الفهرس | Only 14 pages are availabe for public view |
Abstract Providing the dramatic increase in the number of radio devices due to the current advances in Internet of things (IoT), the shortage of frequency resources became a challenge to the new communication systems. To tackle this problem, cognitive radio (CR) has suggested to reutilize frequency bands left unoccupied by their licensed users raising the need to e↵ective estimation techniques that can detect these frequency bands. Additionally, if the spatial domain is investigated along with the spectral domain, a larger number of CRs can share the limited vacant bands simultaneously. In this thesis, the problem of estimating the special and spectral information of the existing users is considered for CR. The considered estimation problem should be handled blindly as CR should not have any prior information about the existing radio devices. Moreover, not only is the blindness the challenge of the considered problem, but also the need to instantaneously search a wideband spectrum, which needs high Nyquist rates. In order to overcome the latter issue, sub-Nyquist methods have been proposed in the literature. While these methods were capable to detect the desired parameters from reduced number of samples, they require a large number of relaxed analog-to-digital converters (ADCs) leading to increasing hardware complexity. In contrast to sub-Nyquist methods, which are carried out in the temporal domain, the proposed algorithms here are applied in the spatial domain of the employed array reducing the number of required samples at each array element to one. In addition, the proposed algorithms employ nonlinear Kalman filters (KFs), which are applied on a proposed spatial state space model, in two di↵erent scenarios; one of which estimates carrier frequencies and the corresponding direction of arrivals (DoA) of band-limited source signals, and the other one concerns with two-dimensional DoA (2DDoA). In each scenario, two di↵erent types of nonlinear KFs are implemented; the first is extended Kalman filter (EKF) and the other is unscented Kalman filter (UKF). Since nonlinear KFs are sub-optimal estimators, their performance can be deteriorated by several factors such as filter tuning and initialization, the variance of the estimated variables, and the value of the inter-elements spacing in the employed array. Using simulations, the e↵ects of these factors on the filter performance are examined and discussed. Overall, relying on one time sample in the proposed algorithms eliminates the high-sampling-rate requirements, whereas exploiting the spatial domain in detecting the unknown parameters results in a gradual decline in the degrees of freedom. |