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; 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在通信，计算和功能上具有不同功能的不同功能的异质设备，以交付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。大量多输入多重输出（MIMO）已被广泛认为是在未来的无线通信网络中实现高容量和高频谱效率的引人注目的技术。为了完全释放大规模的MIMO通信系统所声称的潜在性能增长，至关重要的是在发射机，尤其是在基站侧的发射机上及时，准确的渠道状态信息（CSI）至关重要。该项目的主要目标是探索一种系统的方法，该方法通过大规模的MIMO通信系统中的数量级加速CSI处理。该项目将奠定基础，以提高数据速率和能源效率，下一代无线通信的光谱效率。与该项目相关的研究工作可能会对无线通信系统的轻质人工智能（AI）设计产生重大影响，这将进一步改善许多应用领域，包括超过5G无线网络，自动驾驶机器对机器通信，车辆网络和企业的范围。该项目的结果可以促进我们社会向智能无线网络时代的过渡，无线通信系统可以提供无缝的支持，以匹配许多不同的无线应用程序，并为许多具有高计算需求和服务质量需求的服务提供支持。此外，首席研究人员致力于通过将无线通信系统中的新兴计算和轻量级AI引入三所参与大学的当前电气和计算机工程课程中来整合研究和教育。该项目还将为学生提供学习，开发和应用高级无线通信的机会，他们不会从传统的B.S.那里获得。或M.S.课程。由于复杂的传统模型以及AI模型的开发以及在环境之间的AI模型开发和不一致的性能，对大规模MIMO系统中的相干时间需求可能非常困难。在该研究项目中，将针对专为CSI重建过程中的压缩功能进行优化的新型算法获得理论分析和性能评估，2）简化了多速率压缩和重建的AI结构，以及3）自主CSI重建性能评估和AI模型模型更新。优化的功能和简化的AI结构可以大大降低每秒浮点操作（拖鞋）方面的复杂性。因此，AI实现可以通过1到2个数量级加速，而不会在大规模的MIMO通信系统中及时地进行重建准确性。可以很容易地扩展系统的方法，以促进许多其他遇到类似挑战的应用程序，并在减少延迟和计算需求方面提出了类似的需求。此外，该研究项目可以极大地促进AI支持的大型MIMO系统的理解，以提高频谱和功率效率，并将在根本上为高效的机器对机器通信的设计做出贡献，这些沟通需要高水平的自主权。该奖项反映了NSF的法定任务，并通过评估基金会的范围来反映出支持者的支持者，并通过基金会的范围进行了评估和宽广的影响。