Spectrum sensing for cognitive radio and radar systems

The use of the radio frequency spectrum is increasing at a rapid rate. Reliable and efficient operation in a crowded radio spectrum requires innovative solutions and techniques. Future wireless communication and radar systems should be aware of their surrounding radio environment in order to have the ability to adapt their operation to the effective situation. Spectrum sensing techniques such as detection, waveform recognition, and specific emitter identification are key sources of information for characterizing the surrounding radio environment and extracting valuable information, and consequently adjusting transceiver parameters for facilitating flexible, efficient, and reliable operation. In this thesis, spectrum sensing algorithms for cognitive radios and radar intercept receivers are proposed. Single-user and collaborative cyclostationarity-based detection algorithms are proposed: Multicycle detectors and robust nonparametric spatial sign cyclic correlation based fixed sample size and sequential detectors are proposed. Asymptotic distributions of the test statistics under the null hypothesis are established. A censoring scheme in which only informative test statistics are transmitted to the fusion center is proposed for collaborative detection. The proposed detectors and methods have the following benefits: employing cyclostationarity enables distinction among different systems, collaboration mitigates the effects of shadowing and multipath fading, using multiple strong cyclic frequencies improves the performance, robust detection provides reliable performance in heavy-tailed non-Gaussian noise, sequential detection reduces the average detection time, and censoring improves energy efficiency. In addition, a radar waveform recognition system for classifying common pulse compression waveforms is developed. The proposed supervised classification system classifies an intercepted radar pulse to one of eight different classes based on the pulse compression waveform: linear frequency modulation, Costas frequency codes, binary codes, as well as Frank, P1, P2, P3, and P4 polyphase codes. A robust M-estimation based method for radar emitter identification is proposed as well. A common modulation profile from a group of intercepted pulses is estimated and used for identifying the radar emitter. The M-estimation based approach provides robustness against preprocessing errors and deviations from the assumed noise model

Decentralized detection in resource-limited sensor network architectures

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (leaves 201-207).We consider the problem of decentralized binary detection in a network consisting of a large number of nodes arranged as a tree of bounded height. We show that the error probability decays exponentially fast with the number of nodes under both a Neyman-Pearson criterion and a Bayesian criterion, and provide bounds for the optimal error exponent. Furthermore, we show that under the Neyman-Pearson criterion, the optimal error exponent is often the same as that corresponding to a parallel configuration, implying that a large network can be designed to operate efficiently without significantly affecting the detection performance. We provide sufficient, as well as necessary, conditions for this to happen. For those networks satisfying the sufficient conditions, we propose a simple strategy that nearly achieves the optimal error exponent, and in which all non-leaf nodes need only send 1-bit messages. We also investigate the impact of node failures and unreliable communications on the detection performance. Node failures are modeled by a Galton-Watson branching process, and binary symmetric channels are assumed for the case of unreliable communications. We characterize the asymptotically optimal detection performance, develop simple strategies that nearly achieve the optimal performance, and compare the performance of the two types of networks. Our results suggest that in a large scale sensor network, it is more important to ensure that nodes can communicate reliably with each other(e.g.,by boosting the transmission power) than to ensure that nodes are robust to failures. In the case of networks with unbounded height, we establish the validity of a long-standing conjecture regarding the sub-exponential decay of Bayesian detection error probabilities in a tandem network. We also provide bounds for the error probability, and show that under the additional assumption of bounded Kullback-Leibler divergences, the error probability is (e cnd ), for all d> 1/2, with c c(logn)d being a positive constant. Furthermore, the bound (e), for all d> 1, holds under an additional mild condition on the distributions. This latter bound is shown to be tight. Moreover, for the Neyman-Pearson case, we establish that if the sensors act myopically, the Type II error probabilities also decay at a sub-exponential rate.(cont.) Finally, we consider the problem of decentralized detection when sensors have access to side-information that affects the statistics of their measurements, and the network has an overall cost constraint. Nodes can decide whether or not to make a measurement and transmit a message to the fusion center("censoring"), and also have a choice of the transmission function. We study the tradeoff in the detection performance with the cost constraint, and also the impact of sensor cooperation and global sharing of side-information. In particular, we show that if the Type I error probability is constrained to be small, then sensor cooperation is not necessary to achieve the optimal Type II error exponent.by Wee Peng Tay.Ph.D

