For the past 70 years, the Federal Communications Commission (FCC) has been the licensing authority for wireless spectrum. Traditionally, spectrum was commercially licensed to primary users with defined uses. With the growth of personal communication systems in the 1990''s, unallocated spectrum has become a scarce commodity. However, since most primary users are active only at certain times and places, much of the allocated spectrum remains underutilized. Substantial holes exist in the spatio-temporal spectrum that could be opportunistically used by unlicensed secondary users. As a result, the FCC is considering allowing secondary users to opportunistically use frequencies that are not being used by primary users. If multiple secondary users are present in the same geographical area, the concept of Dynamic Spectrum Sharing (DSS) allows these users to share the opportunistic spectrum. If several secondary users want to use a limited set of frequency resources, they will very likely interfere with each other. Sensing is a distributed technique where each transmitter/receiver pair senses (both passively and actively) the available channels and uses the channel that provides the best performance. While sensing alone allows sharing of the spectrum, it is not the optimal method in terms of maximizing the capacity in such a shared system. If we allow the secondary users to collaborate and share information, optimal capacity might be reached. However, collaboration adds another level of complexity to the transceivers of the secondary users, since they must now be able to communicate (Note that in general, the secondary users may have completely different communication protocols, e.g., Wi-Fi and Bluetooth). Additionally, optimizing the capacity of the available spectrum could have other negative side effects such as impacting the fairness of sharing the resources. Our primary goal is to explore the benefit of this cost-benefit tradeoff by determining the capacity increase obtainable from collaboration. As a secondary goal, we also wish to determine how this increase in capacity affects fairness. To summarize, the goal of this work is to answer the question: Fundamentally, what is the benefit of collaboration in Dynamic Spectrum Sharing? / 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

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.

Spectrum sharing is possible, but lacks R & D support for practical solutions that satisfy both the incumbent and secondary or opportunistic users. The author found a lack of an openly available framework supporting experimental research on the performance of a Spectrum Access System (SAS) and propose to build an open-source Software Defined Radio (SDR) based framework. This framework will test different dynamic spectrum scenarios in a wireless testbed. This thesis presents our Spectrum Access System prototype, discusses the design choices and trade-offs and provides a proof of concept implementation. We show that an Internet-accessible CORNET test bed provides the ideal platform for developing and testing the SAS functionality and its building blocks and offerss the hardware and software as a community resource for research and education. This design provides the necessary interfaces for researchers to develop and test their SAS-related modules, waveforms and scenarios. / Master of Science / In this information age, the number of wireless devices is growing faster than the infrastructure required to make wireless communication possible. This creates a possibility of not having enough radio spectrum to keep up with this growing demand. To alleviate this issue, there is a need to research and find more ways of efficiently utilizing the current spectrum resources available. Dynamic spectrum allocation is one way forward to archiving this goal. Frequency channels are assigned to devices based on prevailing conditions like device location and availability of channels that would cause low interference to other devices. Spectrum utilization is based on time, frequency and space with devices having the ability to hop to the best channel available. In this thesis, an open-source Spectrum Access System (SAS) was created as a platform through which dynamic spectrum allocation research can be done. The SAS is centralized management system that logs information about the prevailing spectrum usage, and in turn uses this information to dynamically allocate spectrum to devices and networks. This thesis shows how it was implemented, its current performance, and the steps that different researchers can take to add their own functionalities.

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.

Due to the rapid development of wireless communications together with the inflexibility of the current spectrum allocation policy, radio spectrum becomes more and more exhausted. One of the critical challenges of wireless communication systems is to efficiently utilize the limited frequency resources to be able to support the growing demand of high data rate wireless services. As a promising solution, cognitive radios have been suggested to deal with the scarcity and under-utilization of radio spectrum. The basic idea behind cognitive radios is to allow unlicensed users, also called secondary users (SUs), to access the licensed spectrum of primary users (PUs) which improves spectrum utilization. In order to not degrade the performance of the primary networks, SUs have to deploy interference control, interference mitigating, or interference avoidance techniques to minimize the interference incurred at the PUs. Cognitive radio networks (CRNs) have stimulated a variety of studies on improving spectrum utilization. In this context, this thesis has two main objectives. Firstly, it investigates the performance of single hop CRNs with spectrum sharing and opportunistic spectrum access. Secondly, the thesis analyzes the performance improvements of two hop cognitive radio networks when incorporating advanced radio transmission techniques. The thesis is divided into three parts consisting of an introduction part and two research parts based on peer-reviewed publications. Fundamental background on radio propagation channels, cognitive radios, and advanced radio transmission techniques are discussed in the introduction. In the first research part, the performance of single hop CRNs is analyzed. Specifically, underlay spectrum access using M/G/1/K queueing approaches is presented in Part I-A while dynamic spectrum access with prioritized traffics is studied in Part I-B. In the second research part, the performance benefits of integrating advanced radio transmission techniques into cognitive cooperative radio networks (CCRNs) are investigated. In particular, opportunistic spectrum access for amplify-and-forward CCRNs is presented in Part II-A where collaborative spectrum sensing is deployed among the SUs to enhance the accuracy of spectrum sensing. In Part II-B, the effect of channel estimation error and feedback delay on the outage probability and symbol error rate (SER) of multiple-input multiple-output CCRNs is investigated. In Part II-C, adaptive modulation and coding is employed for decode-and-forward CCRNs to improve the spectrum efficiency and to avoid buffer overflow at the relay. Finally, a hybrid interweave-underlay spectrum access scheme for a CCRN is proposed in Part II-D. In this work, the dynamic spectrum access of the PUs and SUs is modeled as a Markov chain which then is utilized to evaluate the outage probability, SER, and outage capacity of the CCRN.

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.