This research focuses on developing a cognitive radio that could operate reliably in unforeseen communications environments like those faced by the disaster and emergency response communities. Cognitive radios may also offer the potential to open up secondary or complimentary spectrum markets, effectively easing the perceived spectrum crunch while providing new competitive wireless services to the consumer. A structure and process for embedding cognition in a radio is presented, including discussion of how the mechanism was derived from the human learning process and mapped to a mathematical formalism called the BioCR. Results from the implementation and testing of the model in a hardware test bed and simulation test bench are presented, with a focus on rapidly deployable disaster communications. Research contributions include developing a biologically inspired model of cognition in a radio architecture, proposing that genetic algorithm operations could be used to realize this model, developing an algorithmic framework to realize the cognition mechanism, developing a cognitive radio simulation toolset for evaluating the behavior the cognitive engine, and using this toolset to analyze the cognitive engineà ­s performance in different operational scenarios. Specifically, this research proposes and details how the chaotic meta-knowledge search, optimization, and machine learning properties of distributed genetic algorithm operations could be used to map this model to a computable mathematical framework in conjunction with dynamic multi-stage distributed memories. The system formalism is contrasted with existing cognitive radio approaches, including traditionally brittle artificial intelligence approaches. The cognitive engine architecture and algorithmic framework is developed and introduced, including the Wireless Channel Genetic Algorithm (WCGA), Wireless System Genetic Algorithm (WSGA), and Cognitive System Monitor (CSM). Experimental results show that the cognitive engine finds the best tradeoff between a host radio's operational parameters in changing wireless conditions, while the baseline adaptive controller only increases or decreases its data rate based on a threshold, often wasting usable bandwidth or excess power when it is not needed due its inability to learn. Limitations of this approach include some situations where the engine did not respond properly due to sensitivity in algorithm parameters, exhibiting ghosting of answers, bouncing back and forth between solutions. Future research could be pursued to probe the limits of the engineà ­s operation and investigate opportunities for improvement, including how best to configure the genetic algorithms and engine mathematics to avoid engine solution errors. Future research also could include extending the cognitive engine to a cognitive radio network and investigating implications for secure communications. / Ph. D.

Recent disasters, including the 9/11 terrorist attacks, Hurricane Katrina, the London subway bombings, and the California wildfires, have all highlighted the limitations of current mobile communication systems for public safety first responders. First, in a point-to-point configuration, legacy radio systems used by first responders from differing agencies are often made by competing manufacturers and may use incompatible waveforms or channels. In addition, first responder radio systems, which may be licensed and programmed to operate in frequency bands allocated within their home jurisdiction, may be neither licensed nor available in forward-deployed disaster response locations, resulting in an operational scarcity of usable frequencies. To address these problems, first responders need smart radio solutions which can bridge these disparate legacy radio systems together, can incorporate new smart radio solutions, or can replace these existing aging radios. These smart radios need to quickly find each other and adhere to spectrum usage and access policies. Second, in an infrastructure configuration, legacy radio systems may not operate at all if the existing communications backbone has been destroyed by the disaster event. A communication system which can provide a new, temporary infrastructure or can extend an existing infrastructure into a shaded region is needed. Smart radio nodes that make up the public safety infrastructure again must be able to find each other, adhere to spectrum usage policies, and provide access to other smart radios and legacy public safety radios within their coverage area. This work addresses these communications problems in the following ways. First, it applies cognitive radio technology to develop a smart radio system capable of rapidly adapting itself so it can communicate with existing legacy radio systems or other smart radios using a variety of standard and customized waveforms. These smart radios can also assemble themselves into an ad-hoc network capable of providing a temporary communications backbone within the disaster area, or a network extension to a shaded communications area. Second, this work analyzes and characterizes a series of rendezvous protocols which enable the smart radios to rapidly find each other within a particular coverage area. Third, this work develops a spectrum sharing protocol that enables the smart radios to adhere to spectral policies by sharing spectrum with other primary users of the band. Fourth, the performance of the smart radio architecture, as well as the performance of the rendezvous and spectrum sharing protocols, is evaluated on a smart radio network testbed, which has been assembled in a laboratory setting. Results are compared, when applicable, to existing radio systems and protocols. Finally, this work concludes by briefly discussing how the smart radio technologies developed in this dissertation could be combined to form a public safety communications architecture, applicable to the FCC's stated intent for the 700 MHz Band. In the future, this work will be extended to applications outside of the public safety community, specifically, to communications problems faced by warfighters in the military. / Ph. D.

