With dynamic spectrum access emerging as an important paradigm for efficient spectrum use, mechanisms are required to ensure disciplined spectrum access by secondary users. This must be done without requiring secondary users to disclose private data, such as their exact usage pattern or identities of parties involved. We formulate, design and evaluate a mechanism to collect spectrum activity information using a set of CPEs. A system design is presented which uses a number of techniques to address mobility and security issues involved in relying on CPEs to collect spectrum activity information. The system imposes an observation probability such that a rational cheater is dissuaded from spectrum misuse. The minimum number of CPEs required to impose this observation probability is determined by formulating it as an integer linear program. The security and privacy of this system is analyzed, along with simulation results to evaluate the quality of the solution. Based on the current design, directions for future work are identified and preliminary approaches are presented. / Master of Science

Dynamic Spectrum Access allows wireless users to access a wide range of spectrum which increases a node's ability to communicate with its neighbors, and spectral efficiency through opportunistic access to licensed bands. Our study focuses on the scalability of network performance, which we define in terms of network transport capacity and end-to-end throughput per node, as the network density increases. We develop an analytical procedure for performance evaluation of ad hoc DSA networks using Markov models, and analyze the performance of a DSA network with one transceiver per node and a dedicated control channel. We also develop and integrate a detailed model for energy detection in Poisson networks with sensing. We observe that the network capacity scales sub-linearly with the number of DSA users and the end-to-end throughput diminishes, when the number of data channels is fixed. Nevertheless, we show that DSA can improve network performance by allowing nodes to access more spectrum bands while providing a mechanism for spectrum sharing and maintaining network wide connectivity. We also observe that the percentage of relative overhead at the medium access layer does not scale with the number of users. Lastly, we examine the performance impact of primary user density, detection accuracy, and the number of available data channels. The results help to answer the fundamental question of the scaling behavior of network capacity, end-to-end throughput, and network overhead in ad hoc DSA networks. / Master of Science

This thesis provides three main contributions with respect to the Dynamic Channel and Power Assignment (DCPA) problem. DCPA refers to the allocation of transmit power and frequency channels to links in a cognitive dynamic spectrum network so as to maximize the total number of feasible links while minimizing the aggregate transmit power. In order to provide a method to compare related, yet disparate, work, the first contribution of this thesis is a unifying optimization formulation to describe the DCPA problem. This optimization problem is based on maximizing the number of feasible links and minimizing transmit power of a set of communications links in a given communications network. Using this optimization formulation, this thesis develops its second contribution: a evaluation method for comparing DCPA algorithms. The evaluation method is applied to five DPCA algorithms representative of the DCPA literature . These five algorithms are selected to illustrate the tradeoffs between control modes (centralized versus distributed) and channel/power assignment techniques. Initial algorithm comparisons are done by analyzing channel and power assignment techniques and algorithmic complexity of five different DCPA algorithms. Through simulations, algorithm performance is evaluated by the metrics of feasibility ratio and average power per link. Results show that the centralized algorithm Minimum Power Increase Assignment (MPIA) has the overall best feasibility ratio and the lowest average power per link of the five algorithms we investigated. Through assignment by the least change in transmit power, MPIA minimizes interference and increases the number of feasible links. However, implementation of this algorithm requires calculating the inverse of near singular matrices, which could lead to inaccurate results. The third contribution of this thesis is a proposed distributed channel assignment algorithm, Least Interfering Channel and Iterative Power Assignment (LICIPA). This distributed algorithm has the best feasibility ratio and lowest average power per link of the distributed algorithms. In some cases, LICIPA achieves 90% of the feasibility ratio of MPIA, while having lower complexity and overall lower average run time. / Master of Science

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

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.

