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: 2034530
• 负责人: Chunxiao Chigan
• 金额: $ 24.06万
• 依托单位: University of Massachusetts Lowell
• 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=2034530&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，无线频谱被各种无线技术共享，需求越来越高，无线频谱变得越来越拥挤。为了满足呈指数级增长的频谱需求，迫切需要新的无线技术来实现动态、高效的频谱共享以及与其他网络的共存。该项目通过开发一个框架来解决频谱稀缺问题，该框架可实现全双工传输能力、准确有效的可用频谱检测以及不同无线设备和网络之间的高效频谱共享。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，全双工传输会产生强烈的自干扰和对其他设备的额外干扰，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。该项目将通过充分发挥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。该项目预计将通过增强的无线服务产生重大社会影响。该项目将通过智能全双工 CR 网络 (IFD-CRN) 与分布式软件定义网络 (SDN) 基础设施的协同框架，全面开发支持技术。边缘，用于结合移动边缘计算的普遍异构无线网络。结合智能增强型网络功能虚拟化（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 在无线最终用户附近执行 NFV，并包含物理层，使其适合具有严格延迟要求的超密集、小型蜂窝异构无线网络。 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的情况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造成的独特用户间干扰。该奖项反映了 NSF 的法定使命，并被认为值得支持通过使用基金会的智力优点和更广泛的影响审查标准进行评估。