Software Defined Radios (SDRs) lend themselves to flexibility and extensibility because they<br />depend on software to implement radio functionality. Cognitive Engines (CEs) introduce<br />intelligence to radio by monitoring radio performance through a set of meters and configuring<br />the underlying radio design by modifying its knobs. In Cognitive Radio (CR) applications,<br />CEs intelligently monitor radio performance and reconfigure them to meet it application<br />and RF channel needs. While the issue of introducing computational knobs and meters<br />is mentioned in literature, there has been little work on the practical issues involved in<br />introducing such computational radio controls.<br /><br />This dissertation decomposes the radio definition to reactive models for the CE domain<br />and real-time, or dataflow models, for the SDR domain. By allowing such design space<br />decomposition, CEs are able to define implementation independent radio graphs and rely on<br />a model transformation layer to transform reactive radio models to real-time radio models<br />for implementation. The definition of knobs and meters in the CE domain is based on<br />properties of the dataflow models used in implementing SDRs. A framework for developing<br />this work is presented, and proof of concept radio applications are discussed to demonstrate<br />how CEs can gain insight into computational aspects of their radio implementation during<br />their reconfiguration decision process.<br /> / Ph. D.

This dissertation presents the theory, design, implementation and successful deployment of a cognitive engine decision algorithm by which a cognitive radio-equipped mobile robot may adapt its motion and radio parameters through multi-objective optimization. This provides a proof-of-concept prototype cognitive system that is aware of its environment, its user's needs, and the rules governing its operation. It is to take intelligent action based on this awareness to optimize its performance across both the mobility and radio domains while learning from experience and responding intelligently to ongoing environmental mission changes. The prototype combines the key features of cognitive radios and autonomous vehicles into a single package whose behavior integrates the essential features of both. The use case for this research is a scenario where a small unmanned aerial vehicle (UAV) is traversing a nominally cyclic or repeating flight path (an â •orbitâ •) seeking to observe targets and where possible avoid hostile agents. As the UAV traverses the path, it experiences varying RF effects, including multipath propagation and terrain shadowing. The goal is to provide the capability for the UAV to learn the flight path with respect both to motion and RF characteristics and modify radio parameters and flight characteristics proactively to optimize performance. Using sensor fusion techniques to develop situational awareness, the UAV should be able to adapt its motion or communication based on knowledge of (but not limited to) physical location, radio performance, and channel conditions. Using sensor information from RF and mobility domains, the UAV uses the mission objectives and its knowledge of the world to decide on a course of action. The UAV develops and executes a multi-domain action; action that crosses domains, such as changing RF power and increasing its speed. This research is based on a simple observation, namely that cognitive radios and autonomous vehicles perform similar tasks, albeit in different domains. Both analyze their environment, make and execute a decision, evaluate the result (learn from experience), and repeat as required. This observation led directly to the creation of a single intelligent agent combining cognitive radio and autonomous vehicle intelligence with the ability to leverage flexibility in the radio frequency (RF) and motion domains. Using a single intelligent agent to optimize decision making across both mobility and radio domains is unified multi-domain decision making (UMDDM). / Ph. D.

