Analysis and design of wireless cognitive networks

在认知网络中，用户/无线电通过学习动态地适应他们的环境，以提高网络的整体性能。此外，在认知网络设计中，多输入多输出（MIMO）技术被认为是开发下一代认知无线系统时最有前途的技术之一。近年来，MIMO信号盲截获算法和技术受到了越来越多的关注，因为迄今为止开发的大多数盲识别算法都是基于单输入单输出（SISO）系统的。在MIMO认知系统中，信号截获过程中的基本步骤是盲识别所使用的天线的调制方案（类型和大小）和数量。 考虑到认知网络的智能能力，在实际实施之前，需要解决许多挑战，如频谱共享/感知和系统识别。该建议旨在研究这些挑战，以提高无线认知网络环境中的整体频谱效率。具体来说，我们将专注于高效和可能简单的实现动态频谱感知/共享技术和盲系统的识别，重点是调制类型/大小，使用的天线数量和代码设计。拟议的研究将涉及调查的物理层参数和更高的网络方面的无线协议栈在跨层上下文中的联合设计。

In cognitive networks, users/radios dynamically adapt to their environment through learning with the aim of improving the overall network performance. Furthermore, in cognitive network design, multiple-input multiple-output (MIMO) technology is considered to be one of the most promising technologies when developing the next generation of cognitive wireless systems. Recently, blind algorithms and techniques for MIMO signal interception have gained more attention as most blind identification algorithms developed so far are based on single-input single-output (SISO) systems. In MIMO cognitive systems among the essential steps in the signal interception process are the blind identification of, modulation schemes (type and size) and number of used antennas. Given the projected smart capabilities of cognitive networks, many challenges such as spectrum sharing/sensing and system identification need to be addressed before actual implementation takes place. This proposal aims at investigating these challenges in an effort to enhance the overall spectrum efficiency in a wireless cognitive network setting. Specifically we will focus on efficient and possibly simple implementations of dynamical spectrum sensing/sharing techniques and blind system's identification with emphasis on modulation type/size, number of used antennas, and code design. The proposed research will involve investigations for joint design of the physical layer parameters and higher networking aspects of the wireless protocol stack in a cross-layer context.