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：Medium：Collaborative Research：使用广义阈值的低分辨率采样Li Jian Li，Lee Swinlehurst和Mojtaba Soltanian摘要感兴趣的信号的量化是数字信号处理应用中必要的第一步。当感兴趣的是跨宽频带的信号时，需要在采样率、幅度量化精度、成本和功耗之间进行基本的权衡。研究人员研究具有一般阈值的低分辨率采样技术，这些技术负担得起、技术上可行、易于应用、能源效率高，并符合技术趋势。例如，成功的毫米波(毫米波)大规模多输入多输出实施可以在容量和频谱效率方面提供巨大的收益，这可能会对我们几乎在任何地方使用无线系统的性能产生革命性的影响：在家里、在办公室、在学校、通过公共交通或乘飞机通勤、购物、在餐馆、娱乐场所、体育赛事等。除了消费类应用，还有许多与军事和安全相关的场景可以使用我们的系统。该项目涉及提高开发动态节能和成本效益采样技术的基础知识，并应用工程原理来满足几个重要和相关应用的关键需求。具体地说，该项目涉及解决重要的开放问题，包括确定性可识别性、性能界限和阈值模式对频谱感知和阵列处理、射频干扰缓解和毫米波通信的影响，以获得对具有一般阈值的低分辨率采样新范例的基本见解，设计新的信号处理算法，包括有效和高效的稀疏信号恢复技术和用于增强性能的参数最大似然方法，以及使用测量数据评估和演示性能。该项目还包括通过将实用设计和解决问题的技术纳入教育课程，使学生为21世纪的工程做好准备。

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