Collaborative Research: SWIFT: Intelligent Dynamic Spectrum Access (IDEA): An Efficient Learning Approach to Enhancing Spectrum Utilization and Coexistence

合作研究：SWIFT：智能动态频谱接入 (IDEA)：增强频谱利用和共存的有效学习方法

• 批准号: 2128596
• 负责人: Zhi Tian
• 金额: $ 30万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2128596&HistoricalAwards=false
• 关键词: Collaborative; Research; SWIFT; Intelligent; Dynamic

With the growing importance of wireless connectivity for social and economic interaction, there have been rising demands for greater spectrum use by both primary and secondary active radios, amidst the critical requirement of quiet spectrum for scientific exploration by receiver-only passive systems. To improve the overall spectrum utilization of the wireless ecosystem, this project develops a holistic Intelligent Dynamic spEctrum Access (IDEA) framework that can substantially enhance the spectrum utilization, energy-efficiency, and coexistence capability of spectrum sharing networks. In IDEA, enabling technical innovations across multiple disciplines are synergistically developed, including neuromorphic design of energy-efficient computing hardware at the device and circuit level, and artificial intelligence for spectrum sensing and dynamic access at the network level. The spectrum and interference management in IDEA conscientiously treats the coexistence constraints imposed by passive services, in support of scientific and societal returns from remote sensing investments. The outcomes of this research are expected to broadly impact next-generation wireless networks and Internet of Things applications with high traffic demands, such as autonomous driving, smart cities and remote sensing.The goal of this project is to develop an intelligent dynamic spectrum access framework with unprecedented spectrum utilization efficiency and agility to support spectrum coexistence. The developed IDEA network platform supports heterogeneous devices from both primary and secondary active radios as well as passive radios. Key technical innovations are developed across the network to substantially enhance system-level spectrum utilization and active-passive radio coexistence. Specifically, analog/mixed-signal spiking neural network (SNN)-based neuromorphic computing hardware is designed to provide on-board intelligence at ultra-low power for resource-constrained secondary active radios. Model-free deep reinforcement learning is integrated with wireless domain knowledge and the SNN platform to accelerate learning-based spectrum access and coexistence. Advanced spectrum monitoring techniques are developed to quickly detect and characterize various signal emitters in both active and passive services. Finally, software and hardware testbeds are developed for system level evaluation and tradeoff optimization. The IDEA framework not only empowers efficient spectrum access in highly dynamic wireless environments, but also facilitates holistic system design and optimization across devices and circuits, sensing and communications, and networking.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.

随着无线连接对社会和经济互动的重要性日益增加，对主要和次要有源无线电使用更多频谱的需求不断增加，同时仅接收器无源系统对安静频谱的科学探索提出了关键要求。为了提高无线生态系统的整体频谱利用率，该项目开发了一个整体智能动态频谱接入（IDEA）框架，可以大幅提高频谱共享网络的频谱利用率、能源效率和共存能力。在IDEA中，跨多个学科的技术创新得到协同发展，包括设备和电路层面的节能计算硬件的神经拟态设计，以及网络层面的频谱感知和动态访问的人工智能。 IDEA 中的频谱和干扰管理认真对待无源服务带来的共存限制，以支持遥感投资的科学和社会回报。该研究成果预计将广泛影响具有高流量需求的下一代无线网络和物联网应用，例如自动驾驶、智慧城市和遥感。该项目的目标是开发智能动态频谱访问框架具有前所未有的频谱利用效率和敏捷性来支持频谱共存。开发的 IDEA 网络平台支持主要和辅助有源无线电以及无源无线电的异构设备。整个网络开发了关键技术创新，以大幅提高系统级频谱利用率和主动-被动无线电共存。具体来说，基于模拟/混合信号尖峰神经网络 (SNN) 的神经拟态计算硬件旨在以超低功耗为资源受限的辅助有源无线电提供板载智能。无模型深度强化学习与无线领域知识和 SNN 平台相集成，以加速基于学习的频谱访问和共存。先进的频谱监测技术的开发可以快速检测和表征有源和无源服务中的各种信号发射器。最后，开发了软件和硬件测试平台，用于系统级评估和权衡优化。 IDEA框架不仅能够在高度动态的无线环境中实现高效的频谱接入，而且还有助于跨设备和电路、传感和通信以及网络的整体系统设计和优化。该奖项反映了NSF的法定使命，并通过评估被认为值得支持利用基金会的智力优势和更广泛的影响审查标准。

项目成果

Robust Distributed Swarm Learning for Intelligent IoT

智能物联网的鲁棒分布式群体学习

• DOI: 10.1109/icc45041.2023.10278708
• 发表时间: 2023-05
• 期刊: IEEE International Conference on Communications
• 作者: Fan, Xin;Wang, Yue;Huo, Yan;Tian, Zhi
• 通讯作者: Tian, Zhi

CB-DSL: Communication-efficient and Byzantine-robust Distributed Swarm Learning on Non-i.i.d. Data

CB-DSL：非独立同分布的通信高效且拜占庭鲁棒的分布式群体学习

• DOI: 10.1109/tccn.2023.3312345
• 发表时间: 2023-09
• 期刊: IEEE Transactions on Cognitive Communications and Networking
• 影响因子: 8.6
• 作者: Fan, Xin;Wang, Yue;Huo, Yan;Tian, Zhi
• 通讯作者: Tian, Zhi

QC-ODKLA: Quantized and Communication-Censored Online Decentralized Kernel Learning via Linearized ADMM

QC-ODKLA：通过线性化 ADMM 进行量化和通信审查的在线去中心化内核学习

• DOI: 10.1109/tnnls.2023.3310499
• 发表时间: 2023-09
• 期刊: IEEE Transactions on Neural Networks and Learning Systems
• 影响因子: 10.4
• 作者: Xu, Ping;Wang, Yue;Chen, Xiang;Tian, Zhi
• 通讯作者: Tian, Zhi

Efficient Distributed Swarm Learning for Edge Computing

用于边缘计算的高效分布式群体学习

• DOI: 10.1109/icc45041.2023.10279508
• 发表时间: 2023-05
• 期刊: IEEE International Conference on Communications
• 作者: Fan, Xin;Wang, Yue;Huo, Yan;Tian, Zhi
• 通讯作者: Tian, Zhi