CIF: Medium: Collaborative Research: Low-Resolution Sampling with Generalized Thresholds

CIF：中：协作研究：具有广义阈值的低分辨率采样

• 批准号: 1704240
• 负责人: Jian Li
• 金额: $ 40万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-01 至 2021-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1704240&HistoricalAwards=false
• 关键词: CIF; Medium; Collaborative; Research; Low

CIF: Medium: Collaborative Research:Low-Resolution Sampling with Generalized ThresholdsJian Li, Lee Swindlehurst, and Mojtaba Soltanalian AbstractQuantization of signals of interest is a necessary first step in digital signal processing applications. When signals across a wide frequency band are of interest, a fundamental tradeoff between sampling rate, amplitude quantization precision, cost, and power consumption is encountered. The investigators study low resolution sampling techniques with general thresholds, which are affordable, technically feasible, easy to apply, energy-efficient, and consistent with technological trends. The enormous gains in capacity and spectral efficiency, for example, that could be provided by a successful millimeter wave (mm-wave) massive multiple-input multiple output implementation could have a revolutionary effect on the performance of wireless systems nearly everywhere we use them: at home, at work, at school, commuting via public transportation or by plane, shopping, at restaurants, recreational venues, sporting events, and so on. Besides consumer applications, there are many military- and security-related scenarios where our systems could be used.This project involves advancing fundamental knowledge in developing dynamic energy-efficient and cost-effective sampling techniques and applies engineering principles to address the critical needs of several important and related applications. Specifically, this project involves addressing significant open questions, including deterministic identifiability, performance bounds, and impact of thresholding pattern on spectrum sensing and array processing, radio frequency interference mitigation, and mm-wave communications to gain fundamental insights into the novel paradigm of low resolution sampling with general thresholds, devising novel signal processing algorithms, including effective and efficient sparse signal recovery techniques and parametric maximum likelihood methods for enhanced performance, and evaluating and demonstrating the performance using measured data. This project also involves preparing students for engineering in the 21st century through the incorporation of practical design and problem-solving techniques into both the education curriculum.

CIF：媒介：协作研究：与广义阈值LI，Lee Swindlehurst和Mojtaba Soltanalian抽象的兴趣信号的低分辨率抽样是数字信号处理应用中的必要第一步。当宽频带的信号引起人们的兴趣时，遇到了采样率，幅度量化精度，成本和功耗之间的基本权衡。研究人员研究了具有一般阈值的低分辨率抽样技术，这些采样技术是负担得起的，技术上可行的，易于应用，能效，能效，并且与技术趋势一致。 The enormous gains in capacity and spectral efficiency, for example, that could be provided by a successful millimeter wave (mm-wave) massive multiple-input multiple output implementation could have a revolutionary effect on the performance of wireless systems nearly everywhere we use them: at home, at work, at school, commuting via public transportation or by plane, shopping, at restaurants, recreational venues, sporting events, and so on.除了消费者的应用外，还可以使用许多与军事和安全有关的方案。该项目涉及在开发动态的节能和成本效益的采样技术方面促进基本知识，并应用工程原理来满足几种重要和相关应用的关键需求。 Specifically, this project involves addressing significant open questions, including deterministic identifiability, performance bounds, and impact of thresholding pattern on spectrum sensing and array processing, radio frequency interference mitigation, and mm-wave communications to gain fundamental insights into the novel paradigm of low resolution sampling with general thresholds, devising novel signal processing algorithms, including effective and efficient sparse signal recovery techniques and parametric maximum可提高性能的可能性方法，并使用测量的数据评估和证明性能。该项目还涉及通过将实践设计和解决问题的技术纳入两种教育课程，使学生在21世纪为工程做准备。

项目成果

One-Bit compressive sampling with time-varying thresholds for multiple sinusoids

• DOI: 10.1109/camsap.2017.8313172
• 发表时间: 2017-12
• 期刊: 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
• 作者: Christopher Gianelli;Luzhou Xu;Jian Li;P. Stoica

Sinusoidal Parameter Estimation From Signed Measurements Via Majorization–Minimization Based RELAX

• DOI: 10.1109/tsp.2019.2899804
• 发表时间: 2019-02
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Jiaying Ren;Tianyi Zhang;Jian Li;P. Stoica

Sinusoidal Parameter Estimation from Signed Measurements Obtained via Time-Varying Thresholds

• DOI: 10.1109/acssc.2018.8645123
• 发表时间: 2018-10
• 期刊: 2018 52nd Asilomar Conference on Signals, Systems, and Computers
• 作者: Jiaying Ren;Tianyi Zhang;Jun Yu Li;P. Stoica

RFI Mitigation for One-Bit UWB Radar Systems

• DOI: 10.1109/ieeeconf44664.2019.9048982
• 发表时间: 2019-11
• 期刊: 2019 53rd Asilomar Conference on Signals, Systems, and Computers
• 作者: Tianyi Zhang;Jiaying Ren;Christopher Gianelli;Jian Li

RFI Mitigation for UWB Radar Via Hyperparameter-Free Sparse SPICE Methods

• DOI: 10.1109/tgrs.2018.2880758
• 发表时间: 2019-06
• 期刊: IEEE Transactions on Geoscience and Remote Sensing
• 影响因子: 8.2
• 作者: Jiaying Ren;Tianyi Zhang;Jian Li;L. Nguyen;P. Stoica