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. / <p>QC 20130114</p> / QUASAR

Dynamic Spectrum Access (DSA) is now a commonly used spectrum sharing paradigm to mitigate the spectrum shortage problem. DSA technology allows unlicensed secondary users to access the unused frequency bands without interfering with the incumbent users. The key technical challenges in DSA systems lie in spectrum allocation problems and spectrum user's security issues. This thesis mainly focuses on spectrum monitoring technology in spectrum allocation and incumbent users' (IU) privacy issue. Spectrum monitoring is a powerful tool in DSA to help commercial users to access the unused bands. We proposed a crowdsourcing-based unknown IU pattern monitoring scheme that leverages the power of masses of portable mobile devices to reduce the cost of the spectrum monitoring and demonstrate the ability of our system to capture not only the existing spectrum access patterns but also the unknown patterns where no historical spectrum information exist. Due to the energy limit of the battery-based system, we then leverage solar energy harvesting and develop an energy management scheme to support our spectrum monitoring system. We also provide best privacy-protection strategies for both static and mobile IUs in terms of hiding their true location under the detection of Environmental Sensing Capabilities system. In this thesis, the heuristic approach for our mathematical formulations and simulation results are described in detail. The simulation results show our spectrum monitoring system can obtain a high spectrum monitoring coverage and low energy consumption. Our IU privacy scheme provides great protection for IU's location privacy. / Master of Science / Spectrum relates to the radio frequencies allocated to the federal users and commercial users for communication over the airwaves. It is a sovereign asset that is overseen by the government in each country to manage the radio spectrum and issue spectrum licenses. In addition, spectrum bands are utilized for various purposes because different bands have different characteristics. However, the overly crowded US frequency allocation chart shows the scarcity of usable radio frequencies. The actual spectrum usage measurements reflect that multiple prized spectrum bands lay idle at most time and location, which indicates that the spectrum shortage is caused by the spectrum management policies rather than the physical scarcity of available frequencies. Dynamic spectrum access (DSA) was proposed as a new paradigm of spectrum sharing that allows commercial users to access the abundant white spaces in the licensed spectrum bands to mitigate the spectrum shortage problem and increase spectrum utilization. In DSA, two of the key technical challenges lie in how to dynamically allocate the spectrum and how to protect spectrum users’ security. This thesis focuses on the development of two types of mechanisms for addressing the above two challenges: (1) developing efficient spectrum monitoring schemes to help secondary users (SU) to accurately and dynamically access the white space in spectrum allocation and (2) developing privacy preservation schemes for incumbent users (IU) to protect their location privacy. Specifically, we proposed an unknown IU pattern monitoring scheme that leverages the power of masses of portable mobile devices to reduce the cost of common spectrum monitoring systems. We demonstrate that our system can track not only the existing IU spectrum access patterns but also the unknown patterns where no historical spectrum information exists. We then leverage the solar energy harvesting and design energy management scheme to support our spectrum monitoring system. Finally, we provide a strategy for both static and mobile IUs to hide their true location under the monitoring of Environmental Sensing Capabilities systems.