Dynamic Cellular Cognitive System (DCCS) serves as a cognitive network for white space devices in TV white space. It is also designed to provide quality communications for first responders in area with damaged wireless communication infrastructure. In DCCS network, diverse types of communication devices interoperate, communicate, and cooperate with high spectrum efficiency in a Dynamic Spectrum Access (DSA) scenario. DCCS can expand to a broad geographical distribution via linking to existing infrastructure. DCCS can quickly form a network to accommodate a diverse set of devices in natural disaster areas. It can also recover the infrastructure in a blind spot, for example, a subway or mountain area. Its portability and low cost make it feasible for commercial applications. This dissertation starts with an overview of DCCS network. DCCS defines a cognitive radio network and a set of protocols that each cognitive radio node inside the network must adopt to function as a user within the group. Multiple secondary users cooperate based on a fair and efficient scheme without losing the flexibility and self adaptation features. The basic unit of DCCS is a cell. A set of protocols and algorithms are defined to meet the communication requirement for intra-cell communications. DCCS includes multiple layers and multiple protocols. This dissertation gives a comprehensive description and analysis of building a DCCS network. It covers the network architecture, physical and Medium Access Control (MAC) layers for data and command transmission, spectrum management in DSA scenario, signal classification and synchronization and describes a working prototype of DCCS. Two key technologies of intra-cell communication are spectrum management and Universal Classification and Synchronization (UCS). A channel allocation algorithm based on calculating the throughput of an available is designed and the performance is analyzed. UCS is conceived as a self-contained system which can detect, classify, and synchronize with a received signal and extract all parameters needed for physical layer demodulation. It enables the accommodation of non-cognitive devices and improves communication quality by allowing a cognitive receiver to track physical layer changes at the transmitter. Inter-cell communications are the backhaul connections of DCCS. This dissertation discusses two approaches to obtaining spectrum for inter-cell communications. A temporary leasing approach focuses on the policy aspects, and the other approach is based on using OFDMA to combine separate narrowband channels into a wideband channel that can meet the inter-cell communications throughput requirements. A prototype of DCCS implemented on GNU radio and USRP platform is included in the dissertation. It serves as the proof of concept of DCCS. / Ph. D.

This dissertation introduces the concept of an artificial society based on the use of an action based social language combined with the behavior-based approach to the construction of multi-agent systems to address the problem of developing decentralized, self-organizing networks that dynamically fit into their environment. In the course of accomplishing this, social language is defined as an efficient method for communicating coordination information among cognitive radios inspired by natural societies. This communication method connects the radios within a network in a way that allows the network to learn in a distributed holistic manner. The behavior-based approach to developing multi-agent systems from the field of robotics provides the framework for developing these learning networks. In this approach several behaviors are used to address the multiple objectives of a cognitive radio society and then combined to achieve emergent properties and behaviors. This work presents a prototype cognitive radio society. This society is implemented, using low complexity hardware, and evaluated. The work does not focus on the development of optimized techniques, but rather the complementary design of techniques and agents to create dynamic, decentralized self-organizing networks / Ph. D.

With interoperability issues plaguing emergency responders throughout the country, Cognitive Radio (CR) offers a unique solution to streamline communication between police, Emergency Medical Technicians (EMT), and military officers. Using Software Defined Radio (SDR) technology, a flexible radio platform can be potentially configured using a Cognitive Engine (CE) to transmit and receive many different incompatible radio standards. In this thesis, an interface between a Cognitive Engine and an SDR platform is described which modifies (i.e., configures) the radio's operation. The interface is based upon communicating information via eXtensible Markup Language (XML) data files that contain the radio's Physical (PHY) parameters. The XML data files have been designed such that more development can be made to its structure as this research develops. The GNU Radio and the Universal Software Radio Peripheral (USRP) serve as the SDR platform for an example implementation. The example implementation involves importing XML data files into the SDR for quick configuration. Three configuration examples are used to describe this process. / Master of Science

