"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."

Recently, with the fast development of wireless communications, the radio spectrum becomes a precious natural resource. Many researches and reports reveal the problems of inefficient spectrum utilization. Cognitive Radio (CR) technology is now developing for solving this critical problem. This technology will enable various kinds of wireless systems to look for and connect radio frequency spectrum that the locality leave unused by oneself, to offer the best service to user. The CR will pass in and out the idle frequency band according to the demand while receiving and dispatching the signal, avoid the frequency band that has been already used. In CR network, the objective is to maximize the throughput of secondary users while limiting the probability of colliding with primary users below a prescribed level. In this paper, we consider a distributed secondary networks model where users seek spectrum opportunities independently that overlaying the primary networks to analyze the system performance and the effect to the primary users with the existence of both primary users and secondary users under the cognitive radio networks. In the cognitive system, due to the existence of noise and fading effect, error detection cannot be avoided. Therefore, we made a comparison to the difference of the efficiency among environments of different probability of miss detection. We also propose a random hopping method for all secondary users in system will re-sensing after a random period of time. Hereby, efficiently decreases the ratio of time that influences the primary users by the secondary users, and further research the factor that influences its efficiency.

Opportunistic spectrum access (OSA) promises to significantly improve the utilization of the RF spectrum. Under OSA, an unlicensed secondary user (SU) is allowed to detect and access under-utilized portions of the licensed spectrum, provided that such operation does not interfere with the communication of licensed primary users (PUs). Cognitive radio (CR) is a key enabling technology of OSA. In this dissertation, we propose several radio resource management and routing mechanisms that optimize the discovery and utilization of spectrum opportunities in a cognitive radio network (CRN). First, we propose a sequential channel sensing and probing mechanism that enables a resource-constrained SU to efficiently identify the optimal transmission opportunity from a pool of potentially usable channels. This mechanism maximizes the SUs expected throughput by accounting for the tradeoff between the reward and overhead of scanning additional channels. The optimal channel sensing and probing process is modeled as a maximum-rate-of-return problem in optimal stopping theory. Operational parameters, such as sensing and probing times, are optimized by exploiting the problem's special structure. Second, we study the problem of coordinated spectrum access in CRNs to maximize the CRNs throughput. By exploiting the geographic relationship between an SU and its surrounding PUs, we propose the novel concept of microscopic spectrum opportunity, in which active SUs and PUs are allowed to operate in the same region, subject to power constraints. Under this framework, we formulate the coordinated channel access problem as a joint power/rate control and channel assignment optimization problem. Centralized and distributed approximate algorithms are proposed to solve this problem efficiently. Compared with its macroscopic counterpart, we show that the microscopic-spectrum-opportunity framework offers significant throughput gains. Finally, at the network layer, we study the problem of truthful least-priced-path (LPP) routing for profit-driven CRNs. We design a route selection and pricing mechanism that guarantees truthful spectrum cost reporting from profit-driven SUs and that finds the cheapest route for end users. The problem is investigated with and without capacity constraints at individual nodes. In both cases, polynomial-time algorithms are developed to solve the LPP problem. Extensive simulations are conducted to verify the validity of the proposed mechanisms.

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.

In September 2016, work was initiated on the subprojects which comprise the Conformal C-Band/Multiband Antenna project: SARD #1: Broadband Conformal C-Band Missile Wraparound Antennas; SARD #2: Beam Switching Array Antennas; SARD #3: Multiband Conformal Antennas for Aircraft Applications; SARD #4: High Altitude Coronal Efforts on Antenna Performance; and SARD #5: Small, Medium Gain Multiband Receive Antennas. A brief status of each of them will be discussed and detail the technology areas being developed by each.

The Conformal C-Band/Multi-band Antenna project will support the AWS-3 auction by providing the technology to integrate C-Band or multi-band telemetry(TM) antennas on test articles such as missiles, weapons, or aircraft. These test articles would then provide C-Band or multi-band TM data to ground station receivers that are relocated to the C-Band frequency range through the AWS-3 Spectrum Relocation Fund program. This project would advance the technology of antennas in the C-Band region for test article TM integration. Successful use of C-Band and Multi-Band antennas for aeronautical mobile telemetry (AMT) on test and training ranges is dependent on the advancement of key technologies. This paper will detail the technology areas being matured by this project as well as the capabilities to be demonstrated.

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

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.

Cognitive radio had been proposed as a methodology for overcoming the inefficiency of the conventional static allocation of the available spectrum in wireless communication networks. The majority of opportunistic spectrum access schemes in cognitive radio networks (CRNs) rely on the Listen-Before-Talk (LBT) model due to the half-duplex nature of conventional wireless radios. However, LBT su ers from the problem of high collision rates and low secondary user throughput if time is misaligned among the secondary users (SUs) and the primary users (PUs). This problem can be mitigated by leveraging full-duplex (FD) communications that facilitate concurrent sensing and transmission. This thesis considers the problem of optimal opportunistic multi-channel spectrum sensing and access using FD radios in the presence of uncertain primary user (PU) activity statistics. A joint learningand spectrum access scheme is proposed. To optimize its throughput, the SU sensing period has to be carefully tuned. However, in absence of exact knowledge of the PU activity statistics, the PU's performance may be adversely a ected. To address this problem, a robust optimization problem is formulated. Analysis shows that under some non-restrictive simplifying assumptions, the robust optimization problem is convex. The impact of sensing periods on the PU collision probability and the SU throughput are analyzed, and the optimal sensing period is found via convex optimization. An "\epsilon-greedy algorithm is proposed for use by the SU to learn the PUs' activity statistics in multichannel networks. It is shown that sublinear regrets can be attained by the proposed estimation and robust optimization strategy. Simulation studies demonstrate that the resulting robust solution achieves a good trade-o between optimizing the SU's throughput and protecting the PU. / Thesis / Master of Applied Science (MASc)