CIF: Small: Signal Processing and Learning for NOMA Millimeter-Wave Massive MIMO Systems

CIF：小型：NOMA 毫米波大规模 MIMO 系统的信号处理和学习

• 批准号: 2413622
• 负责人: Yue Wang
• 金额: $ 32万
• 依托单位: Georgia State University Research Foundation, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2413622&HistoricalAwards=false
• 关键词: CIF; Small; Signal; Processing; Learning

With the proliferation of versatile devices and data-consuming services, the quest for spectrum efficiency has led to the combination of three disruptive technologies, namely millimeter-wave (mmWave) communications, massive multiple-input multiple-output (maMIMO), and non-orthogonal multiple access (NOMA). To fully understand the niche role the combined NOMA-mmWave-maMIMO technology could be playing in future wireless systems, this project develops sparse signal-processing and advance machine-learning techniques for efficient sensing and system optimization. The outcomes of this project are expected to make NOMA-mmWave-maMIMO an attractive candidate to support low-power high-data-rate applications in dense urban environments, including wireless broadband access, machine-type communications and Internet of Things. This project provides ample opportunities to students for research and workforce development, and further broadens societal impacts via curriculum development, research dissemination and public outreach. The spectrum sharing paradigm of NOMA needs to be re-engineered to account for the unique angular selectivity of mmWave and maMIMO channels. To this end, this project addresses research challenges related to spectral efficiency, energy efficiency, and intelligence, with focuses on (i) accurate and efficient channel sensing under hardware constraints and in practical settings, and on (ii) the holistic integration of signal processing and machine learning for system enhancement and real-time design optimization in NOMA-mmWave-maMIMO systems. Proposed ideas and methods combine multi-disciplinary research concepts and methodologies from wireless communications, signal processing, optimization and machine learning. This research effort is expected to advance the design of 5G wireless systems and beyond, but also to have a beneficial impact on the development of future intelligent wireless applications.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.

随着多功能设备和数据耗尽服务的扩散，对频谱效率的追求导致了三种破坏性技术的组合，即毫米波（MMWAVE）通信，大量的多输入多输入（Mamimo）和非正交多重访问（NOMA）。为了充分了解利基角色，Noma-Mmwave-Mamimo技术可能在未来的无线系统中发挥作用，该项目开发了稀疏的信号处理，并提高了机器学习技术，以实现有效的感应和系统优化。预计该项目的结果将使Noma-Mmwave-Mamimo成为在密集的城市环境中支持低功率高数据利率应用程序的有吸引力的候选人，包括无线宽带访问，机器式通信和物联网。该项目为学生提供了足够的机会进行研究和劳动力发展，并通过课程开发，研究传播和公共宣传进一步扩大了社会影响。需要对NOMA的频谱共享范式进行重新设计，以说明MMWave和Mamimo通道的独特角度选择性。为此，该项目解决了与光谱效率，能效和智力相关的研究挑战，重点关注（i）（i）在硬件约束和实际环境下的准确有效的频道传感，以及（ii）（ii）NOMA-MWAVE-MMWAVE-MAMMOMO系统的系统增强和实时设计的信号处理和机器学习的整体集成。提出的思想和方法结合了来自无线通信，信号处理，优化和机器学习的多学科研究概念和方法。预计这项研究工作将推进5G无线系统及其他地区的设计，但也将对未来智能无线应用程序的发展产生有益的影响。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智力优点评估来支持的，并具有更广泛的影响。