SBIR Phase I: Adaptive/Cognitive Software Radio Architecture for Gbps+ Wireless Networking

SBIR 第一阶段：用于 Gbps 无线网络的自适应/认知软件无线电架构

• 批准号: 0441240
• 负责人: Raghu Rao
• 金额: $ 9.99万
• 依托单位: Silvus Technologies, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-01-01 至 2005-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0441240&HistoricalAwards=false
• 关键词: SBIR; Phase; Adaptive; Cognitive; Software

This Small Business Innovation Research (SBIR) Phase I project approach to address the next challenge in wireless networking: to increase overall network throughput rates well in excess of a Gbps utilizing broadband links. This is an order of magnitude increase relative to today's systems. Achieving such high rates requires the nodes to incorporate learning and interference mitigation techniques, an optimum co-design of the MAC and PHY layers, and extensive experimentation. The key contribution of this work will be to identify a highly agile cognitive radio platform that can meet the demands of Gbps+ networking. Such a radio platform must (a) accurately sniff, identify, and characterize both friendly (inter network) or foreign (intra network) interference; (b) be quickly reconfigurable to morph itself into the optimal radio for a given set of environmental and user specified parameters; (c) learn interference conditions and leverage the knowledge towards improved network and MAC layer protocols. Development of such a platform requires a departure from traditional approaches and hinges on successful integration of state of the art signal processing and communication algorithms with the SDR framework. Additionally, comprehensive RF/Baseband co-design and optimization is needed, plus efficient learning and cataloguing techniques that are closely coupled to and under direct control of the network layer.Although home data networking has pretty much cut the umbilical cord that tethered all computers and peripherals, that umbilical cord has not been fully severed in the enterprise due to the higher demand on aggregate network throughput. Moreover, the desire to broadcast video inside the home is also being hampered by the capabilities and robustness of today's wireless LAN solutions. This work addresses these issues by leapfrogging next generation WLAN activities and aggressively pursuing a high throughput highly agile physical layer co-designed with an efficient MAC. The work will help demonstrate the limits of WLAN capabilities and will help drive current and future standards activities in this domain. Moreover, it will help demonstrate the tremendous throughput improvements that are achievable when each node actively senses, learns, and ultimately adapts to its channel and environmental conditions.

这个小型企业创新研究 (SBIR) 第一阶段项目方法旨在解决无线网络中的下一个挑战：利用宽带链路将整体网络吞吐率提高到超过 Gbps。相对于当今的系统，这是一个数量级的增长。要实现如此高的速率，节点需要结合学习和干扰抑制技术、MAC 和 PHY 层的最佳协同设计以及广泛的实验。这项工作的主要贡献是确定一个能够满足 Gbps+ 网络需求的高度敏捷的认知无线电平台。这样的无线电平台必须 (a) 准确地嗅探、识别和表征友好（网络间）或外部（网络内）干扰； (b) 可快速重新配置，将自身转变为针对给定环境和用户指定参数的最佳无线电； (c) 了解干扰条件并利用这些知识来改进网络和 MAC 层协议。此类平台的开发需要脱离传统方法，并取决于最先进的信号处理和通信算法与 SDR 框架的成功集成。此外，还需要全面的射频/基带协同设计和优化，以及与网络层紧密耦合并直接控制的高效学习和编目技术。尽管家庭数据网络几乎已经切断了束缚所有计算机和网络的脐带。由于对网络总吞吐量的更高要求，企业中的脐带尚未完全切断。此外，当今无线 LAN 解决方案的功能和稳健性也阻碍了在家中播放视频的愿望。这项工作通过跨越下一代 WLAN 活动并积极追求与高效 MAC 共同设计的高吞吐量、高度敏捷的物理层来解决这些问题。这项工作将有助于展示 WLAN 功能的局限性，并将有助于推动该领域当前和未来的标准活动。此外，它将有助于证明当每个节点主动感知、学习并最终适应其信道和环境条件时，可以实现巨大的吞吐量改进。