Efficient Design Approaches for MIMO/Cooperative Cognitive Radio Transceivers

当前频谱分配政策的僵化结构给快速增长的无线用户带来了瓶颈。另一方面，联邦通信委员会(FCC)的测量显示，大多数获得许可的频段要么未被使用，要么被利用的时间不到10%。为了解决频谱使用的限制，FCC推动了机会主义频谱共享的使用，使未经许可的无线用户可以访问许可的频段。这背后的目的是创建无线设备的认知能力，以满足并发频谱使用。在认知网络中，用户/无线电通过学习或人工智能来动态适应环境，目的是提高网络的整体性能。考虑到认知网络预计的智能能力，在实际实施之前，需要解决许多挑战，如频谱感知/共享和系统识别。该提案旨在研究这些挑战，以努力提高无线认知网络的整体频谱效率。具体地说，我们将重点介绍高效和低复杂度的频谱感知和盲系统识别技术，重点是调制类型/大小、使用的天线数量和码设计识别。拟议的研究计划还将涉及在跨层环境下对无线协议栈的物理层参数和更高联网方面的联合设计进行调查。设计方法将基于移动认知端的智能建筑支持的认知通信。随着认知网络的广泛应用，从安全、医疗保健(例如，跟踪智障人士)到交通运输，我们的研究成果将使研究机构和公司受益，从而为工业界和学术界的研究社区提供这些应用的高效和快速实施。

The rigid structure of the current spectrum allocation policies creates a bottleneck for rapidly growing wireless users. On the other hand, the Federal Communication Commission (FCC) measurements reveal that most of the licensed frequency bands are either unused or utilized less than 10% of the time. In order to address the limitations on spectrum usage, the FCC has motivated the use of opportunistic spectrum sharing to make licensed frequency bands accessible for unlicensed wireless users. The intention behind this is to create cognitive capability of wireless devices for concurrent spectrum usage. In cognitive networks, users/radios dynamically adapt to their environment through learning or artificial intelligence with the aim of improving the overall network performance. Given the projected smart capabilities of cognitive networks, many challenges such as spectrum sensing/sharing and system identification need to be addressed before actual implementation can take place. This proposal aims at investigating these challenges in an effort to enhance the overall spectrum efficiency in wireless cognitive networks. Specifically we will focus on efficient and low complexity spectrum sensing and blind system identification techniques with emphasis on modulation type/size, number of used antennas, and code design identification. The proposed research program will also involve investigations for joint design of the physical layer parameters and higher networking aspects of the wireless protocol stack in a cross-layer context. The design approach will be based on cognitive communications supported by intelligent architectures at the mobile cognitive end. With the wide applications of cognitive networks ranging from safety, healthcare (e.g., tracking of people with mental disabilities) and transportation, our findings will benefit both research institutions and companies, and hence provide the research communities in both industry and academia with efficient and fast implementations of these applications.