With advances in communications technologies, there is a constant need for higher data rates. One possible solution to overcome this need is to allocate additional bandwidth. However, due to spectrum scarcity this is no longer feasible. In addition, the results of spectrum measurement campaigns discovered the fact that the available spectrum is under-utilized. One of the most significant solutions to solve the under- utilization of radio-frequency (RF) spectrum is the cognitive radio (CR) concept. A valid mathematical model that can be applied for most practical scenarios and also captures the random fluctuations of the spectrum is necessary. This model provides a significant insight and also a better quantitative understanding of such systems and this is the topic of this dissertation. Compact mathematical formulations that describe the realistic spectrum usage would improve the recent theoretical work to a large extent. The data generated for such models, provide a mean for a more realistic evaluation of the performance of CR systems. However, measurement based models require a large amount of data and are subject to measurement errors. They are also likely to be subject to the measurement time, location, and methodology. In the first part of this dissertation, we introduce cognitive radio networks and their role on solving the problem of under-utilized spectrum. In the second part of this dissertation, we target the random variable which accounts for the fraction of available subcarriers for the secondary users in an OFDMA based CR system. The time and location dependency of the traffic is taken into account by a non-homogenous Poisson Point Process (PPP). In the third part, we propose a comprehensive statistical model for user activity, spectrum occupancy, and system throughput in the presence of mutual interference in an OFDMA-based CR network which accounts for the sensing procedure of spectrum sensor, spectrum demand-model and spatial density of primary users, system objective for user satisfaction which is to support as many users as possible, and environment-dependent conditions such as propagation path loss, shadowing, and channel fading. In the last part of this dissertation, unlike the second and the third parts that the modeling is theoretical and based on limiting assumptions, the spectrum usage modeling is based on real data collected from an extensive measurement.

With commercial deployment of the Citizens Band Radio Service commencing in the last quarter of 2018, efforts are in progress to improve the efficiency of the Spectrum Access System (SAS) functions. An area of concern as identified in recent field trials is the timebound evacuation of unlicensed secondary users from a frequency band by the SAS on the arrival of an incumbent user. In this thesis, we propose a way to optimize the evacuation process by reducing the number of secondary spectrum grant revocations to be performed. The proposed work leverages knowledge of incumbent user spectrum occupancy pattern obtained from historical spectrum usage data. Using an example model trained on 48 hours of an incumbent user transmission information, we demonstrate prediction of future incumbent user spectrum occupancy for the next 15 hours with 94.4% accuracy. The SAS uses this information to set the time validity of the secondary spectrum grants appropriately. In comparison to a case where spectrum grants are issued with no prior knowledge, the number of revocations declines by 87.5% with a 7.6% reduction in channel utilization. Further, the proposed technique provides a way for the SAS to plan ahead and prepare a backup channel to which secondary users can be redirected which can reduce the evacuation time significantly. / Master of Science / Studies on spectrum occupancy show that, in certain bands, licensed incumbent users use the spectrum only for some time or only within certain geographical limits. The dynamic spectrum access paradigm proposes to reclaim the underutilized spectrum by allowing unlicensed secondary users to access the spectrum opportunistically in the absence of the licensed users. In the United States, the Federal Communications Commission (FCC) has identified 150 MHz of spectrum space from 3550-3700 MHz to implement a dynamic spectrum sharing service called the Citizens Broadband Radio Service (CBRS). The guiding principle of this service is to maximize secondary user channel utilization while ensuring minimal incumbent user disruption. In this study, we propose that these conflicting requirements can be best balanced in the Spectrum Access System (SAS) by programming it to set the time validity of the secondary license grants by taking into consideration the incumbent spectrum occupancy pattern. In order to enable the SAS to learn incumbent spectrum occupancy in a privacy-preserving manner, we propose the use of a deep learning model, specifically the long-short term memory (LSTM). This model can be trained by federal agencies on historical incumbent spectrum occupancy information and then shared with the SAS in a secure manner to obtain prediction information about possible incumbent activity. Then, using the incumbent spectrum occupancy information from the LSTM model, the SAS could issue license grants that would expire before expected arrival time of incumbent user, thus minimizing the number of revocations on incumbent arrival. The scheme was validated using simulations that demonstrated the effectiveness of this approach in minimizing revocation complexity.

The owned spectrum allocation policy, in use since the early days of modern radio communications, has been proven to effectively control interference among radio communication systems. However, the overwhelming proliferation of new operators, innovative services and wireless technologies during the last years has resulted, under this static regulatory regime, in the depletion of spectrum bands with commercially attractive radio propagation characteristics. An important number of spectrum measurements, however, have shown that spectrum is mostly underutilized, thus indicating that the virtual spectrum scarcity problem actually results from static and inflexible spectrum management policies rather than the physical scarcity of radio resources. This situation has motivated the emergence of Dynamic Spectrum Access (DSA) methods based on the Cognitive Radio (CR) paradigm, which has gained popularity as a promising solution to conciliate the existing conflicts between spectrum demand growth and spectrum underutilization. The basic underlying idea of DSA/CR is to allow unlicensed (secondary) users to access in an opportunistic and non-interfering manner some licensed bands temporarily unoccupied by the licensed (primary) users. Due to the opportunistic nature of this principle, the behavior and performance of a DSA/CR network depends on the spectrum occupancy patterns of the primary system. A realistic and accurate modeling of such patterns becomes therefore essential and extremely useful in the domain of DSA/CR research. The potential applicability of spectrum usage models ranges from analytical studies to the design and dimensioning of secondary networks as well as the development of innovative simulation tools and more efficient DSA/CR techniques. Spectrum occupancy modeling in the context of DSA/CR constitutes a rather unexplored research area. This dissertation addresses the problem of modeling spectrum usage in the context of DSA/CR by contributing a comprehensive and holistic set of realistic models capable to accurately capture and reproduce the statistical properties of spectrum usage in real radio communication systems in the time, frequency and space dimensions. The first part of this dissertation addresses the development of a unified methodological framework for spectrum measurements in the context of DSA/CR and presents the results of an extensive spectrum measurement campaign performed over a wide variety of locations and scenarios in the metropolitan area of Barcelona, Spain, to identify potential bands of interest for future DSA/CR deployments. To the best of the author's knowledge, this is the first study of these characteristics performed under the scope of the Spanish spectrum regulation and one of the earliest studies in Europe. The second part deals with various specific aspects related to the processing of measurements to extract spectrum occupancy patterns, which is largely similar to the problem of spectrum sensing in DSA/CR. The performance of energy detection, the most widely employed spectrum sensing technique in DSA/CR, is first assessed empirically. The outcome of this study motivates the development of a more accurate theoretical-empirical performance model as well as an improved energy detection scheme capable to outperform the conventional method while preserving a similar level of complexity, computational cost and application. The findings of these studies are finally applied in the third part of the dissertation to the development of innovative spectrum usage models for the time (in discrete- and continuous-time versions), frequency and space domains. The proposed models can been combined and integrated into a unified modeling approach where the time, frequency and space dimensions of spectrum usage can simultaneously be reproduced, thus providing a complete and holistic characterization of spectrum usage in real systems for the analysis, design and simulation of the future DSA/CR networks.