Algorithms for energy-efficient adaptive wireless sensor networks

Mención Internacional en el título de doctorIn this thesis we focus on the development of energy-efficient adaptive algorithms for Wireless Sensor Networks. Its contributions can be arranged in two main lines. Firstly, we focus on the efficient management of energy resources in WSNs equipped with finite-size batteries and energy-harvesting devices. To that end, we propose a censoring scheme by which the nodes are able to decide if a message transmission is worthy or not given their energetic condition. In order to do so, we model the system using a Markov Decision Process and use this model to derive optimal policies. Later, these policies are analyzed in simplified scenarios in order to get insights of their features. Finally, using Stochastic Approximation, we develop low-complexity censoring algorithms that approximate the optimal policy, with less computational complexity and faster convergence speed than other approaches such as Q-learning. Secondly, we propose a novel diffusion scheme for adaptive distributed estimation in WSNs. This strategy, which we call Decoupled Adapt-then-Combine (D-ATC), is based on keeping an estimate that each node adapts using purely local information and then combines with the diffused estimations by other nodes in its neighborhood. Our strategy, which is specially suitable for heterogeneous networks, is theoretically analyzed using two different techniques: the classical procedure for transient analysis of adaptive systems and the energy conservation method. Later, as using different combination rules in the transient and steady-state regime is needed to obtain the best performance, we propose two adaptive rules to learn the combination coefficients that are useful for our diffusion strategy. Several experiments simulating both stationary estimation and tracking problems show that our method outperforms state-of-the-art techniques in relevant scenarios. Some of these simulations reveal the robustness of our scheme under node failures. Finally, we show that both approaches can be combined in a common setup: a WSN composed of harvesting nodes aiming to solve an adaptive distributed estimation problem. As a result, a censoring scheme is added on top of D-ATC. We show how our censoring approach helps to improve both steady-state and convergence performance of the diffusion scheme.La presente tesis se centra en el desarrollo de algoritmos adaptativos energéticamente eficientes para redes de sensores inalámbricos. Sus contribuciones se pueden englobar en dos líneas principales. Por un lado, estudiamos el problema de la gestión eficiente de recursos energéticos en redes de sensores equipadas con dispositivos de captación de energía y baterías finitas. Para ello, proponemos un esquema de censura mediante el cual, en un momento dado, un nodo es capaz de decidir si la transmisión de un mensaje merece la pena en las condiciones energéticas actuales. El sistema se modela mediante un Proceso de Decisión de Markov (Markov Decision Process, MDP) de horizonte infinito y dicho modelo nos sirve para derivar políticas óptimas de censura bajo ciertos supuestos. Después, analizamos estas políticas óptimas en escenarios simplificados para extraer intuiciones sobre las mismas. Por último, mediante técnicas de Aproximación Estocástica, desarrollamos algoritmos de censura de menor complejidad que aproximan estas políticas óptimas. Las numerosas simulaciones realizadas muestran que estas aproximaciones son competitivas, obteniendo una mayor tasa de convergencia y mejores prestaciones que otras técnicas del estado del arte como las basadas en Q-learning. Por otro lado, proponemos un nuevo esquema de difusión para estimación distribuida adaptativa. Esta estrategia, que denominamos Decoupled Adapt-then-Combine (D-ATC), se basa en mantener una estimación que cada nodo adapta con información puramente local y que posteriormente combina con las estimaciones difundidas por los demás nodos de la vecindad. Analizamos teóricamente nuestra estrategia, que es especialmente útil en redes heterogéneas, usando dos métodos diferentes: el método clásico para el análisis de régimen transitorio en sistemas adaptativos y el método de conservación de la energía. Posteriormente, y dado que para obtener el mejor rendimiento es necesario utilizar reglas de combinación diferentes en el transitorio y en régimen permanente, proponemos dos reglas adaptativas para el aprendizaje de los pesos de combinación para nuestra estrategia de difusión. La primera de ellas está basada en una aproximación de mínimos cuadrados (least-squares, LS); mientras que la segunda se basa en el algoritmo de proyecciones afines (Afifne Projection Algorithm, APA). Se han realizado numerosos experimentos tanto en escenarios estacionarios como de seguimiento que muestran cómo nuestra estrategia supera en prestaciones a otras aproximaciones del estado del arte. Algunas de estas simulaciones revelan además la robustez de nuestra estrategia ante errores en los nodos de la red. Por último, mostramos que estas dos aproximaciones son complementarias y las combinamos en mismo escenario: una red de sensores inalámbricos compuesta de nodos equipados con dispositivos de captación energética cuyo objetivo es resolver de manera distribuida y adaptativa un problema de estimación. Para ello, añadimos la capacidad de censurar mensajes a nuestro esquema D-ATC. Nuestras simulaciones muestran que la censura puede ser beneficiosa para mejorar tanto el rendimiento en régimen permanente como la tasa de convergencia en escenarios relevantes de estimación basada en difusión.This work was partially supported by the "Formación de Profesorado Universitario" fellowship from the Spanish Ministry of Education (FPU AP2010-5225).Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Santiago Zazo Bello.- Secretario: Miguel Lázaro Gredilla.- Vocal: Alexander Bertran

