Analysis and design of wireless cognitive networks

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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.