VTechWorks Repository :: Browsing by Author "MacKenzie, Allen B."

Browsing by Author "MacKenzie, Allen B."

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Analysis and Design of Cognitive Radio Networks and Distributed Radio Resource Management Algorithms
Neel, James O'Daniell (Virginia Tech, 2006-09-06)
Cognitive radio is frequently touted as a platform for implementing dynamic distributed radio resource management algorithms. In the envisioned scenarios, radios react to measurements of the network state and change their operation according to some goal driven algorithm. Ideally this flexibility and reactivity yields tremendous gains in performance. However, when the adaptations of the radios also change the network state, an interactive decision process is spawned and once desirable algorithms can lead to catastrophic failures when deployed in a network. This document presents techniques for modeling and analyzing the interactions of cognitive radio for the purpose of improving the design of cognitive radio and distributed radio resource management algorithms with particular interest towards characterizing the algorithms' steady-state, convergence, and stability properties. This is accomplished by combining traditional engineering and nonlinear programming analysis techniques with techniques from game to create a powerful model based approach that permits rapid characterization of a cognitive radio algorithm's properties. Insights gleaned from these models are used to establish novel design guidelines for cognitive radio design and powerful low-complexity cognitive radio algorithms. This research led to the creation of a new model of cognitive radio network behavior, an extensive number of new results related to the convergence, stability, and identification of potential and supermodular games, numerous design guidelines, and several novel algorithms related to power control, dynamic frequency selection, interference avoidance, and network formation. It is believed that by applying the analysis techniques and the design guidelines presented in this document, any wireless engineer will be able to quickly develop cognitive radio and distributed radio resource management algorithms that will significantly improve spectral efficiency and network and device performance while removing the need for significant post-deployment site management.
Antenna Selection for a Public Safety Cognitive Radio
Hugine, Akilah L. (Virginia Tech, 2006-05-10)
Ever since the dawn of radio communication systems, the antenna has been the key component in the construction and performance of every wireless system. With the proliferation of new radio systems, a cognitive radio is a radio that has the capability to sense, learn, and autonomously adapt to its environment. The hardware components are essential to optimizing performance. Antenna hardware for cognitive radio applications presents distinctive problems, since in theoretical terms, a cognitive radio can operate anywhere in the spectrum. The purpose of this thesis is to investigate a particular type of cognitive radio system and examine the potential affects the antenna will have on the system. The thesis will provide an overview of fundamental antenna properties, the performance characteristics of the particular antenna used in this research, and the system characteristics when the antenna is integrated. This thesis will also illustrate how the antenna and its properties affect the overall public safety cognitive radio performance. This information can be used to establish antenna selection criteria for optimum system performance.
Application of Artificial Intelligence to Wireless Communications
Rondeau, Thomas Warren (Virginia Tech, 2007-09-20)
This dissertation provides the theory, design, and implementation of a cognitive engine, the enabling technology of cognitive radio. A cognitive radio is a wireless communications device capable of sensing the environment and making decisions on how to use the available radio resources to enable communications with a certain quality of service. The cognitive engine, the intelligent system behind the cognitive radio, combines sensing, learning, and optimization algorithms to control and adapt the radio system from the physical layer and up the communication stack. The cognitive engine presented here provides a general framework to build and test cognitive engine algorithms and components such as sensing technology, optimization routines, and learning algorithms. The cognitive engine platform allows easy development of new components and algorithms to enhance the cognitive radio capabilities. It is shown in this dissertation that the platform can easily be used on a simulation system and then moved to a real radio system. The dissertation includes discussions of both theory and implementation of the cognitive engine. The need for and implementation of all of the cognitive components is strongly featured as well as the specific issues related to the development of algorithms for cognitive radio behavior. The discussion of the theory focuses largely on developing the optimization space to intelligently and successfully design waveforms for particular quality of service needs under given environmental conditions. The analysis develops the problem into a multi-objective optimization process to optimize and trade-of of services between objectives that measure performance, such as bit error rate, data rate, and power consumption. The discussion of the multi-objective optimization provides the foundation for the analysis of radio systems in this respect, and through this, methods and considerations for future developments. The theoretical work also investigates the use of learning to enhance the cognitive engine's capabilities through feed-back, learning, and knowledge representation. The results of this work include the analysis of cognitive radio design and implementation and the functional cognitive engine that is shown to work in both simulation and on-line experiments. Throughout, examples and explanations of building and interfacing cognitive components to the cognitive engine enable the use and extension of the cognitive engine for future work.
