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; 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.

在现代社会中，无线设备和服务越来越普遍。同时，无线频谱通过需求更高的各种无线技术共享，并且变得越来越拥挤。为了满足指数增长的频谱需求，对新的无线技术有一个迫切的需求，这些技术可以使频谱和与其他网络共享动态和有效共享。该项目通过开发一个框架来解决频谱稀缺性问题，以实现全双工传输能力，对可用频谱的准确检测以及在各种无线设备和网络之间的有效频谱共享。通过全双工传输，无线设备可以同时传输和接收信息，从理论上讲，通过传统的半双链设备可以实现的容量增加一倍。但是，全双工传播会引起强大的自我干扰和对其他设备的额外干扰，这限制了其潜在的好处。该项目旨在通过在网络中应用机器学习和智能使用计算资源来克服这些挑战。该项目将通过充分意识到全面传输和动态频谱共享的潜力来推动无线网络领域。预计该项目将通过增强的无线服务产生重大的社会影响。该项目将通过具有分布式软件定义的网络（SDN）基础设施的智能全双制CR网络（IFD-CRN）的协同框架来整体开发能够开发能够的技术，以实现优势的无线网络网络，以实现Edge的边缘。再加上智能增强网络功能虚拟化（NFV）体系结构，IFD-CRN将采用先进的机器学习算法来实质上提高频谱效率，数据速率和能源效率，并实现有效的资源利用，并通过基础结构灵活性，可再生能力和可伸缩性和可伸缩性。 IFD-CRN在无线最终用户的接近性方面执行NFV，并且包括物理层，使其适合于超密集的小细胞异质无线网络，具有紧密的延迟要求。 IFD-CRN采用在线频谱预测中采用环状特征检测，在存在由全双工传输引起的强大自我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能够估算网络状态信息和用户特征，以减轻密集网络中全双工引起的独特的用户干扰。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审查审查的评估来通过评估来获得支持的。