The modern web has evolved into a highly capable software platform, which enables near-native performance, while offering installation-free cross-platform applications with a uniform user base and rapid update deployment. SVG, WebGL, and HTML5 Canvas, along with various higher-level JavaScript frameworks allow web applications to drive both 2D and 3D visualization. These technologies allow developing novel visualization applications, which can be applied in the communication domain to geospatially map service quality, and to provide tools research and education in wireless communication. We present two such web applications GeoSpy and CORNET3D. GeoSpy provides 2D and 3D visualization of geospatial data on the web. The application is primarily focused on leveraging 3D portrayals to increase the number of broadband Quality of Service (QoS) metrics, which can be attached to a single point on a map. Additionally, GeoSpy has proven to be a flexible visualization platform by giving the user a high level of customization over HTTP API data. This allows GeoSpy to venture beyond broadband mapping, and provide 3D portrayals of any well-formatted geospatial JSON API. Research of Software Defined Radio (SDR) and Dynamic Spectrum Access (DSA) can be used to significantly improve the wireless QoS. CORNET3D provides a 3D view of the Virginia Tech CORNET SDR testbed with information on which nodes and radios are operational. The application can also display 2D and 3D plots of the spectrum, which is sensed by the radios in real time. The data is sent to the client over a WebSocket connection to enable low latency, compared to conventional HTTP. CORNET3D can teach students about strategies for optimal use of spectrum resources through a game---"by providing them with real-time scoring based on their choices for radio transmission parameters. CORNET3D has demonstrated that the not only can web applications provide rich portrayals of real-time sensor data, but can also serve as a 3D "serious game" platform. / Master of Science

In recent years, extensive research has been devoted to opportunistically exploiting spectrum in a distributed cognitive radio network. In such a network, autonomous secondary users (SUs) compete with each other for better channels without instructions from a centralized authority or explicit coordination among SUs. Channel selection relies on channel occupancy information observed by SUs, including whether a channel is occupied by a PU or an SU. Therefore, the SUs' performance depends on the quality of the information. Current research in this area often assumes that the SUs can distinguish a channel occupied by a PU from one occupied by another SU. This can potentially be achieved using advanced signal detection techniques but not by simple energy detection. However, energy detection is currently the primary detection technique proposed for use in cognitive radio networks. This creates a need to design a channel selection strategy under the assumption that, when SUs observe channel availability, they cannot distinguish between a channel occupied by a PU and one occupied by another SU. Also, as energy detection is simpler and less costly than more advanced signal detection techniques, it is worth understanding the value associated with better channel occupancy information. The first part of this thesis investigates the impact of different types of imperfect information on the performance of secondary users (SUs) attempting to opportunistically exploit spectrum resources in a distributed manner in a channel environment where all the channels have the same PU duty cycle. We refer to this scenario as the homogeneous channel environment. We design channel selection strategies that leverage different levels of information about channel occupancy. We consider two sources of imperfect information: partial observability and sensing errors. Partial observability models SUs that are unable to distinguish the activity of PUs from SUs. Therefore, under the partial observability models, SUs can only observe whether a channel was occupied or not without further distinguishing it was occupied by a PU or by SUs. This type of imperfect information exists, as discussed above, when energy detection is adopted as the sensing technique. We propose two channel selection strategies under full and partial observability of channel activity and evaluate the performance of our proposed strategies through both theoretical and simulation results. We prove that both proposed strategies converge to a stable orthogonal channel allocation when the missed detection rate is zero. The simulation results validate the efficiency and robustness of our proposed strategies even with a non-zero probability of missed detection. The second part of this thesis focuses on computing the probability distribution of the number of successful users in a multi-channel random access scheme. This probability distribution is commonly encountered in distributed multi-channel communication systems. An algorithm to calculate this distribution based on a recursive expression was previously proposed. We propose a non-recursive algorithm that has a lower execution time than the one previously proposed in the literature. The third part of this thesis investigates secondary users (SUs) attempting to opportunistically exploit spectrum resources in a scenario where the channels have different duty cycles, which we refer to as the heterogeneous channel environment. In particular, we model the channel selection process as a one shot game. We prove the existence of a symmetric Nash equilibrium for the proposed static game and design a channel selection strategy that achieves this equilibrium. The simulation results compare the performance of the Nash equilibrium to two other strategies(the random and the proportional strategies) under different PU activity scenarios. / Master of Science

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.

