Efficient Design Approaches for MIMO/Cooperative Cognitive Radio Transceivers

当前频谱分配策略的刚性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，大多数许可频段要么未使用，要么利用率不到10%。为了解决频谱使用的限制，FCC鼓励使用机会性频谱共享，使未经许可的无线用户可以访问许可的频段。这背后的意图是为并发频谱使用创造无线设备的认知能力。在认知网络中，用户/无线电通过学习或人工智能动态适应其环境，目的是提高整体网络性能。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频谱传感/共享和系统识别等许多挑战。本提案旨在研究这些挑战，以提高无线认知网络的整体频谱效率。具体来说，我们将专注于高效和低复杂性的频谱传感和盲系统识别技术，重点是调制类型/大小，使用的天线数量和代码设计识别。拟议的研究计划还将涉及跨层环境下无线协议栈物理层参数和更高网络方面的联合设计研究。设计方法将基于由移动认知端智能架构支持的认知通信。随着认知网络在安全、医疗保健（例如，跟踪智障人士）和交通等领域的广泛应用，我们的研究结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界提供高效、快速的应用。

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.