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、计算机和微电子技术的最新进展导致了具有数据计算和通信能力的智能设备的变革性创新和广泛部署。物联网产品和服务在智慧城市、电子健康、农业、安全、安保、环保等诸多领域发挥着越来越重要的作用。特别是，低功耗大规模物联网应用已经占到物联网市场的60%以上，并继续以无情的速度扩张。低功耗无线物联网设备主要用于上行主导流量的传感和数据采集。在这种设备数量庞大的情况下，基于基站和众多终端用户设备之间协调调度的传统信道接入消耗了过多的带宽和设备能量。非协调上行接入可以克服这两个障碍，但存在多设备信号冲突的风险。该研究项目开发了支持大量低功耗物联网终端的网络中可靠有效地同时接收多设备无线传输的新技术。这项工作有助于重要的技术进步，可以显著影响无线物联网服务的当前和未来应用。研究成果将为信号处理和优化的理论基础，以及支持实际5G和超越无线系统中大规模连接的网络协议设计做出重大贡献。具体而言，该项目活动侧重于设计、分析和优化先进的无线网络接收器，以有效地解码和恢复数据包，当多个设备在其共享无线信道频谱上自发传输时，数据包经常发生碰撞。对于大量低功耗设备来说，有效地恢复碰撞下的数据包可以提高频谱效率和能量效率。具体而言，研究人员将研究在局域网和广域网环境中从多个设备传输同时恢复信号的新解决方案。由于未知信道失真和相互干扰，无线接收机必须通过盲解混的方法从同步上行传输中恢复同信道用户数据包。研究小组将通过推进低秩和稀疏矩阵补全的理论、算法和硬件来解决具有挑战性和困难的盲去混问题。研究人员将开发出更快、更有效的新算法，并实现理想的全局收敛。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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