We are in the midst of a major paradigm shift in how we manage the radio spectrum. This paradigm shift in spectrum management from exclusive access to shared access is necessitated by the growth of wireless services and the demand pressure imposed on limited spectrum resources under legacy management regimes. The primary constraint in any spectrum sharing regime is that the incumbent users (IUs) of the spectrum need to be protected from harmful interference caused due to transmissions from secondary users (SUs). Unfortunately, legacy techniques rely on inadequately flexible and overly conservative methods for prescribing interference protection that result in inefficient utilization of the shared spectrum. In this dissertation, we first propose an analytical approach for characterizing the aggregate interference experienced by the IU when it shares the spectrum with multiple SUs. Proper characterization of aggregate interference helps in defining incumbent protection boundaries, a.k.a. Exclusion Zones (EZs), that are neither overly aggressive to endanger the IU protection requirement, nor overly conservative to limit spectrum utilization efficiency. In particular, our proposed approach addresses the two main limitations of existing methods that use terrain based propagation models for estimating the aggregate interference. First, terrain-based propagation models are computationally intensive and data-hungry making them unsuitable for large real-time spectrum sharing applications such as the spectrum access system (SAS). Second, terrain based propagation models require accurate geo-locations of SUs which might not always be available, such as when SUs are mobile, or when their locations are obfuscated for location privacy concerns. Our second contribution in this dissertation is the novel concept of Multi-tiered Incumbent Protection Zones (MIPZ) that can be used to prescribe interference protection to the IUs. Based on the aforementioned analytical tool for characterizing the aggregate interference, we facilitate a framework that can be used to replace the legacy notion of static and overly conservative EZs with multi-tiered dynamic EZs. MIPZ is fundamentally different from legacy EZs in that it dynamically adjusts the IU's protection boundary based on the radio environment, network dynamics, and the IU interference protection requirement. Our extensive simulation results show that MIPZ can be used to improve the overall spectrum utilization while ensuring sufficient protection to the IUs. As our third contribution, we investigate the operational security (OPSEC) issue raised by the emergence of new spectrum access technologies and spectrum utilization paradigms. For instance, although the use of geolocation databases (GDB) is a practical approach for enabling efficient spectrum sharing, it raises a potentially serious OPSEC problem, especially when some of the IUs are federal government entities, including military users. We show that malicious queriers can readily infer the locations of the IUs even if the database's responses to the queries do not directly reveal such information. To address this issue, we propose a perturbation-based optimal obfuscation strategy that can be implemented by the GDB to preserve the location privacy of IUs. The proposed obfuscation strategy is optimal in the sense that it maximizes IUs' location privacy while ensuring that the expected degradation in the SUs' performance due to obfuscated responses does not exceed a threshold. In summary, this dissertation focuses on investigating techniques that improve the utilization efficiency of the shared spectrum while ensuring adequate protection to the IUs from SU induced interference as well as from potential OPSEC threats. We believe that this study facilitates the regulators and other stakeholders a better understanding of mechanisms that enable improved spectrum utilization efficiency and minimize the associated OPSEC threats, and hence, helps in wider adoption of dynamic spectrum sharing. / Ph. D.

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.

