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构设备，以提供具有各种性能需求的应用，例如高数据速率、低功耗和低延迟。大规模多输入多输出（MIMO）已被广泛认为是在未来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。为了充分释放大规模MIMO通信系统所要求的潜在性能增益，在发射机处，特别是在基站侧，具有及时且准确的信道状态信息（CSI）是至关重要的。本计画的主要目标是探索一种系统化的方法，在大规模多输入多输出通讯系统中加速信道状态信息的处理。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。该项目的成果可以促进我们的社会向智能无线网络时代的过渡，在智能无线网络时代，无线通信系统可以提供无缝支持，以匹配大量网络设备的许多不同的无线应用，并支持许多具有高计算需求和服务质量需求的服务。此外，首席研究员致力于通过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当前的电气和计算机工程课程来整合研究和教育。该项目还将为学生提供学习，开发和应用先进无线通信的机会，这是他们从传统的学士学位中无法获得的。或多发性硬化症由于复杂的传统模型以及AI模型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处理可能是极其困难的。在本研究项目中，将获得新算法的理论分析和性能评估，这些新算法设计用于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，以及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结构可以显着降低每秒浮点运算（FLOPs）的复杂性。因此，AI实现可以被加速1到2个数量级，而不会损失用于大规模MIMO通信系统中的及时CSI处理的重构精度。系统的方法可以很容易地扩展，以促进许多其他应用程序，遇到类似的挑战，并提出类似的需求，减少延迟和计算的需要。此外，该研究项目还将极大地促进对支持人工智能的大规模MIMO系统的理解，以实现更好的频谱和功率效率，并将为设计需要高度自主性的高效机器对机器通信做出根本性贡献。该奖项反映了NSF的法定使命，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。

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.