Traditionally, networking technology has been limited because of the networks inability to adapt resulting in sub-optimal performance. Limited in state, scope and response mechanisms, network elements consisting of nodes, protocol layers and policies have been unable to make intelligent decisions. Modern networks often operate in environments where network resources (e.g. node energy, link quality, bandwidth, etc.), application data (e.g. location of user) and user behaviors (e.g. user mobility and user request pattern) experience changes over time. These changes degrade the network performance and cause service interruption. In recent years, the words "cognitive" and "smart" have become the buzzwords and have been applied to many different networking and communication systems. Cognitive networks are being touted as the next generation network services which will perceive the current network conditions and dynamically adjust their parameters to achieve better productivity. Cognitive radios will provide the end-user intelligence needed for cognitive networks and provide dynamic spectrum access for better spectrum efficiency. We are interested in assessing the practical impact of Cognitive Networks on the Wireless Communication industry. Our goal is to propose a formal business model that will help assess the implications of this new technology in the real world and the practical feasibility of its implementation. We use the layered business model proposed by Ballon [8] which follows a multi-parameter approach by defining four levels on which business models operate and by identifying three critical design parameters on each layer. The Value Network layer identifies the important entities which come into the picture in the light of the new technology. The Functional layer addresses the issue of different architectural implementations of the Cognitive Networks. At the Financial layer, we propose a NPV model which highlights the cost/revenue implications of the technology in the real world and contrasts the different Dynamic Spectrum Access (DSA) schemes from a financial perspective. Finally, the Value Proposition layer seeks to explain the end-user flexibility and efficient spectrum management provided by the use of Cognitive radios and Cognitive networks. / Master of Science

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

The exploding increase of wireless communications combined with the existing inefficient usage of the licensed spectrum gives a strong impetus to the development and standardization of cognitive radio networking and communications. In this dissertation, a framework for Dynamic Spectrum Access (DSA) is first presented, which is the enabling technology for increasing the spectral efficiency of wireless communications. Based on that, Cognitive Radio (CR) can be developed as an enabling technology for supporting the DSA, which means that the wireless users are provided with enhanced capability for sensing the operating radio environment and for exploiting the network side information obtained from this sensing. The DSA concept means that the users of a wireless system are divided into a multi-tiered hierarchy with the primary users (PUs) entitled to protection and with cognitive radio capable secondary users (SUs). The improved spectrum efficiency is obtained by means of a medium access control protocol with knowledge about the statistical properties or available local information of the channels already occupied by PUs as well as knowledge about the interference tolerance within which the interference to PUs is kept to a given level. Related to this, emphasis is laid on the protocol capability to determine the efficiency of the secondary sharing of spectrum. Based on the type of available local information, the capacity of opportunistic communication is investigated for three models. These are: with dynamic, distributed channels information; with dynamic, parallel channels information; and under a dynamic sub-channels allocation scheme. The results indicate that this capacity is robust with reference to the uncertainty associated with localized sensing of distributed dynamic channels and with timely sensing of parallel dynamic channels. The extension to dynamic parallel sub-channels enables resource allocation to be carried out in sub-channels. The analytical results on the performance of sub-channel allocation indicate a robust traffic capacity in terms of blocking probability, drop-out probability and delay performance as function of PUs traffic loads.

"Vehicular Dynamic Spectrum Access (VDSA) combines the advantages of dynamic spectrum access to achieve higher spectrum efficiency and the special mobility pattern of vehicle fleets. This dissertation presents several noval contributions with respect to vehicular communications, especially vehicle-to-vehicle communications. Starting from a system engineering aspect, this dissertation will present several promising future directions for vehicle communications, taking into consideration both the theoretical and practical aspects of wireless communication deployment. This dissertation starts with presenting a feasibility analysis using queueing theory to model and estimate the performance of VDSA within a TV whitespace environment. The analytical tool uses spectrum measurement data and vehicle density to find upper bounds of several performance metrics for a VDSA scenario in TVWS. Then, a framework for optimizing VDSA via artificial intelligence and learning, as well as simulation testbeds that reflect realistic spectrum sharing scenarios between vehicle networks and heterogeneous wireless networks including wireless local area networks and wireless regional area networks. Detailed experimental results justify the testbed for emulating a mobile dynamic spectrum access environment composed of heterogeneous networks with four dimensional mutual interference. Vehicular cooperative communication is the other proposed technique that combines the cooperative communication technology and vehicle platooning, an emerging concept that is expected to both increase highway utilization and enhance both driver experience and safety. This dissertation will focus on the coexistence of multiple vehicle groups in shared spectrum, where intra-group cooperation and inter-group competition are investigated in the aspect of channel access. Finally, a testbed implementation VDSA is presented and a few applications are developed within a VDSA environment, demonstrating the feasibility and benefits of some features in a future transportation system."

