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，毫米波（MMWave）和Terahertz（THZ）频带支持宽带的带宽传输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，可靠和高数据速率通信的关键启用技术是大量多重输入多重输出（MIMO）系统的部署，该系统由大量的传输和接收天线组成。这允许密集的空间采样和使用空间自由度以进行有效的通信系统设计。但是，由于电路的复杂性，成本增加和功耗而不切实际，传统的射频（RF）电路设计不切实际。这些限制导致非线性要求开发非传统处理算法，该算法是最近开发的机器学习网络是合适的。另一个挑战是无线通道，在这些较高的频率下，在频段的不同频率上，自然界具有显着的路径损失。为了应对较高的路径损失，需要寻找方法来提高通道质量，该项目应用高级通道变形方法。 由工作产生的理论思想将得到适当的实验性工作，以导致实际上可行的系统。该项目将导致最先进的无线通信系统，该系统应有助于维持无线技术领域的领导能力，以培训在这一战略重要性领域的下一代研究人员。开发下一代MMWave和THZ基于大规模的多重输入多数输出（MIMO）无线通信系统，使用机器学习（ML）Algorithms，该项目具有四个主要的组合。一种是在严重受限的环境中基于ML的稀疏通道建模，即有限的感应，测量数量有限，精度和系统缺陷。这项工作将域知识与数据驱动技术相结合，以应对系统中的非线性和缺陷。第二个组件是使用块 - 帕克斯技术的新通道建模以及基于模型和基于ML的推理算法的开发。块通道结构在两个维度上不能在分析上进行分析，并要求ML技术从数据中学习。第三个组件是合并可重构智能表面（RISS）以进行频道变形以提高通道质量。 该项目的最终组成部分是实验性工作，渠道响起和射线追踪，以支持，验证和完善理论模型。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估来支持的。

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