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

Li jian, Lee Swindlehurst， and Mojtaba Soltanalian[摘要]对感兴趣的信号进行量化是数字信号处理应用中必要的第一步。当对宽频带上的信号感兴趣时，会遇到采样率、幅度量化精度、成本和功耗之间的基本权衡。研究者研究了具有一般阈值的低分辨率采样技术，这些技术价格合理，技术可行，易于应用，节能且符合技术趋势。例如，成功的毫米波（mm-wave）大规模多输入多输出实现可以提供容量和频谱效率方面的巨大收益，这可能会对几乎所有我们使用它们的无线系统的性能产生革命性的影响：在家里，在工作中，在学校，通过公共交通或飞机通勤，购物，在餐馆，娱乐场所，体育赛事等。除了消费者应用之外，还有许多与军事和安全相关的场景可以使用我们的系统。该项目涉及发展动态节能和成本效益采样技术的基础知识，并应用工程原理来解决几个重要和相关应用的关键需求。具体而言，该项目涉及解决重要的开放问题，包括确定性可识别性、性能界限以及阈值模式对频谱传感和阵列处理、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参数最大似然方法来增强性能，并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结合到教育课程中，为21世纪的工程学生做好准备。

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.