The radio frequency spectrum is not being efficiently utilized partly due to the current policy of allocating the frequency bands to specific services and users. In opportunistic spectrum access (OSA), the ``white spaces'' that are not occupied by primary users (a.k.a. incumbent users) can be opportunistically utilized by secondary users. To achieve this, we need to solve two problems: (i) primary-secondary incumbent protection, i.e., prevention of harmful interference from secondary users to primary users; (ii) secondary-secondary network coexistence, i.e., mitigation of mutual interference among secondary users. The first problem has been addressed by spectrum sensing techniques in cognitive radio (CR) networks and geolocation database services in database-driven spectrum sharing. The second problem is the main focus of this dissertation. To obtain a clear picture of coexistence issues, we propose a taxonomy of heterogeneous coexistence mechanisms for shared spectrum access. Based on the taxonomy, we choose to focus on four typical coexistence scenarios in this dissertation. Firstly, we study sensing-based OSA, when secondary users are capable of employing the channel aggregation technique. However, channel aggregation is not always beneficial due to dynamic spectrum availability and limited radio capability. We propose a channel usage model to analyze the impact of both primary and secondary user behaviors on the efficiency of channel aggregation. Our simulation results show that user demands in both the frequency and time domains should be carefully chosen to minimize expected cumulative delay. Secondly, we study the coexistence of homogeneous CR networks, termed as self-coexistence, when co-channel networks do not rely on inter-network coordination. We propose an uplink soft frequency reuse technique to enable globally power-efficient and locally fair spectrum sharing. We frame the self-coexistence problem as a non-cooperative game, and design a local heuristic algorithm that achieves the Nash equilibrium in a distributed manner. Our simulation results show that the proposed technique is mostly near-optimal and improves self-coexistence in spectrum utilization, power consumption, and intra-cell fairness. Thirdly, we study the coexistence of heterogeneous CR networks, when co-channel networks use different air interface standards. We propose a credit-token-based spectrum etiquette framework that enables spectrum sharing via inter-network coordination. Specifically, we propose a game-auction coexistence framework, and prove that the framework is stable. Our simulation results show that the proposed framework always converges to a near-optimal distributed solution and improves coexistence fairness and spectrum utilization. Fourthly, we study database-driven OSA, when secondary users are mobile. The use of geolocation databases is inadequate in supporting location-aided spectrum sharing if the users are mobile. We propose a probabilistic coexistence framework that supports mobile users by locally adapting their location uncertainty levels in order to find an appropriate trade-off between interference mitigation effectiveness and location update cost. Our simulation results show that the proposed framework can determine and adapt the database query intervals of mobile users to achieve near-optimal interference mitigation with minimal location updates. / Ph. D.

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.

Convexity in graphs has been widely discussed in graph theory and G. Chartrand et al. introduced the convexity number of oriented graphs in 2002. In this thesis, we follow the notions addressed by them and develop an extension in some related topics of convexity in directed graphs. Let D be a connected oriented graph. A set S subseteq V(D) is convex in D if, for every pair of vertices x, yin S, the vertex set of every x-y geodesic (x-y shortest directed path) and y-x geodesic in D is contained in S. The convexity number con(D) of a nontrivial oriented graph D is the maximum cardinality of a proper convex set of D. We show that for every possible triple n, m, k of integers except for k=4, there exists a strongly connected digraph D of order n, size m, and con(D)=k. Let G be a graph. We define the convexity spectrum S_{C}(G)={con(D): D is an orientation of G} and the strong convexity spectrum S_{SC}(G)={con(D): D is a strongly connected orientation of G}. Then S_{SC}(G) ⊆ S_{C}(G). We show that for any n ¡Ú 4, 1 ≤ a ≤ n-2 and a n ¡Ú 2, there exists a 2-connected graph G with n vertices such that S_C(G)=S_{SC}(G)={a,n-1}, and there is no connected graph G of order n ≥ 3 with S_{SC}(G)={n-1}. We also characterizes the convexity spectrum and the strong convexity spectrum of complete graphs, complete bipartite graphs, and wheel graphs. Those convexity spectra are of different kinds. Let the difference of convexity spectra D_{CS}(G)=S_{C}(G)- S_{SC}(G) and the difference number of convexity spectra dcs(G) be the cardinality of D_{CS}(G) for a graph G. We show that 0 ≤ dcs(G) ≤ ⌊|V(G)|/2⌋, dcs(K_{r,s})=⌊(r+s)/2⌋-2 for 4 ≤ r ≤ s, and dcs(W_{1,n-1})= 0 for n ≥ 5. The convexity spectrum ratio of a sequence of simple graphs G_n of order n is r_C(G_n)= liminflimits_{n to infty} frac{|S_{C}(G_n)|}{n-1}. We show that for every even integer t, there exists a sequence of graphs G_n such that r_C(G_n)=1/t and a formula for r_C(G) in subdivisions of G.

