CAREER: Signal Processing for Software Radio Wireless Systems

职业：软件无线电无线系统的信号处理

• 批准号: 9703074
• 负责人: Hui Liu
• 金额: $ 21万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-01 至 2001-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9703074&HistoricalAwards=false
• 关键词: CAREER; Signal; Processing; Software; Radio

The steadily decreasing cost of wireless mobile communications and its growing deployment have resulted in its increased popularity. The trend towards software radio base-station, one such instance, opens up a new avenue of possibilities for innovative baseband signal processing technologies in wireless communications which in turn, introduces new challenges in academic research due to the complex and interdisciplinary nature of practical systems. The development of future high performance wireless systems requires the integration of technological advances that span a broad area of communications and signal processing subjects. This research involves the development and design of a wireless communication laboratory, and the implementation of a high performance antenna array CMDA wireless network. Focussing on critical issues like complexity and robustness, the investigator seeks theoretically sound and algorithmically feasible solutions to the multipath fading problem unique to wireless mobile systems. Key to the new system is a self-adaptive CDMA receiver that is capable of tracking environmental variations without the use of training sequences. The novel methodologies developed here take advantage of both spatial and temporal diversities in a unified fashion, thereby allowing optimum performance to be realized without introducing undue complexity.

无线移动通信成本的稳步下降和部署的不断增加使其越来越受欢迎。例如，软件无线电基站的发展趋势为无线通信中创新的基带信号处理技术开辟了一条新的可能性途径，这反过来又由于实际系统的复杂性和跨学科性质，给学术研究带来了新的挑战。未来高性能无线系统的发展需要跨越通信和信号处理学科的广泛领域的技术进步的集成。本研究涉及无线通信实验室的开发与设计，以及高性能天线阵列CMDA无线网络的实现。专注于关键问题，如复杂性和鲁棒性，研究者寻求理论健全和算法可行的解决方案，多径衰落问题独特的无线移动系统。新系统的关键是一个自适应CDMA接收器，它能够在不使用训练序列的情况下跟踪环境变化。这里开发的新方法以统一的方式利用了空间和时间的多样性，从而在不引入过度复杂性的情况下实现了最佳性能。