Spectrum sensing for cognitive radios: Algorithms, performance, and limitations

Inefficient use of radio spectrum is becoming a serious problem as more and more wireless systems are being developed to operate in crowded spectrum bands. Cognitive radio offers a novel solution to overcome the underutilization problem by allowing secondary usage of the spectrum resources along with high reliable communication. Spectrum sensing is a key enabler for cognitive radios. It identifies idle spectrum and provides awareness regarding the radio environment which are essential for the efficient secondary use of the spectrum and coexistence of different wireless systems. The focus of this thesis is on the local and cooperative spectrum sensing algorithms. Local sensing algorithms are proposed for detecting orthogonal frequency division multiplexing (OFDM) based primary user (PU) transmissions using their autocorrelation property. The proposed autocorrelation detectors are simple and computationally efficient. Later, the algorithms are extended to the case of cooperative sensing where multiple secondary users (SUs) collaborate to detect a PU transmission. For cooperation, each SU sends a local decision statistic such as log-likelihood ratio (LLR) to the fusion center (FC) which makes a final decision. Cooperative sensing algorithms are also proposed using sequential and censoring methods. Sequential detection minimizes the average detection time while censoring scheme improves the energy efficiency. The performances of the proposed algorithms are studied through rigorous theoretical analyses and extensive simulations. The distributions of the decision statistics at the SU and the test statistic at the FC are established conditioned on either hypothesis. Later, the effects of quantization and reporting channel errors are considered. Main aim in studying the effects of quantization and channel errors on the cooperative sensing is to provide a framework for the designers to choose the operating values of the number of quantization bits and the target bit error probability (BEP) for the reporting channel such that the performance loss caused by these non-idealities is negligible. Later a performance limitation in the form of BEP wall is established for the cooperative sensing schemes in the presence of reporting channel errors. The BEP wall phenomenon is important as it provides the feasible values for the reporting channel BEP used for designing communication schemes between the SUs and the FC

On the Design and Analysis of Secure Inference Networks

Parallel-topology inference networks consist of spatially-distributed sensing agents that collect and transmit observations to a central node called the fusion center (FC), so that a global inference is made regarding the phenomenon-of-interest (PoI). In this dissertation, we address two types of statistical inference, namely binary-hypothesis testing and scalar parameter estimation in parallel-topology inference networks. We address three different types of security threats in parallel-topology inference networks, namely Eavesdropping (Data-Confidentiality), Byzantine (Data-Integrity) or Jamming (Data-Availability) attacks. In an attempt to alleviate information leakage to the eavesdropper, we present optimal/near-optimal binary quantizers under two different frameworks, namely differential secrecy where the difference in performances between the FC and Eve is maximized, and constrained secrecy where FC’s performance is maximized in the presence of tolerable secrecy constraints. We also propose near-optimal transmit diversity mechanisms at the sensing agents in detection networks in the presence of tolerable secrecy constraints. In the context of distributed inference networks with M-ary quantized sensing data, we propose a novel Byzantine attack model and find optimal attack strategies that minimize KL Divergence at the FC in the presence of both ideal and non-ideal channels. Furthermore, we also propose a novel deviation-based reputation scheme to detect Byzantine nodes in a distributed inference network. Finally, we investigate optimal jamming attacks in detection networks where the jammer distributes its power across the sensing and the communication channels. We also model the interaction between the jammer and a centralized detection network as a complete information zero-sum game. We find closed-form expressions for pure-strategy Nash equilibria and show that both the players converge to these equilibria in a repeated game. Finally, we show that the jammer finds no incentive to employ pure-strategy equilibria, and causes greater impact on the network performance by employing mixed strategies