Dynamic spectrum access (DSA) is a promising technology to mitigate spectrum shortage and improve spectrum utilization. However, DSA users have to face two fundamental issues, interference coordination between DSA users and protections to primary users (PUs). These two issues are very challenging, since generally there is no powerful infrastructure in DSA networks to support centralized control. As a result, DSA users have to perform spectrum managements, including spectrum access and power allocations, independently without accurate channel state information. In this thesis, a novel spectrum management approach is proposed, in which Q-learning, a type of reinforcement learning, is utilized to enable DSA users to carry out effective spectrum managements individually and intelligently. For more efficient processes, powerful neural networks (NNs) are employed to implement Q-learning processes, so-called deep Q-network (DQN). Furthermore, I also investigate the optimal way to construct DQN considering both the performance of wireless communications and the difficulty of NN training. Finally, extensive simulation studies are conducted to demonstrate the effectiveness of the proposed spectrum management approach. / Generally, in dynamic spectrum access (DSA) networks, co-operations and centralized control are unavailable and DSA users have to carry out wireless transmissions individually. DSA users have to know other users’ behaviors by sensing and analyzing wireless environments, so that DSA users can adjust their parameters properly and carry out effective wireless transmissions. In this thesis, machine learning and deep learning technologies are leveraged in DSA network to enable appropriate and intelligent spectrum managements, including both spectrum access and power allocations. Accordingly, a novel spectrum management framework utilizing deep reinforcement learning is proposed, in which deep reinforcement learning is employed to accurately learn wireless environments and generate optimal spectrum management strategies to adapt to the variations of wireless environments. Due to the model-free nature of reinforcement learning, DSA users only need to directly interact with environments to obtain optimal strategies rather than relying on accurate channel estimations. In this thesis, Q-learning, a type of reinforcement learning, is adopted to design the spectrum management framework. For more efficient and accurate learning, powerful neural networks (NN) is employed to combine Q-learning and deep learning, also referred to as deep Q-network (DQN). The selection of NNs is crucial for the performance of DQN, since different types of NNs possess various properties and are applicable for different application scenarios. Therefore, in this thesis, the optimal way to construct DQN is also analyzed and studied. Finally, the extensive simulation studies demonstrate that the proposed spectrum management framework could enable users to perform proper spectrum managements and achieve better performance.

Ubiquitous connectivity plays an important role in improving the quality of life in terms of economic development, health and well being, social justice and equity, as well as in providing new educational opportunities. However, rural communities which account for 46% of the world's population lacks access to proper connectivity to avail such societal benefits, creating a huge "digital divide" between the urban and rural areas. A primary reason is that the Information and Communication Technologies (ICT) providers have less incentives to invest in rural areas due to lack of promising revenue returns. Existing research and industrial attempts in providing connectivity to rural communities suffer from severe drawbacks, such as expensive wireless spectrum licenses and infrastructures, under- and over-provisioning of spectrum resources while handling heterogeneous traffic, lack of novel wireless technologies tailored to the unique challenges and requirements of rural communities (e.g., agricultural fields). Leveraging the recent advances in Dynamic Spectrum Access (DSA) technologies like wide band spectrum analyzers and spectrum access systems, and multi-radio access technologies (multi-RAT), this dissertation proposes a novel Diverse Band-aware DSA (d-DSA) network architecture, that addresses the drawbacks of existing standard and DSA wireless solutions, and extends ubiquitous connectivity to rural communities; a step forward in the direction of the societal and economic improvements in rural communities, and hence, narrowing the "digital divide" between the rural and urban societies. According to this paradigm, a certain wireless device is equipped with software defined radios (SDRs) that are capable of accessing multiple (un)licensed spectrum bands, such as, TV, LTE, GSM, CBRS, ISM, and possibly futuristic mmWaves. In order to fully exploit the potential of the d-DSA paradigm, while meeting heterogeneous traffic demands that may be generated in rural communities, we design efficient routing strategies and optimization techniques, which are based on a variety of tools such as graph modeling, integer linear programming, dynamic programming, and heuristic design. Our results on realistic traces in a large variety of rural scenarios show that the proposed techniques are able to meet the heterogeneous traffic requirements of rural applications, while ensuring energy efficiency and robustness of the architecture for providing connectivity to rural communities.

We develop efficient coordination techniques that support inelastic traffic in large-scale distributed dynamic spectrum access DSA networks. By means of any learning algorithm, the proposed techniques enable DSA users to locate and exploit spectrum opportunities effectively, thereby increasing their achieved throughput (or "rewards" to be more general). Basically, learning algorithms allow DSA users to learn by interacting with the environment, and use their acquired knowledge to select the proper actions that maximize their own objectives, thereby "hopefully" maximizing their long-term cumulative received reward/throughput. However, when DSA users' objectives are not carefully coordinated, learning algorithms can lead to poor overall system performance, resulting in lesser per-user average achieved rewards. In this thesis, we derive efficient objective functions that DSA users an aim to maximize, and that by doing so, users' collective behavior also leads to good overall system performance, thus maximizing each user's long-term cumulative received rewards. We show that the proposed techniques are: (i) efficient by enabling users to achieve high rewards, (ii) scalable by performing well in systems with a small as well as a large number of users, (iii) learnable by allowing users to reach up high rewards very quickly, and (iv) distributive by being implementable in a decentralized manner. / Graduation date: 2013

