NSF-AoF: Collaborative Research: CIF: Small: 6G Wireless Communications via Enhanced Channel Modeling and Estimation, Channel Morphing and Machine Learning for mmWave Bands

该项目解决了下一代6G无线通信系统的挑战。对于这些系统，支持宽带传输的毫米波(毫米波)和太赫兹(THz)频段将在提供下一代系统设想的高级服务方面发挥重要作用。由于波长较小，实现可靠和高数据速率通信的关键技术是部署大规模多输入多输出(MIMO)系统，该系统由非常大量的用于发送和接收的天线组成。这允许密集的空间采样和使用空间自由度来进行有效的通信系统设计。然而，由于电路复杂、成本增加和功耗增加，小的外形系数使得传统的射频(RF)电路设计不切实际。这些约束导致了非线性，这就要求开发最近开发的机器学习网络适用的非传统处理算法。另一个挑战是无线信道，它在这些较高的频率上具有显著的路径损耗，并且在频带中的不同频率上具有不同的性质。为了处理更高的路径损耗，需要找到提高信道质量的方法，本项目将先进的信道变形方法应用于此。这项工作产生的理论想法将得到适当的实验工作的支持，以导致实际可行的系统。该项目将带来最先进的无线通信系统，有助于保持无线技术的领先地位，并培训这一战略重要领域的下一代研究人员。为了开发使用机器学习(ML)算法的下一代基于毫米波和太赫兹的大规模多输入多输出(MIMO)无线通信系统，该项目有四个主要组成部分。一种是基于ML的稀疏信道建模，适用于感知受限、测量次数有限、精度有限和系统缺陷等严重约束的环境。这项工作将领域知识与数据驱动技术相结合，以处理系统中的非线性和缺陷。第二个组成部分是使用块稀疏技术的新的信道建模，以及相关的基于模型和基于ML的推理算法的开发。块通道结构在两个维度上不能分析处理，需要ML技术来从数据中学习。第三个组件是结合可重新配置的智能表面(RISS)以进行通道变形以提高通道质量。这个项目的最后一个组成部分是实验工作，频道探测和射线追踪，以支持、验证和完善理论模型。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。

The project addresses challenges of next generation 6G wireless communication systems. For these systems, millimeter-wave (mmWave) and terahertz (THz) frequency bands that support wide bandwidth transmissions will play an important role in providing the advanced services envisioned of next generation systems. Due to the small wavelength, a key enabling technology for reliable and high data rate communication is the deployment of massive Multiple Input Multiple Output (MIMO) systems which consist of a very large number of antennas for transmission and reception. This allows for dense spatial sampling and use of spatial degrees of freedom for effective communication system design. However, the small form factor makes traditional radio-frequency (RF) circuitry design impractical due to circuit complexity, increased cost, and power consumption. These constraints lead to nonlinearities that call for developing nontraditional processing algorithms for which recently developed machine learning networks are suitable. Another challenge is the wireless channel which at these higher frequencies has significant path loss and varies in nature across different frequencies in the bands. To deal with the higher path loss there is a need for finding ways to enhance the quality of the channel, to which this project applies advanced channel morphing methods. The theoretical ideas resulting from the work will be supported with appropriate experimental work to lead to practically viable systems. The project will lead to state-of-the-art wireless communication systems that should help with maintaining leadership in wireless technology as well to train the next generation of researchers in this area of strategic importance.To develop next generation mmWave and THz based massive multiple input multiple-output (MIMO) wireless communication systems using machine learning (ML) algorithms, this project has four major components. One is ML-based sparse channel modeling in severely constrained environments, i.e., limited sensing, limited number of measurements, limited precision, and system imperfections. This work combines domain knowledge with data driven techniques to deal with the nonlinearities and imperfections in the system. A second component is novel channel modeling using block-sparse techniques and development of associated model-based and ML-based inference algorithms. Block channel structure is not analytically tractable in two dimensions and calls for ML techniques to learn from data. A third component is incorporation of reconfigurable intelligent surfaces (RISs) for channel morphing to improve channel quality. A final component of this project is experimental work, channel sounding and ray tracing, to support, validate, and refine the theoretical models.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.