An Approach to Using Cognition in Wireless Networks
Morales-Tirado, Lizdabel (Virginia Tech, 2009-12-18)
Third Generation (3G) wireless networks have been well studied and optimized with traditional radio resource management techniques, but still there is room for improvement. Cognitive radio technology can bring significantcant network improvements by providing awareness to the surrounding radio environment, exploiting previous network knowledge and optimizing the use of resources using machine learning and artificial intelligence techniques. Cognitive radio can also co-exist with legacy equipment thus acting as a bridge among heterogeneous communication systems. In this work, an approach for applying cognition in wireless networks is presented. Also, two machine learning techniques are used to create a hybrid cognitive engine. Furthermore, the concept of cognitive radio resource management along with some of the network applications are discussed. To evaluate the proposed approach cognition is applied to three typical wireless network problems: improving coverage, handover management and determining recurring policy events. A cognitive engine, that uses case-based reasoning and a decision tree algorithm is developed. The engine learns the coverage of a cell solely from observations, predicts when a handover is necessary and determines policy patterns, solely from environment observations.
Approaches to Joint Base Station Selection and Adaptive Slicing in Virtualized Wireless Networks
Teague, Kory Alan (Virginia Tech, 2018-11-19)
Wireless network virtualization is a promising avenue of research for next-generation 5G cellular networks. This work investigates the problem of selecting base stations to construct virtual networks for a set of service providers, and adaptive slicing of the resources between the service providers to satisfy service provider demands. A two-stage stochastic optimization framework is introduced to solve this problem, and two methods are presented for approximating the stochastic model. The first method uses a sampling approach applied to the deterministic equivalent program of the stochastic model. The second method uses a genetic algorithm for base station selection and adaptively slicing via a single-stage linear optimization problem. A number of scenarios are simulated using a log-normal model designed to emulate demand from real world cellular networks. Simulations indicate that the first approach can provide a reasonably tight solution, but is constrained as the time expense grows exponentially with the number of parameters. The second approach provides a significant improvement in run time with the introduction of marginal error.
Automatic Modulation Classication and Blind Equalization for Cognitive Radios
Ramkumar, Barathram (Virginia Tech, 2011-07-28)
Cognitive Radio (CR) is an emerging wireless communications technology that addresses the inefficiency of current radio spectrum usage. CR also supports the evolution of existing wireless applications and the development of new civilian and military applications. In military and public safety applications, there is no information available about the signal present in a frequency band and hence there is a need for a CR receiver to identify the modulation format employed in the signal. The automatic modulation classifier (AMC) is an important signal processing component that helps the CR in identifying the modulation format employed in the detected signal. AMC algorithms developed so far can classify only signals from a single user present in a frequency band. In a typical CR scenario, there is a possibility that more than one user is present in a frequency band and hence it is necessary to develop an AMC that can classify signals from multiple users simultaneously. One of the main objectives of this dissertation is to develop robust multiuser AMC's for CR. It will be shown later that multiple antennas are required at the receiver for classifying multiple signals. The use of multiple antennas at the transmitter and receiver is known as a Multi Input Multi Output (MIMO) communication system. By using multiple antennas at the receiver, apart from classifying signals from multiple users, the CR can harness the advantages offered by classical MIMO communication techniques like higher data rate, reliability, and an extended coverage area. While MIMO CR will provide numerous benefits, there are some significant challenges in applying conventional MIMO theory to CR. In this dissertation, open problems in applying classical MIMO techniques to a CR scenario are addressed. A blind equalizer is another important signal processing component that a CR must possess since there are no training or pilot signals available in many applications. In a typical wireless communication environment the transmitted signals are subjected to noise and multipath fading. Multipath fading not only affects the performance of symbol detection by causing inter symbol interference (ISI) but also affects the performance of the AMC. The equalizer is a signal processing component that removes ISI from the received signal, thus improving the symbol detection performance. In a conventional wireless communication system, training or pilot sequences are usually available for designing the equalizer. When a training sequence is available, equalizer parameters are adapted by minimizing the well known cost function called mean square error (MSE). When a training sequence is not available, blind equalization algorithms adapt the parameters of the blind equalizer by minimizing cost functions that exploit the higher order statistics of the received signal. These cost functions are non convex and hence the blind equalizer has the potential to converge to a local minimum. Convergence to a local minimum not only affects symbol detection performance but also affects the performance of the AMC. Robust blind equalizers can be designed if the performance of the AMC is also considered while adapting equalizer parameters. In this dissertation we also develop Single Input Single Output (SISO) and MIMO blind equalizers where the performance of the AMC is also considered while adapting the equalizer parameters.