Wind waves are highly complex, random phenomena. One way to describe the irregular nature of the sea surface is the use of wave energy spectrum. Spectral information for wind waves in the Black Sea is extremely limited. Knowledge on spectral characteristics of wind waves would contribute to scientific, engineering and operational coastal and marine activities in the Black Sea. The aim of the present thesis is to investigate characteristics of wind wave spectra for the Eastern Black Sea. This would allow detailed understanding of the nature of the waves occurring in this enclosed basin. Long-term wave measurements obtained by directional buoys deployed offshore at Sinop, Hopa and Gelendzhik were utilized as the three sets of wave data. Records were analyzed to identify them as uni-modal or multi-modal spectra, and occurrences of spectral peaks were computed. Single peaked spectra were studied as belonging to fully arisen or developing sea states. Model parameters of JONSWAP and PM spectra were estimated for the observed spectra by using a least square error method. The records of developing seas were further analyzed to select the ones belonging to stable wind conditions. Fetch dependencies of non-dimensional spectral variables, mean parameters of JONSWAP model spectrum and the envelop of dimensionless spectra were investigated for this data sub-set.

Cognitive radio (CR) technology has recently been introduced to opportunistically exploit the spectrum. We present a robust and cost-effective design to ensure the improvement of spectrum efficiency with CR. We first propose probability-based spectrum sensing by utilizing the statistical characteristics of licensed channel occupancy, which achieves nearly optimal performance with relatively low complexity. Based on the statistical model, we then propose periodic spectrum sensing scheduling to determine the optimal inter-sensing duration and vary the transmit power at each data sample to enhance throughput and reduce interference. We further develop a probability-based scheme for combination of local sensing information collected from cooperative CR users, which enables combination of both synchronous and asynchronous sensing information. To satisfy the stringent bandwidth constraint for reporting, we also propose to simultaneously send local sensing data to a combining node through the same narrowband channel. With proper preprocessing at individual users, such a design maintains reasonable detection performance while the bandwidth required for reporting does not change with the number of cooperative users. To better utilize the spectrum and avoid possible interference, we propose spectrum shaping schemes based on spectral precoding, which enable efficient spectrum sharing between CR and licensed users and exhibit the advantages of both simplicity and flexibility. We also propose a novel resource allocation approach based on the probabilities of licensed channel availability obtained from spectrum sensing. Different from conventional approaches, the probabilistic approach exploits the flexibility of CR to ensure efficient spectrum usage and protect licensed users from unacceptable interference.

