyang;Qian, Yi
• 通讯作者: Qian, Yi

An Evaluation Platform for Channel Estimation in MIMO Systems

MIMO 系统中信道估计的评估平台

• DOI: 10.1109/naecon58068.2023.10365882
• 发表时间: 2023-08
• 期刊: NAECON 2023 - IEEE National Aerospace and Electronics Conference
• 作者: Mercado;Kumar, Venkataramani;Ye, Feng;Hu, Rose Qingyang;Qian, Yi
• 通讯作者: Qian, Yi

Towards Detection of Zero-Day Botnet Attack in IoT Networks Using Federated Learning

使用联合学习检测物联网网络中的零日僵尸网络攻击

• DOI: 10.1109/icc45041.2023.10279423
• 发表时间: 2023-05-28
• 期刊: ICC 2023 - IEEE International Conference on Communications
• 作者: Jielun Zhang;Shicong Liang;Feng Ye;R. Hu;Yi Qian
• 通讯作者: Yi Qian