Autonomous Link-Adaptive Schemes for Heterogeneous Networks with Congestion Feedback
Ahmad, Syed Amaar (Virginia Tech, 2014-03-19)
LTE heterogeneous wireless networks promise significant increase in data rates and improved coverage through (i) the deployment of relays and cell densification, (ii) carrier aggregation to enhance bandwidth usage and (iii) by enabling nodes to have dual connectivity. These emerging cellular networks are complex and large systems which are difficult to optimize with centralized control and where mobiles need to balance spectral efficiency, power consumption and fairness constraints. In this dissertation we focus on how decentralized and autonomous mobiles in multihop cellular systems can optimize their own local objectives by taking into account end-to-end or network-wide conditions. We propose several link-adaptive schemes where nodes can adjust their transmit power, aggregate carriers and select points of access to the network (relays and/or macrocell base stations) autonomously, based on both local and global conditions. Under our approach, this is achieved by disseminating the dynamic congestion level in the backhaul links of the points of access. As nodes adapt locally, the congestion levels in the backhaul links can change, which can in turn induce them to also change their adaptation objectives. We show that under our schemes, even with this dynamic congestion feedback, nodes can distributedly converge to a stable selection of transmit power levels and points of access. We also analytically derive the transmit power levels at the equilibrium points for certain cases. Moreover, through numerical results we show that the corresponding system throughput is significantly higher than when nodes adapt greedily following traditional link layer optimization objectives. Given the growing data rate demand, increasing system complexity and the difficulty of implementing centralized cross-layer optimization frameworks, our work simplifies resource allocation in heterogeneous cellular systems. Our work can be extended to any multihop wireless system where the backhaul link capacity is limited and feedback on the dynamic congestion levels at the access points is available.
Average Link Rate Analysis over Finite Time Horizon in a Wireless Network
Bodepudi, Sai Nisanth (Virginia Tech, 2017-03-30)
Instantaneous and ergodic rates are two of the most commonly used metrics to characterize throughput of wireless networks. Roughly speaking, the former characterizes the rate achievable in a given time slot, whereas the latter is useful in characterizing average rate achievable over a long time period. Clearly, the reality often lies somewhere in between these two extremes. Consequently, in this work, we define and characterize a more realistic N-slot average rate (achievable rate averaged over N time slots). This N-slot average rate metric refines the popular notion of ergodic rate, which is defined under the assumption that a user experiences a complete ensemble of channel and interference conditions in the current session (not always realistic, especially for short-lived sessions). The proposed metric is used to study the performance of typical nodes in both ad hoc and downlink cellular networks. The ad hoc network is modeled as a Poisson bipolar network with a fixed distance between each transmitter and its intended receiver. The cellular network is also modeled as a homogeneous Poisson point process. For both these setups, we use tools from stochastic geometry to derive the distribution of N-slot average rate in the following three cases: (i) rate across N time slots is completely correlated, (ii) rate across N time slots is independent and identically distributed, and (iii) rate across N time slots is partially correlated. While the reality is close to third case, the exact characterization of the first two extreme cases exposes certain important design insights.
Backpressure Policies for Wireless ad hoc Networks
Shukla, Umesh Kumar (Virginia Tech, 2010-03-16)
Interference in ad hoc wireless networks causes the performance of traditional networking protocols to suffer. However, some user applications in ad hoc networks demand high throughput and low end-user delay. In the literature, the backpressure policy, i.e. queue backlog differential-based joint routing and scheduling, is known to be throughput-optimal with robust support for traffic load fluctuations \cite{Tssailus92}. Unfortunately, many backpressure-based algorithms cannot be implemented due to high end-user delay, inaccurate assumptions for interference, and high control overhead in distributed scenarios. We develop new backpressure based approaches to address these issues. We first propose a heuristic packet forwarding scheme that solves the issue of high end-user delay and still provides near-optimal throughput. Next we develop a novel interference model that provides simple yet accurate interference relationships among users. Such a model is helpful in designing a simple backpressure scheduling algorithm that does not violate realistic interference constraints. Finally we develop distributed backpressure algorithms based on our proposed ideas. Our distributed algorithms provide throughput performance close to the optimal and have low control overhead and simple implementation.