In 2015, Federal Communications Commission (FCC) enabled shared commercial use of the 3.550-3.700 GHz band. A framework was developed to enable this spectrum-sharing capa- bility which included an automated frequency coordinator called Spectrum Access System (SAS). This work extends the open source SAS based on the aforementioned FCC SAS framework developed by researchers at Virginia Tech Wireless group, with real-time envi- ronment sensing capability along with intelligent incumbent detection using Software-defined Radios (SDRs) and a real-time graphical user interface. This extended version is called the OpenSAS. Furthermore, the SAS client and OpenSAS are extended to be compliant with the Wireless Innovation Forum (WINNF) specifications by testing the SAS-CBRS Base Station Device (CBSD) interface with the Google SAS Test Environment. The Environment Sensing Capability (ESC) functionality is evaluated and tested in our xG Testbed to verify its ability to detect the presence of users in the CBRS band. An ML-based feedforward neural net- work model is employed and trained using simulated radar waveforms as incumbent signals and captured 5G New Radio (NR) signals as a non-incumbent signal to predict whether the detected user is a radar incumbent or an unknown user. If the presence of incumbent radar is detected with an 85% or above certainty, incumbent protection is activated, terminating CBSD grants causing damaging interference to the detected incumbent. A 5G NR signal is used as a non-incumbent user and added to the training dataset to better the ability of the model to reject non-incumbent signals. The model achieves a maximum validation accuracy of 95.83% for signals in the 40-50 dB Signal-to-Noise Ratio (SNR) range. It achieves an 85.35% accuracy for Over the air (OTA) real-time tests. The non-incumbent 5G NR signal rejection accuracy is 91.30% for a calculated SNR range of 10-20 dB. In conclusion, this work advances state of the art in spectrum sharing systems by presenting an enhanced open source SAS and evaluating the newly added functionalities. / Master of Science / In 2015, Federal Communications Commission (FCC) enabled shared commercial use of the 3.550-3.700 GHz band. A framework was developed to enable this spectrum-sharing capability which included an automated frequency coordinator called Spectrum Access System (SAS). The task of the SAS is to make sure no two users use the same spectrum in the same location causing damaging interference to each other. The SAS is also responsible for prioritizing the higher tier users and protecting them from interference from lower tier users. This work extends the open source SAS based on the aforementioned FCC SAS framework developed by researchers at Virginia Tech Wireless group, with real-time environment sensing capability along with intelligent incumbent detection using Software-defined Radios (SDRs) and a real-time graphical user interface. This extended version is called the OpenSAS. Furthermore, the SAS client and OpenSAS are extended to be compliant with the Wireless Innovation Forum (WINNF) specifications by testing the SAS-CBRS Base Station Device (CBSD) interface with the Google SAS Test Environment. The Environment Sensing Capability (ESC) functionality is evaluated and tested in our xG Testbed to verify its ability to detect the presence of users in the CBRS band. The ESC is used to detect incumbent users (the highest tier) that do not inform the SAS about their use of the spectrum. An ML-based feedforward neural net- work model is employed and trained using simulated radar waveforms as incumbent signals and captured 5G New Radio (NR) signals as a non-incumbent signal to predict whether the detected user is a radar incumbent or an unknown user. If the presence of incumbent radar is detected with an 85% or above certainty, incumbent protection is activated, terminating CBSD grants causing damaging interference to the detected incumbent. A 5G NR signal is used as a non-incumbent user and added to the training dataset to better the ability of the model to reject non-incumbent signals. The model achieves a maximum validation accuracy of 95.83% for signals in the 40-50 dB Signal to-Noise Ratio (SNR) range. It achieves an 85.35% accuracy for Over the air (OTA) real-time tests. The non-incumbent 5G NR signal rejection accuracy is 91.30% for a calculated SNR range of 10-20 dB. In conclusion, this work advances state of the art in spectrum sharing systems by presenting an enhanced open source SAS and evaluating the newly added functionalities.

Cognitive radios (CRs) are devices that are capable of sensing the spectrum and using its free portions in an opportunistic manner. The free spectrum portions are referred to as white spaces or spectrum holes. It is widely believed that CRs are one of the key enabling technologies for realizing a new regulatory spectrum management paradigm, viz. dynamic spectrum access (DSA). CRs often employ software-defined radio (SDR) platforms that are capable of executing artificial intelligence (AI) algorithms to reconfigure their transmission/reception (TX/RX) parameters to communicate efficiently while avoiding interference with licensed (a.k.a. primary or incumbent) users and unlicensed (a.k.a. secondary or cognitive) users. When different stakeholders share a common resource, such as the case in spectrum sharing, security, privacy, and enforcement become critical considerations that affect the welfare of all stakeholders. Recent advances in radio spectrum access technologies, such as CRs, have made spectrum sharing a viable option for significantly improving spectrum utilization efficiency. However, those technologies have also contributed to exacerbating the difficult problems of security, privacy and enforcement. In this dissertation, we review some of the critical security and privacy threats that impact spectrum sharing. We also discuss ex ante (preventive) approaches which mitigate the security and privacy threats and help spectrum enforcement. / Ph. D.

Call number: LD2668 .T4 1956 H38 / Master of Science