Cooperative Wideband Spectrum Sensing Based on Joint Sparsity

COOPERATIVE WIDEBAND SPECTRUM SENSING BASED ON JOINT SPARSITY By Ghazaleh Jowkar, Master of Science A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University Virginia Commonwealth University 2017 Major Director: Dr. Ruixin Niu, Associate Professor of Department of Electrical and Computer Engineering In this thesis, the problem of wideband spectrum sensing in cognitive radio (CR) networks using sub-Nyquist sampling and sparse signal processing techniques is investigated. To mitigate multi-path fading, it is assumed that a group of spatially dispersed SUs collaborate for wideband spectrum sensing, to determine whether or not a channel is occupied by a primary user (PU). Due to the underutilization of the spectrum by the PUs, the spectrum matrix has only a small number of non-zero rows. In existing state-of-the-art approaches, the spectrum sensing problem was solved using the low-rank matrix completion technique involving matrix nuclear-norm minimization. Motivated by the fact that the spectrum matrix is not only low-rank, but also sparse, a spectrum sensing approach is proposed based on minimizing a mixed-norm of the spectrum matrix instead of low-rank matrix completion to promote the joint sparsity among the column vectors of the spectrum matrix. Simulation results are obtained, which demonstrate that the proposed mixed-norm minimization approach outperforms the low-rank matrix completion based approach, in terms of the PU detection performance. Further we used mixed-norm minimization model in multi time frame detection. Simulation results shows that increasing the number of time frames will increase the detection performance, however, by increasing the number of time frames after a number of times the performance decrease dramatically

Docitive Networks. A Step Beyond Cognition

Projecte fet en col.laboració amb Centre Tecnològic de Telecomunicacions de CatalunyaCatalà: En les Xarxes Docents es por ta més enllà la idea d'elaborar decisions intel ligents. Per mitjà de compartir informació entre els nodes, amb l'objectiu primordial de reduir la complexitat i millorar el rendiment de les Xarxes Cognitives. Per a això es revisen alguns conceptes importants de les bases de l'Aprenentatge Automàtic, prestant especial atenció a l'aprenentatge per reforç. També es fa una visió de la Teoria de Jocs Evolutius i de la dinàmica de rèpliques. Finalment, simulacions ,basades en el projecte TIC-BUNGEE, es mostren per validar els conceptes introduïts.Castellano: Las Redes Docentes llevan más alla la idea de elaborar decisiones inteligentes, por medio de compartir información entre los nodos, con el objetivo primordial de reducir la complejidad y mejorar el rendimiento de las Redes Cognitiva. Para ello se revisan algunos conceptos importantes de las bases del Aprendizaje Automático, prestando especial atencion al aprendizaje por refuerzo, también damos una visón de la Teoría de Juegos Evolutivos y de la replicación de dinamicas. Por último, las simulaciones basadas en el proyecto TIC-BUNGEE se muestran para validar los conceptos introducidos.English: The Docitive Networks further use the idea of drawing intelligent decisions by means of sharing information between nodes with the prime aim of reduce complexity and enhance performance of Congnitive Networks. To this end we review some important concepts form Machine Learning, paying special atention to Reinforcement Learning, we also go insight Evolutionary Game Theory and Replicator Dynamics. Finally, simulations Based on ICT-BUNGEE project are shown to validate the introduced concepts

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Air Force Institute of Technology Research Report 2016

This Research Report presents the FY16 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs)

Cognitive Radio Network with a distributed control channel and quality-of-service solution

The proliferation of wireless access and applications to the Internet and the advent of a myriad of highly evolved portable communication devices; creates the need for an efficiently utilized radio spectrum. This is paramount in the licensed and unlicensed radio frequency bands, that spawn an exponential growth in Dynamic Spectrum Access (DSA) research, Cognitive Radio (CR) and Cognitive Radio Networks (CRN) research. DSA research has given way to the paradigm shift toward CR with its dynamic changes in transmission schemas. This paradigm shift from a fixed and centralized frequency spectrum environment has morphed into a dynamic and decentralized one. CR provides wireless nodes the capability to adapt and exploit the frequency spectrum. The spectrum information obtained is scanned and updated to determine the channel quality for viability and a utilization/availability by the licensed (primary) user. To take advantage of the CR capabilities, previous research has focused on a Common Control Channel(CCC) for the control signals to be used for spectrum control. This utilization generates channel saturation, extreme transmission overhead of control information, and a point of vulnerability. The traditional designs for wireless routing protocols do not support an ad hoc multi-hop cognitive radio network model. This research focuses on a real world implementation of a heterogeneous ad hoc multi-hop Cognitive Radio Network. An overall model, coined Emerald, has been designed to address the architecture; the Medium Access Control layer, E-MAC; and the network layer, E-NET. First, a Medium Access Control(MAC) layer protocol is provided to avoid the pitfalls of a common control channel. This new design provides CRNs with network topology and channel utilization information. Spectrum etiquette, in turn, addresses channel saturation, control overhead, and the single point of vulnerability. Secondly, a routing model is proposed that will address the efficiency of an ad hoc multi-hop CRN with a focus on the Quality-of-Service(QoS) of the point-to-point as well as end-to-end communication. This research has documented weaknesses in spectrum utilization; it has been expanded to accommodate a distributed control environment. Subsets of the model will be validated through Network Simulator-2(NS/2) and MatLab© simulations to determine point-to-point and end-to-end communications