Behavior-based Incentives for Node Cooperation in Wireless Ad Hoc Networks
Srivastava, Vivek (Virginia Tech, 2008-09-17)
A Mobile Ad Hoc Network (MANET) adopts a decentralized communication architecture which relies on cooperation among nodes at each layer of the protocol stack. Its reliance on cooperation for success and survival makes the ad hoc network particularly sensitive to variations in node behavior. Specifically, for functions such as routing, nodes which are limited in their resources may be unwilling to cooperate in forwarding for other nodes. Such selfish behavior leads to degradation in the performance of the network and possibly, in the extreme case, a complete cessation of operations. Consequently it is important to devise solutions to encourage resource-constrained nodes to cooperate. Incentive schemes have been proposed to induce selfish nodes to cooperate. Though many of the proposed schemes in the literature are payment-based, nodes can be incentivized to cooperate by adopting policies which are non-monetary in nature, but rather are based on the threat of retaliation for non-cooperating nodes. These policies, for which there is little formal analysis in the existing literature on node cooperation, are based on observed node behavior. We refer to them as behavior-based incentives. In this work, we analyze the effectiveness of behavior-based incentives in inducing nodes to cooperate. To determine whether an incentive scheme is effective in fostering cooperation we develop a game-theoretic model. Adopting a repeated game model, we show that nodes may agree to cooperate in sharing their resources and forward packets, even if they perceive a cost in doing so. This happens as the nodes recognize that refusing to cooperate will result in similar behavior by others, which ultimately would compromise the viability of the network as a whole. A major shortcoming in the analysis done in past works is the lack of consideration of practical constraints imposed by an ad hoc environment. One such example is the assumption that a node, when making decisions about whether to cooperate, has perfect knowledge of every other node's actions. In a distributed setting this is impractical. In our work, we analyze behavior-based incentives by incorporating such practical considerations as imperfect monitoring into our game-theoretic models. In modeling the problem as a game of imperfect public monitoring (nodes observe a common public signal that reflects the actions of other nodes in the network) we show that, under the assumption of first order stochastic dominance of the public signal, the grim trigger strategy leads to an equilibrium for nodes to cooperate. Even though a trigger-based strategy like grim-trigger is effective in deterring selfish behavior it is too harsh in its implementation. In addition, the availability of a common public signal in a distributed setting is rather limited. We, therefore, consider nodes that individually monitor the behavior of other nodes in the network and keep this information private. Note that this independent monitoring of behavior is error prone as a result of slow switching between transmit and promiscuous modes of operation, collisions and congestion due to the wireless medium, or incorrect feedback from peers. We propose a probability-based strategy that induces nodes to cooperate under such a setting. We analyze the strategy using repeated games with imperfect private monitoring and show it to be robust to errors in monitoring others" actions. Nodes achieve a near-optimal payoff at equilibrium when adopting this strategy. This work also characterizes the effects of a behavior-based incentive, applied to induce cooperation, on topology control in ad hoc networks. Our work is among the first to consider selfish behavior in the context of topology control. We create topologies based on a holistic view of energy consumption " energy consumed in forwarding packets as well as in maintaining links. Our main results from this work are to show that: (a) a simple forwarding policy induces nodes to cooperate and leads to reliable paths in the generated topology, (b) the resulting topologies are well-connected, energy-efficient and exhibit characteristics similar to those in small-world networks.