This thesis considers resource management issues in wireless sensor networks (WSNs), wireless local area networks (WLANs), and cognitive radio (CR) networks. Since energy is a critical resource in WSNs, we consider energy minimization techniques based on explicit node cooperation and distributed source coding (DSC). The explicit node cooperation based on space time block codes (STBC) improves energy efficiency of WSNs, by reducing the energy consumption per bit of each sensor node. The DSC on the other hand exploits the spatial correlation in WSNs, and thus reduces the data generated in a WSN. For the purpose of our analysis, we model the spatial correlation according to a linear Gauss-Markov model. Through our numerical results, we observe that the node cooperation combined with DSC can improve energy efficiency for many cases of interest. A unique aspect of our work is we obtain important structural results using the concepts from monotone comparative statics. These structural results provide insights into the general design of WSNs. Through our numerical results, we also demonstrate that, the cooperation based transmission can achieve better mutual information (MI)-energy tradeoff than the non-cooperation based transmission scheme. From the perspective of WLANs, we propose a price based approach to regulate the channel occupancy of low rate users, which is known to be the primary cause for low overall throughput in WLANs. Owing to the decentralized nature of WLANs we use non-cooperative game theory as a tool for analysis. Specifically, we use supermodular game theory. Through our analysis, we show that an increase in price leads to an increase in rate of WLAN users. We also prove that the best response dynamics indeed converge to the Nash equilibrium of the underlying non-cooperative game. Through our numerical results, we demonstrate that by proper tuning of the price, the proposed price based approach can lead to an improvement in overall throughput of a WLAN. Finally from the perspective of CR networks, we consider the impact of number of channels captured by a secondary user on its transmission control protocol (TCP) throughput. From our simulation results it was found that, there exists a definite optimal number of channels a secondary user needs to capture, to maximize its TCP throughput.

Wireless communications systems underwent an evolution as the voice oriented applications evolved to data and multimedia based services. Furthermore, current wireless technologies, regulations and the un- derstanding of the technology are insufficient for the requirements of future wireless systems. Along with the rapid rise at the number of users, increasing demand for more communications capacity to deploy multimedia applications entail effective utilization of communications resources. Therefore, there is a need for effective spectrum allocation, adaptive and complex modulation, error recovery, channel estimation, diversity and code design techniques to allow high data rates while maintaining desired quality of service, and reconfigurable and flexible air interface technologies for better interference and fading management. However, traditional communications system design is based on allocating fixed amounts of resources to the user and does not consider adaptive spectrum utilization. Technologies which will lead to adaptive, intelligent, and aware wireless communications systems are expected to come up with consistent methodologies to provide solutions for the capacity, interference, and reliability problems of the wireless networks. Spectrum sensing feature of cognitive radio systems are a step forward to better recognize the problems and to achieve efficient spectrum allocation. On the other hand, even though spectrum sensing can constitute a solid base to achieve the reconfigurability and awareness goals of next generation networks, a new perspective is required to benefit from the whole dimensions of the available electro hyperspace. Therefore, spectrum sensing should evolve to a more general and comprehensive awareness providing a mechanism, not only as a part of CR systems which provide channel occupancy information but also as a communication environment awareness component of dynamic spectrum access paradigm which can adapt sensing parameters autonomously to ensure robust identification and parameter estimation for the signals over the monitored spectrum. Such an approach will lead to recognition of communications opportunities in different dimensions of spectrum hyperspace, and provide necessary information about the air interfaces, access techniques and waveforms that are deployed over the monitored spectrum to accomplish adaptive resource management and spectrum access. We define multidimensional signal analysis as a methodology, which not only provides the information that the spectrum hyperspace dimension in interest is occupied or not, but also reveals the underlaying information regarding to the parameters, such as employed channel access methods, duplexing techniques and other parameters related to the air interfaces of the signals accessing to the monitored channels and more. To achieve multidimensional signal analysis, a comprehensive sensing, classification, and a detection approach is required at the initial stage. In this thesis, we propose the multidimensional signal analysis procedures under signal identification algorithms in time, frequency. Moreover, an angle of arrival estimation system for wireless signals, and a spectrum usage modeling and prediction method are proposed as multidimensional signal analysis functionalities.