The Universal Classifier Synchronizer (UCS) is a Cognitive Radio system/sensor that can detect, classify, and extract the relevant parameters from a received signal to establish physical layer communications using the received signal's profile. The current implementation is able to identify signals including AM, FM, MPSK, QAM, MFSK, and OFDM. The system is constructed to run on a Universal Software Radio Peripheral (USRP) with the GNU Radio software toolkit and also runs on an Anritsu™ signal analyzer. In both prototypes, the UCS system runs on a host computer's General Purpose Processor (GPP) and is constructed in Matlab™. The aim is to then create a portable and standalone version of the UCS system as an intermediate step towards building a future commercial implementation. This application and particular implementation aims to run on a Lyrtech SFF SDR platform and uses its FPGA and DSP modules for implementation. This platform is one of the more advanced SDR platforms available, and the aim is to develop parts of the UCS system to run on this platform. The aim is to eventually develop the complete UCS cognitive radio system on the Lyrtech SFF SDR platform that can act as a standalone portable cognitive radio system. The modules created and implanted/implemented on the SDR hardware are the Bandwidth Estimation, and Symbol Timing & Coarse Classification modules. This is the system decision path towards classification, synchronization, and demodulation of digital phase modulated signals (QAM and MPSK signal types) and also analog signals. The Digital Receiver Module (DRM) is implemented on the FPGA and takes care of all the digital down conversions, mixing, decimation, and low pass filtering. The FPGA is connected to the DSP module via a bus subsystem where the DSP receives real-time base-band complex IQ samples for further signal processing. The main UCS algorithm runs on the platform's DSP and is compiled from executable embedded C-code. Therefore, this system can then be implemented on virtually any setup that has an RF front end, digital receiver module, and processing module that will execute floating and fixed point C-code with minor changes. / Master of Science

The Virginia Tech Universal Classifier Synchronizer (UCS) system can enable a cognitive receiver to detect, classify and extract all the parameters needed from a received signal for physical layer demodulation and configure a cognitive radio accordingly. Currently, UCS can process analog amplitude modulation (AM) and frequency modulation (FM) and digital narrow band M-PSK, M-QAM and wideband signal orthogonal frequency division multiplexing (OFDM). A fully developed prototype of UCS system was designed and implemented in our laboratory using GNU radio software platform and Universal Software Radio Peripheral (USRP) radio platform. That system introduces a lot of latency issues because of the limited USB data transfer speeds between the USRP and the host computer. Also, there are inherent latencies and timing uncertainties in the General Purpose Processor (GPP) software itself. Solving the timing and latency problems requires running key parts of the software-defined radio (SDR) code on a Field Programmable Gate Array (FPGA)/Digital Signal Processor (DSP)/GPP based hybrid platform. Our objective is to port the entire UCS system on the Lyrtech SFF SDR platform which is a hybrid DSP/FPGA/GPP platform. Since the FPGA allows parallel processing on a wideband signal, its computing speed is substantially faster than GPPs and most DSPs, which sequentially process signals. In addition, the Lyrtech Small Form Factor (SFF)-SDR development platform integrates the FPGA and the RF module on one platform; this further reduces the latency in moving signals from RF front end to the computing component. Also for UCS to be commercially viable, we need to port it to a more portable platform which can be transitioned to a handset radio in the future. This thesis is a proof of concept implementation of the coarse classifier which is the first step of classification. Both fixed point and floating point implementations are developed and no compiler specific libraries or vendor specific libraries are used. This makes transitioning the design to any other hardware like GPPs and DSPs of other vendors possible without having to change the basic framework and design. / Master of Science

No / Opportunistic secondary usage of underutilised radio spectrum is currently of great interest and the use of TV White Spaces (TVWS) has been considered for Long Term Evolution (LTE) broadband services. However, wireless microphones operating in TV bands pose a challenge to TVWS opportunistic access. Efficient and proactive spectrum sensing could prevent harmful interference between collocated devices, but existing spectrum sensing schemes such as energy detection and schemes based on Random Matrix Theory (RMT) have performance limitations. We propose a new blind spectrum sensing scheme with higher performance based on RMT supported by a new formula for the estimation of noise variance. The performance of the proposed scheme has been evaluated through extensive simulations on wireless microphone signals. The proposed scheme has also been compared to energy detection schemes, and shows higher performance in terms of the probability of false alarm (Pfa) and probability of detection (Pd).