Building a Cognitive Radio: From Architecture Definition to Prototype Implementation
Le, Bin (Virginia Tech, 2007-06-11)
Cognitive radio (CR) technology introduces a revolutionary wireless communication mechanism in terminals and network segments, so that they are able to learn their environment and adapt intelligently to the most appropriate way of providing the service for the user's exact need. By supporting multi-band, mode-mode cognitive applications, the cognitive radio addresses an interactive way of managing the spectrum that harmonizes technology, market and regulation. This dissertation gives a complete story of building a cognitive radio. It goes through concept clarification, architecture definition, functional block building, system integration, and finally to the implementation of a fully-functional cognitive radio node prototype that can be directly packaged for application use. This dissertation starts with a comprehensive review of CR research from its origin to today. Several fundamental research issues are then addressed to let the reader know what makes CR a challenging and interesting research area. Then the CR system solution is introduced with the details of its hierarchical functional architecture called the Egg Model, modular software system called the cognitive engine, and the kernel machine learning mechanism called the cognition cycle. Next, this dissertation discusses the design of specific functional building blocks which incorporate environment awareness, solution making, and adaptation. These building blocks are designed to focus on the radio domain that mainly concerns the radio environment and the radio platform. Awareness of the radio environment is achieved by extracting the key environmental features and applying statistical pattern recognition methods including artificial neural networks and k-nearest neighbor clustering. Solutions for the radio behavior are made according to the recognized environment and the previous knowledge through case based reasoning, and further adapted or optimized through genetic algorithm solution search. New experiences are gained through the practice of the new solution, and thus the CR's knowledge evolves for future use; therefore, the CR's performance continues improving with this reinforcement learning approach. To deploy the solved solution in terms of the radio's parameters, a platform independent radio interface is designed. With this general radio interface, the algorithms in the cognitive engine software system can be applied to various radio hardware platforms. To support and verify designed cognitive algorithms and cognitive functionalities, a complete reconfigurable SDR platform, called the CWT2 waveform framework, is designed in this dissertation. In this waveform framework, a hierarchical configuration and control system is constructed to support flexible, real-time waveform reconfigurability. Integrating all the building blocks described above allows a complete CR node system. Based on this general CR node structure, a fully-functional Public Safety Cognitive Radio (PSCR) node is prototyped to provide the universal interoperability for public safety communications. Although the complete PSCR node software system has been packaged to an official release including installation guide and user/developer manuals, the process of building a cognitive radio from concept to a functional prototype is not the end of the CR research; on-going and future research issues are addressed in the last chapter of the dissertation.
Building a Dynamic Spectrum Access Smart Radio With Application to Public Safety Disaster Communications
Silvius, Mark D. (Virginia Tech, 2009-08-13)
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.
Classification and Parameter Estimation of Asynchronously Received PSK/QAM Modulated Signals in Flat-Fading Channels
Headley, William C. (Virginia Tech, 2009-04-30)
One of the fundamental hurdles in realizing new spectrum sharing allocation policies is that of reliable spectrum sensing. In this thesis, three research thrusts are presented in order to further research in this critical area. The first of these research thrusts is the development of a novel asynchronous and noncoherent modulation classifier for PSK/QAM modulated signals in flat-fading channels. In developing this classifier, a novel estimator for the unknown channel gain and fractional time delay is proposed which uses a method-of-moments based estimation approach. For the second research thrust of this thesis, the developed method-of-moments based estimation approach is extended to estimate the signal-to-noise ratio of PSK/QAM modulated signals in flat-fading channels, in which no a priori knowledge of the modulation format and channel parameters is assumed. Finally, in the third research thrust, a distributed spectrum sensing approach is proposed in which a network of radios collaboratively detects the presence, as well as the modulation scheme, of a signal through the use of a combination of cyclic spectrum feature-based signal classification and an iterative algorithm for optimal data fusion.
Coarse Radio Signal Classifier on a Hybrid FPGA/DSP/GPP Platform
Nair, Sujit S. (Virginia Tech, 2009-12-07)
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.
