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

下一代无线通信将需要支持在通信、计算和功率方面具有不同能力的异类设备，以提供具有高数据速率、低功耗和低延迟等各种性能要求的应用。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大容量和高频谱效率的一种引人注目的技术。为了充分释放大规模MIMO通信系统所带来的潜在性能提升，在发射端，尤其是基站侧，及时准确地获得信道状态信息至关重要。该项目的主要目标是探索一种系统化的方法，在海量MIMO通信系统中以数量级的速度加速CSI处理。该项目将为提高下一代无线通信的数据速率和能量效率、频谱效率奠定基础。与该项目相关的研究工作可能会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Beyond 5G无线网络、自主机器对机器通信、车载网络和物联网。该项目的成果将促进我们的社会过渡到智能无线网络时代，在这个时代，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要求和服务质量需求的服务。此外，首席调查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三所参与大学的现有电气和计算机工程课程，从而将研究和教育结合起来。该项目还将为学生提供学习、开发和应用高级无线通信的机会，这是他们在传统的学士或硕士课程中无法获得的。由于复杂的传统模型以及人工智能模型的开发和跨环境的不一致性能，满足大规模MIMO系统中的相干时间要求对于CSI处理来说可能非常困难。在本研究项目中，将对设计的新算法进行理论分析和性能评估：1)优化CSI重建过程中的解压缩特征；2)简化用于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AI结构可以显著降低每秒浮点运算(FLOPS)的复杂性。因此，对于大规模MIMO通信系统中的及时CSI处理，AI实现可以在不损失重建精度的情况下加速1到2个数量级。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，以获得更好的频谱和功率效率，并将从根本上促进需要高自主水平的高效机器对机器通信的设计。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。

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.