"We proposed a novel spectrum allocation approach for distributed cognitive radio networks. Cognitive radio systems are capable of sensing the prevailing environmental conditions and automatically adapting its operating parameters in order to enhance system and network performance. Using this technology, our proposed approach optimizes each individual wireless device and its single-hop communication links using the partial operating parameter and environmental information from adjacent devices within the wireless network. Assuming stationary wireless nodes, all wireless communication links employ non-contiguous orthogonal frequency division multiplexing (NC-OFDM) in order to enable dynamic spectrum access (DSA). The proposed approach will attempt to simultaneously minimize the bit error rate, minimize out-of-band (OOB) interference, and maximize overall throughput using a multi-objective fitness function. Without loss in generality, genetic algorithms are employed to perform the actual optimization. Two generic optimization approaches, subcarrier-wise approach and block-wise approach, were proposed to access spectrum. We also proposed and analyzed several approaches implemented via genetic algorithms (GA), such as quantizing variables, using adaptive variable ranges, and Multi-Objective Genetic Algorithms, for increasing the speed and improving the results of combined spectrum utilization/cross-layer optimization approaches proposed, together with several assisting processes and modifications devised to make the optimization to improve efficiency and execution time."

Dynamic spectrum access based on cognitive radio has been regarded as a prospective solution to improve spectrum utilization for wireless communications. By considering the allocation efficiency, fairness, and economic incentives, spectrum marketing has been attracting more and more attentions in recent years. In this thesis, we focus on one of the most effective spectrum marketing methods, i.e., auction approach, in multi-channel cognitive radio networks. After presenting some fundamentals and related works, we begin our discussion in a recall-based auction system where buyers have various service requirements and the seller could recall some sold items after the auction to deal with a sudden increase of its own demand. Both single-winner and multi-winner auctions are designed and analyzed. In addition, we also consider the heterogeneity of radio resource sellers and formulate a framework of combinatorial spectrum auction. With theoretical analyses and simulation results, we show that our proposed algorithms can improve spectrum utilization while satisfy the heterogeneous requirements of different wireless users.

This thesis explores the use of the Naïve Bayesian classifier as a method of determining high-level information about secondary users in a Dynamic Spectrum Access (DSA) network using a low complexity channel sensing method.  With a growing number of users generating an increased demand for broadband access, determining an efficient method for utilizing the limited available broadband is a developing current and future issue.  One possible solution is DSA, which we simulate using the Universal DSA Network Simulator (UDNS), created by our team at Virginia Tech. However, DSA requires user devices to monitor large amounts of bandwidth, and the user devices are often limited in their acceptable size, weight, and power.  This greatly limits the usable bandwidth when using complex channel sensing methods.  Therefore, this thesis focuses on energy detection for channel sensing. Constraining computing requirements by operating with limited spectrum sensing equipment allows for efficient use of limited broadband by user devices.  The research on using the Naïve Bayesian classifier coupled with energy detection and the UDNS serves as a strong starting point for supplementary work in the area of radio classification. / Master of Science

In this dissertation, we studied and addressed the privacy-preserving and authentication techniques for some network applications, where existing internet security solutions cannot address them straightforwardly due to different trust and attack models and possibly constrained resources. For example, in a centralized dynamic spectrum access (DSA) system, the spectrum resource licensees called incumbent users (IUs), have strong operational privacy requirements for the DSA service provider called spectrum access system (SAS), and hence SAS is required to perform spectrum computation without knowing IUs' operational information. This means SAS can at most be considered as a semi-trusted party which is honest but curious, and common anonymization and end-to-end encryption cannot address this issue, and dedicated solutions are required. Another example is that in an intra-vehicle Controller Area Network (CAN), the transmitter can only embed 64 bits of message and its authentication tag into on message frame, which makes it difficult to achieve message authentication in real-time with sufficient cryptographic strength. The focus of this dissertation is to fill the gap of existing solutions with stronger security notion and practicability. On the topic of privacy-preserving DSA systems, we firstly explored existing solutions and proposed a comparative study. We additionally proposed a new metric for evaluation and showed the advantages and disadvantages of existing solutions. We secondly studied the IU location privacy in 3.5GHz band ESC-based DSA system and proposed a novel scheme called PriDSA. PriDSA addresses malicious colluding SAS attack model through leveraging different and relatively lightweight cryptography primitive with novel design, granting stronger security notion and improved efficiency as well. We thirdly studied the operational privacy of both IU and secondary users (SUs) in a general centralized SAS based DSA system and proposed a novel framework called PeDSS. Through our novel design that integrates differential privacy with secure multi-party computation protocol, PeDSS exhibits great communication and computation overhead compared to existing solutions. On the topic of lightweight message authentication in resource-constrained networks, we firstly explored message authentication schemes with high cryptographic strength and low communication-overhead and proposed a novel scheme called CuMAC. CuMAC provides a flexible trade-off between authentication delay and cryptographic strength, through the embodiment of a novel concept that we refer to as accumulation of cryptographic strength. We secondly explored the possibility of achieving both high cryptographic strength and low authentication delay and proposed a variant of CuMAC called CuMAC/S. By employing the novel idea of message speculation, CuMAC/S achieves enables the accumulation of cryptographic strength while incurring minimal delay when the message speculation accuracy is high. / Doctor of Philosophy / The privacy-preserving and message authentication issues of some network applications are distinctive from common internet security due to different attack models and possibly constrained resources, and these security and privacy concerns cannot be addressed by applying existing internet security solutions straightforwardly. For example, in a centralized dynamic spectrum access (DSA) system, the spectrum resource licensees called incumbent users (IUs), have strong operational privacy requirements for the DSA service provider called spectrum access system (SAS), and hence SAS is required to perform spectrum computation without knowing IUs' operational information. This means SAS can at most be considered as a semi-trusted party which is honest but curious, and common anonymization and end-to-end encryption cannot address this issue, and dedicated solutions are required. Another example is that in an intra-vehicle Controller Area Network (CAN), the transmitter can only embed 64 bits of message and its authentication tag into on message frame, which makes it difficult to achieve message authentication in real-time with sufficient cryptographic strength. We addressed the privacy issue of DSA systems by proposing novel schemes incorporating efficient cryptographic primitives and various privacy-preserving techniques, achieving a greatly higher efficiency or stronger privacy-preserving level. We addressed the lightweight authentication issue of resource-constrained networks by employing the novel concept of security accumulation and message speculation, achieving high cryptographic strength, low communication overhead, and probable low latency.