Cognitive Gateway to Promote Interoperability, Coverage and Throughput in Heterogeneous Communication Systems
Chen, Qinqin (Virginia Tech, 2009-12-08)
With the reality that diverse air interfaces and dissimilar access networks coexist, accompanied by the trend that dynamic spectrum access (DSA) is allowed and will be gradually employed, cognition and cooperation form a promising framework to achieve the ideality of seamless ubiquitous connectivity in future communication networks. In this dissertation, the cognitive gateway (CG), conceived as a special cognitive radio (CR) node, is proposed and designed to facilitate universal interoperability among incompatible waveforms. A proof-of-concept prototype is built and tested. Located in places where various communication nodes and diverse access networks coexist, the CG can be easily set up and works like a network server with differentiated service (Diffserv) architecture to provide automatic traffic relaying and link establishment. The author extracts a scalable '“source-CG-destination“ snapshot from the entire network and investigates the key enabling technologies for such a snapshot. The CG features provide universal interoperability, which is enabled by a generic waveform representation format and the reconfigurable software defined radio platform. According to the trend of an all IP-based solution for future communication systems, the term “waveform“ in this dissertation has been defined as a protocol stack specification suite. The author gives a generic waveform representation format based on the five-layer TCP/IP protocol stack architecture. This format can represent the waveforms used by Ethernet, WiFi, cellular system, P25, cognitive radios etc. A significant advantage of CG over other interoperability solutions lies in its autonomy, which is supported by appropriate signaling processes and automatic waveform identification. The service process in a CG is usually initiated by the users who send requests via their own waveforms. These requests are transmitted during the signaling procedures. The complete operating procedure of a CG is depicted as a waveform-oriented cognition loop, which is primarily executed by the waveform identifier, scenario analyzer, central controller, and waveform converter together. The author details the service process initialized by a primary user (e.g. legacy public safety radio) and that initialized by a secondary user (e.g. CR), and describes the signaling procedures between CG and clients for the accomplishment of CG discovery, user registration and un-registration, link establishment, communication resumption, service termination, route discovery, etc. From the waveforms conveyed during the signaling procedures, the waveform identifier extracts the parameters that can be used for a CG to identify the source waveform and the destination waveform. These parameters are called “waveform indicators.“ The author analyzes the four types of waveforms of interest and outlines the waveform indicators for different types of communication initiators. In particular, a multi-layer waveform identifier is designed for a CG to extract the waveform indicators from the signaling messages. For the physical layer signal recognition, a Universal Classification Synchronization (UCS) system has been invented. UCS is conceived as a self-contained system which can detect, classify, synchronize with a received signal and provide all parameters needed for physical layer demodulation without prior information from the transmitter. Currently, it can accommodate the modulations including AM, FM, FSK, MPSK, QAM and OFDM. The design and implementation details of a UCS have been presented. The designed system has been verified by over-the-air (OTA) experiments and its performance has been evaluated by theoretical analysis and software simulation. UCS can be ported to different platforms and can be applied for various scenarios. An underlying assumption for UCS is that the target signal is transmitted continually. However, it is not the case for a CG since the detection objects of a CG are signaling messages. In order to ensure higher recognition accuracy, signaling efficiency, and lower signaling overhead, the author addresses the key issues for signaling scheme design and their dependence on waveform identification strategy. In a CG, waveform transformation (WT) is the last step of the link establishment process. The resources required for transformation of waveform pairs, together with the application priority, constitute the major factors that determine the link control and scheduling scheme in a CG. The author sorts different WT into five categories and describes the details of implementing the four typical types of WT (including physical layer analog – analog gateway, up to link layer digital – digital gateway, up-to-network-layer digital gateway, and Voice over IP (VoIP) – an up to transport layer gateway) in a practical CG prototype. The issues that include resource management and link scheduling have also been addressed. This dissertation presents a CG prototype implemented on the basis of GNU Radio plus multiple USRPs. In particular, the service process of a CG is modeled as a two-stage tandem queue, where the waveform identifier queues at the first stage can be described as M/D/1/1 models and the waveform converter queue at the second stage can be described as G/M/K/K model. Based on these models, the author derives the theoretical block probability and throughput of a CG. Although the “source-CG-destination” snapshot considers only neighboring nodes which are one-hop away from the CG, it is scalable to form larger networks. CG can work in either ad-hoc or infrastructure mode. Utilizing its capabilities, CG nodes can be placed in different network architectures/topologies to provide auxiliary connectivity. Multi-hop cooperative relaying via CGs will be an interesting research topic deserving further investigation.
