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

随着多功能设备和数据消费服务的激增，对频谱效率的追求导致了三种颠覆性技术的结合，即毫米波（mmWave）通信、大规模多输入多输出（maMIMO）和非正交多址（NOMA）。为了充分了解组合NOMA-mmWave-maMIMO技术在未来无线系统中可能发挥的利基作用，该项目开发了稀疏信号处理和先进的机器学习技术，以实现高效的传感和系统优化。该项目的成果预计将使NOMA-mmWave-maMIMO成为一个有吸引力的候选者，以支持密集城市环境中的低功耗高数据速率应用，包括无线宽带接入，机器类型通信和物联网。该项目为学生提供了充分的研究和劳动力发展机会，并通过课程开发，研究传播和公众宣传进一步扩大社会影响。NOMA的频谱共享范例需要重新设计，以考虑毫米波和maMIMO信道的独特角度选择性。为此，该项目解决了与频谱效率，能源效率和智能相关的研究挑战，重点是（i）在硬件限制和实际环境下准确有效的信道感知，以及（ii）信号处理和机器学习的整体集成，用于NOMA-mmWave-maMIMO系统的系统增强和实时设计优化。提出的思想和方法联合收割机多学科的研究概念和方法，从无线通信，信号处理，优化和机器学习。该研究成果有望推动5G无线系统及以后的设计，并对未来智能无线应用的开发产生有益影响。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响评审标准进行评估，被认为值得支持。

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.