Yes / A secondary cooperative overlay dynamic spectrum access protocol in cognitive radio networks is proposed, allowing secondary users to access the primary system using full power without causing harmful interference to primary users. Moreover, an enhancement in the primary system will be achieved as a result of secondary relaying of primary messages. A detailed description of the protocol is given and illustrated with network scenarios.

This dissertation considers a deep reinforcement learning (DRL) setting under the practical challenges of real-world wireless communication systems. The non-stationary and partially observable wireless environments make the learning and the convergence of the DRL agent challenging. One way to facilitate learning in partially observable environments is to combine recurrent neural network (RNN) and DRL to capture temporal information inherent in the system, which is referred to as deep recurrent Q-network (DRQN). However, training DRQN is known to be challenging requiring a large amount of training data to achieve convergence. In many targeted wireless applications in the 5G and future 6G wireless networks, the available training data is very limited. Therefore, it is important to develop DRL strategies that are capable of capturing the temporal correlation of the dynamic environment that only requires limited training overhead. In this dissertation, we design efficient DRL frameworks by utilizing echo state network (ESN), which is a special type of RNNs where only the output weights are trained. To be specific, we first introduce the deep echo state Q-network (DEQN) by adopting ESN as the kernel of deep Q-networks. Next, we introduce federated ESN-based policy gradient (Fed-EPG) approach that enables multiple agents collaboratively learn a shared policy to achieve the system goal. We designed computationally efficient training algorithms by utilizing the special structure of ESNs, which have the advantage of learning a good policy in a short time with few training data. Theoretical analyses are conducted for DEQN and Fed-EPG approaches to show the convergence properties and to provide a guide to hyperparameter tuning. Furthermore, we evaluate the performance under the dynamic spectrum sharing (DSS) scenario, which is a key enabling technology that aims to utilize the precious spectrum resources more efficiently. Compared to a conventional spectrum management policy that usually grants a fixed spectrum band to a single system for exclusive access, DSS allows the secondary system to dynamically share the spectrum with the primary system. Our work sheds light on the real deployments of DRL techniques in next generation wireless systems. / Doctor of Philosophy / Model-free reinforcement learning (RL) algorithms such as Q-learning are widely used because it can learn the policy directly through interactions with the environment without estimating a model of the environment, which is useful when the underlying system model is complex. Q-learning performs poorly for large-scale models because the training has to updates every element in a large Q-table, which makes training difficult or even impossible. Therefore, deep reinforcement learning (DRL) exploits the powerful deep neural network to approximate the Q-table. Furthermore, a deep recurrent Q-network (DRQN) is introduced to facilitate learning in partially observable environments. However, DRQN training requires a large amount of training data and a long training time to achieve convergence, which is impractical in wireless systems with non-stationary environments and limited training data. Therefore, in this dissertation, we introduce two efficient DRL approaches: deep echo state Q-network (DEQN) and federated ESN-based policy gradient (Fed-EPG) approaches. Theoretical analyses of DEQN and Fed-EPG are conducted to provide the convergence properties and the guideline for designing hyperparameters. We evaluate and demonstrate the performance benefits of the DEQN and Fed-EPG under the dynamic spectrum sharing (DSS) scenario, which is a critical technology to efficiently utilize the precious spectrum resources in 5G and future 6G wireless networks.