Cognitive Networks
Thomas, Ryan William (Virginia Tech, 2007-06-15)
For complex computer networks with many tunable parameters and network performance objectives, the task of selecting the ideal network operating state is difficult. To improve the performance of these kinds of networks, this research proposes the idea of the cognitive network. A cognitive network is a network composed of elements that, through learning and reasoning, dynamically adapt to varying network conditions in order to optimize end-to-end performance. In a cognitive network, decisions are made to meet the requirements of the network as a whole, rather than the individual network components. We examine the cognitive network concept by first providing a definition and then outlining the difference between it and other cognitive and cross-layer technologies. From this definition, we develop a general, three-layer cognitive network framework, based loosely on the framework used for cognitive radio. In this framework, we consider the possibility of a cognitive process consisting of one or more cognitive elements, software agents that operate somewhere between autonomy and cooperation. To understand how to design a cognitive network within this framework we identify three critical design decisions that affect the performance of the cognitive network: the selfishness of the cognitive elements, their degree of ignorance, and the amount of control they have over the network. To evaluate the impact of these decisions, we created a metric called the price of a feature, defined as the ratio of the network performance with a certain design decision to the performance without the feature. To further aid in the design of cognitive networks, we identify classes of cognitive networks that are structurally similar to one another. We examined two of these classes: the potential class and the quasi-concave class. Both classes of networks will converge to Nash Equilibrium under selfish behavior and in the quasi-concave class this equilibrium is both Pareto and globally optimal. Furthermore, we found the quasi-concave class has other desirable properties, reacting well to the absence of certain kinds of information and degrading gracefully under reduced network control. In addition to these analytical, high level contributions, we develop cognitive networks for two open problems in resource management for self-organizing networks, validating and illustrating the cognitive network approach. For the first problem, a cognitive network is shown to increase the lifetime of a wireless multicast route by up to 125\%. For this problem, we show that the price of selfishness and control are more significant than the price of ignorance. For the second problem, a cognitive network minimizes the transmission power and spectral impact of a wireless network topology under static and dynamic conditions. The cognitive network, utilizing a distributed, selfish approach, minimizes the maximum power in the topology and reduces (on average) the channel usage to within 12\% of the minimum channel assignment. For this problem, we investigate the price of ignorance under dynamic networks and the cost of maintaining knowledge in the network. Today's computer networking technology will not be able to solve the complex problems that arise from increasingly bandwidth-intensive applications competing for scarce resources. Cognitive networks have the potential to change this trend by adding intelligence to the network. This work introduces the concept and provides a foundation for future investigation and implementation.
Cognitive Networks: Foundations to Applications
Friend, Daniel (Virginia Tech, 2009-03-06)
Fueled by the rapid advancement in digital and wireless technologies, the ever-increasing capabilities of wireless devices have placed upon us a tremendous challenge - how to put all of this capability to effective use. Individually, wireless devices have outpaced the ability of users to optimally configure them. Collectively, the complexity is far more daunting. Research in cognitive networks seeks to provide a solution to the diffculty of effectively using the expanding capabilities of wireless networks by embedding greater degrees of intelligence within the network itself. In this dissertation, we address some fundamental questions related to cognitive networks, such as "What is a cognitive network?" and "What methods may be used to design a cognitive network?" We relate cognitive networks to a common artificial intelligence (AI) framework, the multi-agent system (MAS). We also discuss the key elements of learning and reasoning, with the ability to learn being the primary differentiator for a cognitive network. Having discussed some of the fundamentals, we proceed to further illustrate the cognitive networking principle by applying it to two problems: multichannel topology control for dynamic spectrum access (DSA) and routing in a mobile ad hoc network (MANET). The multichannel topology control problem involves confguring secondary network parameters to minimize the probability that the secondary network will cause an outage to a primary user in the future. This requires the secondary network to estimate an outage potential map, essentially a spatial map of predicted primary user density, which must be learned using prior observations of spectral occupancy made by secondary nodes. Due to the complexity of the objective function, we provide a suboptimal heuristic and compare its performance against heuristics targeting power-based and interference-based topology control objectives. We also develop a genetic algorithm to provide reference solutions since obtaining optimal solutions is impractical. We show how our approach to this problem qualifies as a cognitive network. In presenting our second application, we address the role of network state observations in cognitive networking. Essentially, we need a way to quantify how much information is needed regarding the state of the network to achieve a desired level of performance. This question is applicable to networking in general, but becomes increasingly important in the cognitive network context because of the potential volume of information that may be desired for decision-making. In this case, the application is routing in MANETs. Current MANET routing protocols are largely adapted from routing algorithms developed for wired networks. Although optimal routing in wired networks is grounded in dynamic programming, the critical assumption, static link costs and states, that enables the use of dynamic programming for wired networks need not apply to MANETs. We present a link-level model of a MANET, which models the network as a stochastically varying graph that possesses the Markov property. We present the Markov decision process as the appropriate framework for computing optimal routing policies for such networks. We then proceed to analyze the relationship between optimal policy and link state information as a function of minimum distance from the forwarding node. The applications that we focus on are quite different, both in their models as well as their objectives. This difference is intentional and signficant because it disassociates the technology, i.e. cognitive networks, from the application of the technology. As a consequence, the versatility of the cognitive networks concept is demonstrated. Simultaneously, we are able to address two open problems and provide useful results, as well as new perspective, on both multichannel topology control and MANET routing. This material is posted here with permission from the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Virginia Tech library's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this material, you agree to all provisions of the copyright laws protecting it.
