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

NSF-AoF：协作研究：CIF：小型：通过增强型毫米波信道建模和估计、信道变形和机器学习实现 6G 无线通信

• 批准号: 2225617
• 负责人: Bhaskar Rao
• 金额: $ 60万
• 依托单位: University of California-San Diego
• 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: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2225617&HistoricalAwards=false
• 关键词: NSF; AoF; Collaborative; Research; CIF

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.

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，支持宽带宽传输的毫米波 (mmWave) 和太赫兹 (THz) 频段将在提供下一代系统设想的先进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，该系统由大量用于传输和接收的天线组成。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，由于电路复杂性、成本和功耗增加，小外形尺寸使得传统射频 (RF) 电路设计变得不切实际。这些限制导致非线性，需要开发最近开发的机器学习网络适合的非传统处理算法。另一个挑战是无线信道在这些较高频率下具有显着的路径损耗，并且在频带中的不同频率上本质上有所不同。为了应对较高的路径损耗，需要找到提高信道质量的方法，该项目为此应用了先进的信道变形方法。 这项工作产生的理论想法将得到适当的实验工作的支持，以形成实际可行的系统。该项目将带来最先进的无线通信系统，这将有助于保持无线技术的领先地位，并培训这一具有战略意义的领域的下一代研究人员。 开发下一代基于毫米波和太赫兹的大规模多波通信输入多输出（MIMO）无线通信系统采用机器学习（ML）算法，该项目有四个主要组成部分。一是在严重受限的环境中基于机器学习的稀疏通道建模，即有限的传感、有限的测量数量、有限的精度和系统缺陷。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型通道建模以及相关的基于模型和基于机器学习的推理算法的开发。块通道结构在二维上无法进行分析处理，需要机器学习技术从数据中学习。第三个组成部分是结合可重构智能表面（RIS）进行通道变形，以提高通道质量。 该项目的最后一个组成部分是实验工作、通道探测和射线追踪，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进行评估，被认为值得支持审查标准。

项目成果

Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions

• DOI: 10.48550/arxiv.2210.07236
• 发表时间: 2022-10
• 期刊: ArXiv
• 作者: Kuan-Lin Chen;H. Garudadri;B. Rao

R-fiducial: Millimeter Wave Radar Fiducials for Sensing Traffic Infrastructure

R-fiducial：用于传感交通基础设施的毫米波雷达基准点

• DOI: 10.1109/vtc2023-spring57618.2023.10199374
• 发表时间: 2023
• 期刊: 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring
• 作者: Dunna, Manideep;Bansal, Kshitiz;Ganesh, Sanjeev Anthia;Patamasing, Eamon;Bharadia, Dinesh
• 通讯作者: Bharadia, Dinesh

Maximum Likelihood-Based Gridless DoA Estimation Using Structured Covariance Matrix Recovery and SBL With Grid Refinement

• DOI: 10.1109/tsp.2023.3254919
• 发表时间: 2023-01-01
• 期刊: IEEE TRANSACTIONS ON SIGNAL PROCESSING
• 影响因子: 5.4
• 作者: Pote, Rohan R.;Rao, Bhaskar D.
• 通讯作者: Rao, Bhaskar D.

Light-Weight Sequential SBL Algorithm: An Alternative to OMP

轻量级顺序 SBL 算法：OMP 的替代方案

• DOI: 10.1109/icassp49357.2023.10096051
• 发表时间: 2023
• 作者: Pote, Rohan R.;Rao, Bhaskar D.
• 通讯作者: Rao, Bhaskar D.

Regularized Neural Detection for Millimeter Wave Massive Mimo Communication Systems with One-Bit Adcs

• DOI: 10.1109/icassp49357.2023.10096921
• 发表时间: 2023-06
• 期刊: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Aditya Sant;B. Rao