Compression and Cooperation for Wideband Spectrum Sensing and Cognition

这项研究的目的是提高宽带频谱感知和认知的分辨率，降低成本，这是频谱共享认知网络中具有挑战性的核心问题。该方法寻求利用压缩采样和用户合作的好处来开发用于宽带网络的协作压缩感知框架。研究方向包括以负担得起的信号捕获成本进行压缩的宽带感知和以较低的网络开销实现可靠的协作感知。本研究将压缩和协作技术与宽带频谱感知的创新目标相结合。该项目寻求开发一套利用频谱稀疏性来减少超宽带和宽带无线电中的高速采样需求的压缩传感算法。研究了一种分布式联合协作和压缩方法，该方法利用空间分集来缓解无线信道衰落引起的隐藏终端问题。此外，该研究还考察了协作感知系统中的基本权衡，包括分集增益、压缩、感知时间和复杂度。该项目有可能导致能够提高无线网络的频谱利用效率和用户容量的技术，从而允许大量新的认知无线电收发机。这类设备包括用于监控和跟踪应用的无线传感器和射频识别(RFID)芯片组。正在研究的压缩感知和分散协作技术也有可能促进其他与感知相关的应用，如无线传感器网络。通过这个项目获得的研究经验有助于学生为21世纪的工程做好准备。

The objective of this research is to increase the resolution and reduce the cost of wideband spectrum sensing and cognition, which are challenging core issues in spectrum-sharing cognitive networks. The approach seeks to leverage the benefits of compressive sampling and user cooperation to develop a collaborative compressed sensing framework for wideband networks. Research thrusts include compressed wideband sensing at affordable signal acquisition costs and reliable collaborative sensing with low network overhead.This research combines compression and collaboration techniques with the goal of innovation in wideband spectrum sensing. The project seeks to develop a suite of compressed sensing algorithms that exploit spectral sparsity to reduce the need for high-speed sampling in ultra-wideband and wideband radios. A decentralized approach for joint cooperation and compression, which exploits spatial diversity to alleviate the hidden terminal problem caused by wireless channel fading, is investigated for ad hoc cognitive networks. Further, the research examines fundamental tradeoffs in a cooperative sensing system including diversity gain, compression, sensing time and complexity.This project has the potential to lead to techniques that can improve the spectrum utilization efficiency and user capacity of wireless networks, thus allowing a multitude of new cognitive radio transceivers. Such devices include wireless sensors and radio frequency identification (RFID) chipsets for monitoring and tracking applications. The compressed sensing and decentralized collaboration techniques being investigated also have the potential to contribute to other sensing-related applications, such as wireless sensor networks. Research experiences gained through this project help to prepare students for engineering in the 21st Century.