EAGER: Collaborative Research: Cross-Layer Modeling and Design of Energy-Aware Cognitive Radio Networks

EAGER：协作研究：能源感知认知无线电网络的跨层建模和设计

• 批准号: 1264486
• 负责人: Shuvra Shikhar Bhattacharyya
• 金额: $ 15万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-02-01 至 2015-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1264486&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Cross; Layer

Minimization of energy consumption is critical to developing green, sustainable technologies for cognitive radio terminals that can connect to networks that operate on different frequency bands with a variety of air interfaces. The intellectual merit of this project is a unified and coherent consideration of RF components, communication system algorithms, baseband computation platforms, and design tools, to greatly increase spectrum sharing efficiency. Dataflow methodologies are a promising candidate for the modeling, analysis and verification of cognitive radio systems. As dataflow models are abstract and platform independent, the same model can be used to generate implementations for very different devices from low-power sensor nodes to high-end mobile terminals. The key novelty is in the development of systematic methods for design, implementation, and integration of configurable RF chains, and in the development of dataflow methods for formal analysis and optimization of these new capabilities. The expected results are: (1) Energy consumption models and a design framework for computation, control and configuration of future radio devices, leveraging the investigators' existing experimental testbeds, (2) Configurable radio architectures for wide-scale cognitive access of noncontiguous RF spectrum, and (3) Design methodologies for flexible, energy-efficient cognitive wireless networks. The broader impact includes international collaboration through the WiFiUS program creating a holistic design for configurable frequency agile terminals. A novel interdisciplinary approach is enabled by the unique international team, which builds upon collaborations between experts at the Tampere University of Technology and University of Oulu in Finland, and Rice University and the University of Maryland in the US.

最大限度地减少能源消耗对于开发绿色、可持续的认知无线电终端技术至关重要，认知无线电终端可以连接到具有各种空中接口的不同频段上运行的网络。该项目的智能优点是对射频组件、通信系统算法、基带计算平台和设计工具进行了统一和一致的考虑，大大提高了频谱共享效率。数据流方法是认知无线电系统建模、分析和验证的一个很有前途的候选方法。由于数据流模型是抽象的且与平台无关，因此可以使用相同的模型为从低功率传感器节点到高端移动终端的非常不同的设备生成实现。关键的创新之处在于开发了设计、实现和集成可配置射频链的系统方法，以及开发了用于对这些新功能进行正式分析和优化的数据流方法。预期的结果是：(1)能量消耗模型和未来无线电设备的计算、控制和配置的设计框架，利用研究人员现有的实验试验台；(2)可配置的无线电架构，用于大规模认知接入非连续射频频谱；(3)灵活、节能的认知无线网络的设计方法。更广泛的影响包括通过WiFiUS计划进行国际合作，为可配置的频率捷变终端创建整体设计。独特的国际团队使一种新颖的跨学科方法成为可能，该团队建立在芬兰坦佩雷理工大学和奥卢大学以及美国莱斯大学和马里兰大学专家之间的合作基础上。