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. / Radio spectrum is a precious resource that enables wireless communications. On the one hand, the demand for wireless spectrum is skyrocketing due to the ever-increasing number of smartphones and other wireless devices. On the other hand, the total usable wireless spectrum is limited. As a result, we are at a stage where spectrum demand far exceeds the supply. Since spectrum is a finite resource, the only way to fulfill this demand is by sharing the spectrum dynamically among multiple users—i.e., by enabling “dynamic spectrum sharing” among different class of users and uses. In this dissertation, we seek to investigate methods and tools for improving the utilization efficiency of the shared spectrum as well as for ensuring the operational privacy and security of spectrum users in dynamic spectrum sharing. In doing so, we propose several novel approaches and demonstrate their efficacy in improving spectrum utilization efficiency and operational privacy by providing results from extensive simulations and relevant real-world case studies. We believe that studies of this kind facilitate the regulators and other stakeholders a better understanding of mechanisms that enable improved spectrum utilization efficiency and minimize the associated operational privacy and security threats—and hence, help in wider adoption of dynamic spectrum sharing.

Recent advances in spectrum access technologies, such as software-defined radios, have made dynamic spectrum sharing (DSS) a viable option for addressing the spectrum shortage problem. However, these advances have also contributed to the increased possibility of "rogue" transmitter radios which may cause significant interference to other radios in DSS. One approach for countering such threats is to employ a transmitter authentication scheme at the physical (PHY) layer. In PHY-layer authentication, an authentication signal is generated by the transmitter, and embedded into the message signal. This enables a regulatory enforcement entity to extract the authentication signal from the received signal, uniquely identify a transmitter, and collect verifiable evidence of a rogue transmission that can be used later during an adjudication process. There are two primary technical challenges in devising a transmitter authentication scheme for DSS: (1) how to generate and verify the authentication signal such that the required security and privacy criteria are met; and (2) how to embed and extract the authentication signal without negatively impacting the performance of the transmitters and the receivers in DSS. With regard to dealing with the first challenge, the authentication schemes in the prior art, which provide privacy-preserving authentication, have limited practical value for use in large networks due to the high computational complexity of their revocation check procedures. In this dissertation, the novel approaches which significantly improve scalability of the transmitter authentication with respect to revocation, are proposed. With regard to dealing with the second challenge, in the existing PHY-layer authentication techniques, the authentication signal is embedded into the message signal in such a way that the authentication signal appears as noise to the message signal and vice versa. Hence, existing schemes are constrained by a fundamental tradeoff between the message signal's signal to interference and noise ratio (SINR) and the authentication signal's SINR. In this dissertation, the novel approaches which are not constrained by the aforementioned tradeoff between message and authentication signals, are proposed. / Ph. D. / Recent advances in spectrum access technologies, such as software-defined radios, have made dynamic spectrum sharing (DSS) a viable option for addressing the spectrum shortage problem. However, these advances have also contributed to the increased possibility of “rogue” transmitter radios which may cause significant interference to other radios in DSS. One approach for countering such threats is to employ a <i>transmitter authentication</i> scheme at the physical (PHY) layer. In PHY-layer authentication, an authentication signal is generated by the transmitter, and embedded into the message signal. This enables a regulatory enforcement entity to extract the authentication signal from the received signal, uniquely identify a transmitter, and collect verifiable evidence of a rogue transmission that can be used later during an adjudication process. There are two primary technical challenges in devising a transmitter authentication scheme for DSS: (1) how to generate and verify the authentication signal such that the required security and privacy criteria are met; and (2) how to embed and extract the authentication signal without negatively impacting the performance of the transmitters and the receivers in DSS. With regard to dealing with the first challenge, the authentication schemes in the prior art, which provide privacy-preserving authentication, have limited practical value for use in large networks due to the high computational complexity of their revocation check procedures. In this dissertation, the novel approaches which significantly improve scalability of the transmitter authentication with respect to revocation, are proposed. With regard to dealing with the second challenge, in the existing PHY-layer authentication techniques, the authentication signal is embedded into the message signal in such a way that the authentication signal appears as noise to the message signal and vice versa. Hence, existing schemes are constrained by a fundamental tradeoff between the message signal’s signal to interference and noise ratio (SINR) and the authentication signal’s SINR. In this dissertation, the novel approaches which are not constrained by the aforementioned tradeoff between message and authentication signals, are proposed.

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