The spectacular growth in wireless services imposes scarcity in term of the available radio spectrum. A solution to overcome this scarcity is to adopt what so called cognitive radio based on dynamic spectrum access. With dynamic spectrum access, secondary (unlicensed) users can access spectrum owned by primary (licensed) users when it is temporally and/or geographically unused. This unused spectrum is termed as spectrum opportunity. Finding these spectrum opportunities related aspects are studied in this thesis where two approaches of finding spectrum opportunities, namely spectrum sensing and geo-locations databases are considered.

In spectrum sensing arena, two topics are covered, blind spectrum sensing and sensing time and periodic sensing interval optimization. For blind spectrum sensing, a spectrum scanner based on maximum minimum eigenvalues detector and frequency domain rectangular filtering is developed. The measurements show that the proposed scanner outperforms the energy detector scanner in terms of the probability of detection. Continuing in blind spectrum sensing, a novel blind spectrum sensing technique based on discriminant analysis called spectrum discriminator has been developed in this thesis. Spectrum discriminator has been further developed to peel off multiple primary users with different transmission power from a wideband sensed spectrum. The spectrum discriminator performance is measured and compared with the maximum minimum eigenvalues detector in terms of the probability of false alarm, the probability of detection and the sensing time.

For sensing time and periodic sensing interval optimization, a new approach that aims at maximizing the probability of right detection, the transmission efficiency and the captured opportunities is proposed and simulated. The proposed approach optimizes the sensing time and the periodic sensing interval iteratively. Additionally, the periodic sensing intervals for multiple channels are optimized to achieve as low sensing overhead and unexplored opportunities as possible for a multi channels system.

The thesis considers radar bands and TV broadcasting bands to adopt geo-locations databases for spectrum opportunities. For radar bands, the possibility of spectrum sharing with secondary users in L, S and C bands is investigated. The simulation results show that band sharing is possible with more spectrum opportunities offered by C band than S and L band which comes as the least one. For the TV broadcasting bands, the thesis treats the power assignment for secondary users operate in Gävle area, Sweden. Furthermore, the interference that the TV transmitter would cause to the secondary users is measured in different locations in the same area.

There has been a recent explosive growth in mobile data consumption. This, in turn, imposes many challenges for mobile services providers and regulators in many aspects. One of these primary challenges is maintaining the radio spectrum to handle the current and upcoming expansion in mobile data traffic. In this regard, a radio spectrum regulatory framework based on secondary spectrum access is proposed as one of the solutions for the next generation wireless networks. In secondary spectrum access framework, secondary (unlicensed) systems coexist with primary (licensed) systems and access the spectrum on an opportunistic base.

In this thesis, aspects related to finding the free of use spectrum portions - called spectrum opportunities - are treated. One way to find these opportunities is spectrum sensing which is considered as an enabler of opportunistic spectrum access. In particular, this thesis investigates some topics in blind spectrum sensing where no priori knowledge about the possible co-existing systems is available.

As a standalone contribution in blind spectrum sensing arena, a new blind sensing technique is developed in this thesis. The technique is based on discriminant analysis statistical framework and called spectrum discriminator (SD). A comparative study between the SD and some existing blind sensing techniques was carried out and showed a reliable performance of the SD.

The thesis also contributes by exploring sensing parameters optimization for two existing techniques, namely, energy detector (ED) and maximum-minimum eigenvalue detector (MME). For ED, the sensing time and periodic sensing interval are optimized to achieve as high detection accuracy as possible. Moreover, a study of sensing parameters optimization in a real-life coexisting scenario, that is, LTE cognitive femto-cells, is carried out with an objective of maximizing cognitive femto-cells throughput. In association with this work, an empirical statistical model for LTE channel occupancy is accomplished. The empirical model fits the channels' active and idle periods distributions to a linear combination of multiple exponential distributions. For the MME, a novel solution for the filtering problem is introduced. This solution is based on frequency domain rectangular filtering. Furthermore, an optimization of the observation bandwidth for MME with respect to the signal bandwidth is analytically performed and verified by simulations.

After optimizing the parameters for both ED and MME, a two-stage fully-blind self-adapted sensing algorithm composed of ED and MME is introduced. The combined detector is found to outperform both detectors individually in terms of detection accuracy with an average complexity lies in between the complexities of the two detectors. The combined detector is tested with measured TV and wireless microphone signals.

The performance evaluation in the different parts of the thesis is done through measurements and/or simulations. Active measurements were performed for sensing performance evaluation. Passive measurements on the other hand were used for LTE downlink channels occupancy modeling and to capture TV and wireless microphone signals.