Cognitive Radar Applied To Target Tracking Using Markov Decision Processes
Selvi, Ersin Suleyman (Virginia Tech, 2018-01-30)
The radio-frequency spectrum is a precious resource, with many applications and users, especially with the recent spectrum auction in the United States. Future platforms and devices, such as radars and radios, need to be adaptive to their spectral environment in order to continue serving the needs of their users. This thesis considers an environment with one tracking radar, a single target, and a communications system. The radar-communications coexistence problem is modeled as a Markov decision process (MDP), and reinforcement learning is applied to drive the radar to optimal behavior.
A Complete & Practical Approach to Ensure the Legality of a Signal Transmitted by a Cognitive Radio
Cowhig, Patrick Carpenter (Virginia Tech, 2006-09-05)
The computational power and algorithms needed to create a cognitive radio are quickly becoming available. There are many advantages to having a radio operated by cognitive engine, and so cognitive radios are likely to become very popular in the future. One of the main difficulties associated with the cognitive radio is ensuring the signal transmitted will follow all FCC rules. The work presented in this thesis provides a methodology to guarantee that all signals will be legal and valid. The first part to achieving this is a practical and easy to use software testing program based on the tabu search algorithm that tests the software off-line. The primary purpose of the software testing program is to find most of the errors, specially structural errors, while the radio is not in use so that it does not affect the performance of the system. The software testing program does not provide a complete assurance that no errors exist, so to supplement this deficit, a built-in self-test (BIST) is employed. The BIST is designed with two parts, one that is embedded into the cognitive engine and one that is placed into the radio's API. These two systems ensure that all signals transmitted by the cognitive radio will follow FCC rules while consuming a minimal amount of computational power. The software testing approach based on the tabu search is shown to be a viable method to test software with improved results over previous methods. Also, the software BIST demonstrated its ability to find errors in the signal production and is dem to only require an insignificant amount of computational power. Overall, the methods presented in this paper provide a complete and practical approach to assure the FCC of the legality of all signals in order to obtain a license for the product.
Comprehensive Performance Analysis of Localizability in Heterogeneous Cellular Networks
Bhandari, Tapan (Virginia Tech, 2017-08-03)
The availability of location estimates of mobile devices (MDs) is vital for several important applications such as law enforcement, disaster management, battlefield operations, vehicular communication, traffic safety, emergency response, and preemption. While global positioning system (GPS) is usually sufficient in outdoor clear sky conditions, its functionality is limited in urban canyons and indoor locations due to the absence of clear line-of-sight between the MD to be localized and a sufficient number of navigation satellites. In such scenarios, the ubiquitous nature of cellular networks makes them a natural choice for localization of MDs. Traditionally, localization in cellular networks has been studied using system level simulations by fixing base station (BS) geometries. However, with the increasing irregularity of the BS locations (especially due to capacity-driven small cell deployments), the system insights obtained by considering simple BS geometries may not carry over to real-world deployments. This necessitates the need to study localization performance under statistical (random) spatial models, which is the main theme of this work. In this thesis, we use powerful tools from stochastic geometry and point process theory to develop a tractable analytical model to study the localizability (ability to get a location fix) of an MD in single-tier and heterogeneous cellular networks (HetNets). More importantly, we study how availability of information about the location of proximate BSs at the MD impacts localizability. To this end, we derive tractable expressions, bounds, and approximations for the localizability probability of an MD. These expressions depend on several key system parameters, and can be used to infer valuable system insights. Using these expressions, we quantify the gains achieved in localizability of an MD when information about the location of proximate BSs is incorporated in the model. As expected, our results demonstrate that localizability improves with the increase in density of BS deployments.

Browsing by Author "MacKenzie, Allen B."

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