CCSS: Collaborative Research: Intelligent Full-Duplex Cognitive Radio Networks for Pervasive Heterogeneous Wireless Networking

CCSS：协作研究：用于普遍异构无线网络的智能全双工认知无线电网络

• 批准号: 2034616
• 负责人: Brian Mark
• 金额: $ 24.14万
• 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: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2034616&HistoricalAwards=false
• 关键词: CCSS; Collaborative; Research; Intelligent; Full

Wireless devices and services are becoming increasingly pervasive in modern society. Meanwhile, the wireless spectrum is being shared by diverse wireless technologies with higher demands and is becoming more and more crowded. To meet the exponentially growing spectrum demands, there is a critical need for new wireless technologies that enable dynamic and efficient sharing of the spectrum and coexistence with other networks. This project addresses the issue of spectrum scarcity by developing a framework for achieving full-duplex transmission capability, accurate and efficient detection of available spectrum, and efficient spectrum sharing among diverse wireless devices and networks. With full-duplex transmission, a wireless device can transmit and receive information simultaneously, theoretically doubling the capacity achievable by conventional half-duplex devices. However, full-duplex transmission incurs both strong self-interference and additional interference to other devices, which limits its potential benefits. The project aims to overcome these challenges by applying machine learning and intelligent use of computational resources in the network. The project will advance the field of wireless networking by fully realizing the potential of full-duplex transmission and dynamic spectrum sharing. The project is expected to have a significant societal impact through enhanced wireless services.This project will holistically develop enabling technologies, through a synergistic framework of intelligent full-duplex CR networks (IFD-CRNs) with distributed software defined network (SDN) infrastructure at the edge, for pervasive heterogeneous wireless networking incorporating mobile edge computing. Coupled with an intelligence-enhanced network function virtualization (NFV) architecture, an IFD-CRN will employ advanced machine learning algorithms to substantially improve spectrum efficiency, data rates, and energy efficiency, and achieve efficient resource utilization with infrastructural flexibility, evolvability, and scalability. An IFD-CRN performs NFV in the proximity of wireless end users and is inclusive of the physical layer, making it suitable for hyper-dense, small cell heterogeneous wireless networks with tight latency requirements. IFD-CRN employs cyclic feature detection with online spectrum prediction to perform fast spectrum detection in the presence of strong self-interference caused by full-duplex transmission. A learning-based mechanism will enable IFD-CRNs to estimate the network state information and user characteristics to mitigate the unique inter-user interference caused by full-duplex in a dense network.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.

无线设备和服务在现代社会中变得越来越普遍。与此同时，无线频谱正被具有更高需求的各种无线技术共享，并且变得越来越拥挤。为了满足呈指数增长的频谱需求，迫切需要能够实现频谱的动态和有效共享以及与其他网络共存的新无线技术。该项目通过开发一个框架来解决频谱稀缺的问题，该框架用于实现全双工传输能力，准确有效地检测可用频谱，以及在各种无线设备和网络之间进行有效的频谱共享。通过全双工传输，无线设备可以同时发送和接收信息，理论上是传统半双工设备可实现的容量的两倍。然而，全双工传输既会产生强烈的自干扰，也会对其他设备产生额外的干扰，这限制了它的潜在优势。该项目旨在通过应用机器学习和智能使用网络中的计算资源来克服这些挑战。该项目将通过充分实现全双工传输和动态频谱共享的潜力来推动无线网络领域的发展。该项目将通过智能全双工CR网络（IFD-CRN）与边缘分布式软件定义网络（SDN）基础设施的协同框架，为融合移动的边缘计算的普适异构无线网络全面开发使能技术。结合智能增强的网络功能虚拟化（NFV）架构，IFD-CRN将采用先进的机器学习算法，大幅提高频谱效率、数据速率和能源效率，并通过基础设施的灵活性、可演进性和可扩展性实现高效的资源利用。IFD-CRN在无线终端用户附近执行NFV，并且包括物理层，使其适用于具有严格延迟要求的超密集、小小区异构无线网络。IFD-CRN采用循环特征检测和在线频谱预测，在全双工传输引起的强自干扰存在下执行快速频谱检测。基于学习的机制将使IFD-CRN能够估计网络状态信息和用户特征，以减轻密集网络中全双工所造成的独特用户间干扰。该奖项反映了NSF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

Multiband Spectrum Sensing with Non-exponential Channel Occupancy Times

• DOI: 10.1109/icc42927.2021.9500620
• 发表时间: 2021-06
• 期刊: ICC 2021 - IEEE International Conference on Communications
• 作者: Hanke Cheng;B. L. Mark;Y. Ephraim;Chun-Hung Chen

Cognitive Overlay for Inter-system Cellular Dynamic Spectrum Sharing in the Downlink

下行链路中系统间蜂窝动态频谱共享的认知叠加

• 发表时间: 2021
• 期刊: IEEE International Conference on Communications workshops
• 作者: Everett, Jared S.;Mark, Brian L.
• 通讯作者: Mark, Brian L.