CIF: Small: Robust Signal Recovery and Grant-Free Access for Massive IoT Connectivity

CIF：小型：强大的信号恢复和无授权访问大规模物联网连接

• 批准号: 2009001
• 负责人: Zhi Ding
• 金额: $ 43.1万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2009001&HistoricalAwards=false
• 关键词: CIF; Small; Robust; Signal; Recovery

Recent advances in information technologies, computers, and microelectronics have led to transformative innovations and broad deployment of smart devices with data computation and communication capabilities. Internet of things (IoT) products and services are playing increasingly important roles in many fields such as smart city, e-health, agriculture, safety, security, and environmental protection. In particular, low-power massive IoT applications already account for over 60% of the IoT market which continues to expand at a relentless pace. Low-power wireless IoT devices are primarily deployed for sensing and data collection with uplink dominated traffic. Given the massive number of such devices, traditional channel access based on coordinated scheduling between base-station and the multitude of end-user devices consumes too much bandwidth and device energy. Uncoordinated uplink access can overcome both obstacles but at the risk of multi-device signal collisions. This research project develops novel technologies for reliable and efficient reception of simultaneous multi-device wireless transmissions in networks that support massive number of low power IoT terminals. This work contributes to vital technological advancement that can significantly impact the current and future applications of wireless IoT services. The research outcomes shall contribute substantially to the theoretical foundation of signal processing and optimization, as well as to the design of network protocols to support massive connectivity in practical 5G and Beyond wireless systems.Specifically, the project activities focus on the design, analysis, and optimization of advanced wireless network receivers to effectively decode and recover data packets that are often in collision when multiple devices spontaneously transmit over their shared wireless channel spectrum. Effective recovery of data packets under collision improves both spectral efficiency and energy efficiency for a large population of low power devices. Specifically, the researchers shall investigate novel solutions for simultaneous signal recovery from multiple device transmissions in both local area and wide area network environments. Subject to unknown channel distortions and mutual interference, wireless receivers must recover co-channel user packets from simultaneous uplink transmissions via blind demixing. The research team will tackle the challenging and difficult problem of blind demixing by advancing the theory, algorithm, and hardware for low-rank and sparse matrix completion. The researchers shall develop novel algorithms that are faster and more effective with desired global convergence.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.

信息技术，计算机和微电子学的最新进展导致了具有数据计算和通信功能的智能设备的变革性创新和广泛部署。物联网（物联网）产品和服务在许多领域中扮演着越来越重要的角色，例如智慧城市，电子卫生，农业，安全，安全和环境保护。特别是，低功率大规模的物联网应用已经占物联网市场的60％以上，这继续以不懈的速度扩展。低功率无线IoT设备主要部署，用于传感和数据收集，并以上行链路为主导的流量。考虑到大量此类设备，基于基础站和大量最终用户设备之间的协调调度的传统频道访问会消耗过多的带宽和设备能量。不协调的上行链路访问可以克服这两个障碍，但有多设备信号碰撞的风险。该研究项目开发了新技术，可在网络中可靠，有效地接受同时多设备的无线传输，以支持大量的低功率IoT终端。这项工作有助于重要的技术进步，这可能会严重影响无线物联网服务的当前和未来应用。研究结果应为信号处理和优化的理论基础以及网络协议的设计做出基本贡献，以支持实用的5G和无线系统中的大量连通性。特别是，项目活动集中于设计，分析和优化高级无线网络接收器的高级网络接收器，以有效地传播和恢复多个设备时，这些数据包在多个设备上是多个设备的频率分享的。在碰撞下，数据包的有效恢复可提高大量低功率设备人群的光谱效率和能源效率。具体而言，研究人员应研究新的解决方案，以从当地和广域网络环境中的多个设备传输中同时恢复信号。在未知的通道扭曲和相互干扰的情况下，无线接收器必须通过盲链断从同时上行链路传输中恢复共沟通用户数据包。研究团队将通过推进理论，算法和硬件来解决低级别和稀疏矩阵完成的理论，算法和硬件来解决盲目混合的挑战和困难问题。研究人员应开发出新的算法，这些算法更快，更有效地通过所需的全球融合。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知识分子优点和更广泛的影响评估标准通过评估来支持的。

项目成果

Over-the-Air Multisensor Collaboration for Resource Efficient Joint Detection

• DOI: 10.1109/tsp.2023.3310895
• 发表时间: 2024
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Carlos Feres;B. Levy;Zhi Ding

Over-the-Air Collaborative Learning in Joint Decision Making

• DOI: 10.1109/globecom48099.2022.10001286
• 发表时间: 2022-12
• 期刊: GLOBECOM 2022 - 2022 IEEE Global Communications Conference
• 作者: Carlos Feres;B. Levy;Z. Ding

Wirtinger Flow for Nonconvex Blind Demixing With Optimal Step Size

• DOI: 10.1109/lwc.2022.3203313
• 发表时间: 2022-12
• 期刊: IEEE Wireless Communications Letters
• 影响因子: 6.3
• 作者: Chih-Ho Hsu;Carlos Feres;Zhi Ding

Wirtinger Flow Meets Constant Modulus Algorithm: Revisiting Signal Recovery for Grant-Free Access

Wirtinger Flow 与恒模算法的结合：重新审视信号恢复以实现无授权访问

• DOI: 10.1109/tsp.2021.3103038
• 发表时间: 2021
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Feres, Carlos;Ding, Zhi
• 通讯作者: Ding, Zhi

Anchor Selection for Topology Inference and Routing in Wireless Sensor Networks

无线传感器网络中拓扑推理和路由的锚点选择

• DOI: 10.23919/jcin.2020.9200895
• 发表时间: 2020
• 期刊: Journal of communications and information networks
• 作者: Bouchoucha, Taha;Ding, Zhi
• 通讯作者: Ding, Zhi