Multidimensional-cognitive and statistical signal processing

对宽带频谱不断增长的需求突显了对能够尽可能有效地利用稀缺频谱资源的智能技术的迫切需求。在这项研究计划中，我们将发明多维统计信号处理算法，用于认知无线通信网络、雷达网络、医学成像系统和语音通信系统。这些子系统与环境交互并监视环境，协作并改变它们自己的参数和行为，以提高它们的性能。例如，他们扫描和检测信号空闲信道的可用性，并机会主义地使用它们。智能和实用的算法将被发明用于诸如多媒体和增强型医学成像设备的应用中。为此，研究了环境监测、信道识别、频谱信息获取、分布式用户之间的协作和信息共享以及竞争性分布式资源分配/管理等关键问题。

The ever-increasing demand for broadband spectrum has highlighted the critical need for smart technologies that can make use of scarce spectral resources as efficiently as possible. In this research program, we will invent multidimensional statistical-signal-processing algorithms for applications in cognitive wireless communication networks, radar networks, medical imaging systems and voice communication systems. The subsystems interact with and monitor the environment, collaborating and altering their own parameters and behavior in order to enhance their performance. For example, they scan and detect the availability of signal free channels, and opportunistically use them. Intelligent and practical algorithms will be invented for applications such as in multimedia and in enhanced medical imaging devices. To this end, many critical problems are studied including monitoring of the environment, channel identification, spectral information acquisition, collaboration, and information sharing among distributed users, and competitive distributed resource allocation/management.