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. / Master of Science

[pt] Este trabalho estuda o problema de detecção de hipóteses binárias em sistemas distribuídos com centro de fusão operando em presença de canais seletivos em frequência. O uso de uma técnica de múltiplo acesso, referida aqui como CS-CDMA, é proposta para comunicação ortogonal entre os nós e o centro de fusão, assim como detector ótimo Bayesiano para fusão de dados em tais sistemas distribuídos é obtido. Como a complexidade do detector ótimo cresce exponencialmente com o número de nós sensores, um receptor sub-ótimo de baixa complexidade que realiza uma detecção casada multi-usuário seguida de decisão pela regra da maioria é proposto e examinado neste trabalho. Técnicas para estimação de canal, cega e assistida, necessárias para a implementação prática da detecção casada são também propostas. Simulações indicam que este receptor, de baixa complexidade, possui um desempenho próximo ao receptor ótimo. Com o objetivo de se ampliar o desempenho do detector casado do centro de fusão, é examinado o uso de cooperação na rede de sensores. Resultados de simulações mostraram que, como esperado, o uso de cooperação em sistema distribuídos utilizando o esquema de múltiplo acesso CS-CDMA melhora o desempenho do decisor do centro de fusão, entretanto esse ganho de desempenho mostrou-se mais significativo em ambientes com poucos multipercursos, uma vez que os sistemas distribuídos CS-CDMA não-cooperativos propostos exploram eficientemente a diversidade de multipercurso. Finalmente, este trabalho propõe um procedimento de fusão adaptativa não-assistida para sistemas distribuídos com fusão centralizada. Simulações mostram que a estratégia de fusão adaptativa possui desempenho muito próximo ao da regra de fusão ótima. / [en] This work studies the problem of detecting binary hypotheses in distributed systems with a fusion center operating in frequency selective channels. The use of a multiple access technique, referred herein as Chip Spread- Code Division Multiple Access (CS-CDMA), is proposed for orthogonal communication between the nodes and the fusion center and the Bayesian optimum detector for data fusion for such distributed systems is obtained. As the complexity of the optimal detector grows exponentially with the number of sensor nodes, a sub-optimal low-complexity receiver that performs a multi-user matched detection followed by the majority rule is proposed and examined in this work. Blind and assisted techniques for channel estimation necessary for the practical implementation of the matched detection have also been proposed. Simulations indicate that this low complexity receptor has a performance close to the optimal receiver. In order to increase the performance of the matched detector of the fusion center, it was examined the use of cooperation in this sensor network. Simulation results showed that, as expected, the use of cooperation in the distributed system with a multiple access scheme CS-CDMA improves the performance of the fusion center, however, this performance increasing was more significant in environments with few multipath, since the non-cooperative CS-CDMA distributed systems proposed here, efficiently exploits the multipath diversity. Finally, this paper proposes a non-assisted adaptive fusion for distributed systems with centralized fusion. Simulations show that the adaptive fusion strategy has a performance very close to the optimal fusion rule.

This dissertation studies a number of important issues that arise in multiple-input multiple-out wireless systems. First, wireless systems equipped with multiple-transmit multiple-receive antennas are considered where an energy-based antenna selection is performed at the receiver. Three different situations are considered: (i) selection over iid MIMO fading channel, (ii) selection over spatially correlated fading channel, and (iii) selection for space-time coded OFDM systems. In all cases, explicit upper bounds are derived and it is shown that using the proposed antenna selection, one can achieve the same diversity order as that attained by full-complexity MIMO systems. Next, joint source-channel coding problem for MIMO antenna systems is studied and a turbo-coded multiple description code for multiple antenna transmission is developed. Simulations indicate that by the proposed iterative joint source-channel decoding that exchanges the extrinsic information between the source code and the channel code, one can achieve better reconstruction quality than that can be achieved by the single-description codes at the same rate. The rest of the dissertation deals with wireless networks. Two problems are studied: channel coding for cooperative diversity in wireless networks, and distributed detection in wireless sensor networks. First, a turbo-code based channel code for three-terminal full-duplex wireless relay channels is proposed where both the source and the relay nodes employ turbo codes. An iterative turbo decoding algorithm exploiting the information arriving from both the source and relay nodes is proposed. Simulation results show that the proposed scheme can perform very close to the capacity of a wireless relay channel. Next the parallel and serial binary distributed detection problem in wireless sensor networks is investigated. Detection strategies based on single-bit and multiple-bit decisions are considered. The expressions for the detection and false alarm rates are derived and used for designing the optimal detection rules at all sensor nodes. Also, an analog approach to the distributed detection in wireless sensor networks is proposed where each sensor nodes simply amplifies-and-forwards its sufficient statistics to the fusion center. This method requires very simple processing at the local sensor. Numerical examples indicate that the analog approach is superior to the digital approach in many cases.

We consider the distributed detection of an emitter using multiple sensors deployed at deterministic locations. The signal from the emitter follows a signal attenuation model dependent on the distance between the sensor and the emitter. The sensors transmit their decisions to the fusion center through a parallel access Binary Symmetric Channel (BSC) with a cross-over probability. We seek to optimize the detection performance under a prescribed false alarm at the sensor level and at the system level. We consider the triangular topology structure and using the least favorable emitter range study the impact of the BSC on the system level detection fusion rules. The MAJORITY fusion rule is found to be optimal under certain conditions.

No description available.

Master of Science / Department of Electrical and Computer Engineering / Balasubramaniam Natarajan / In this thesis, we first consider the problem of distributed estimation in an energy and rate-constrained wireless sensor network. To this end, we study three estimators namely - (1) Best Linear Unbiased Estimator (BLUE-1) that accounts for the variance of noise in measurement, uniform quantization and channel, and derive its variance and its lower bound; (2) Best Linear Unbiased Estimator (BLUE-2) that accounts for the variance of noise in measurement and uniform quantization, and derive lower and upper bounds for its variance; (3) Best Linear Unbiased Estima- tor (BLUE-3) that incorporates the effects of probabilistic quantization noise and measurement noise, and derive an upper bound for its variance. Then using BLUE-1, we analyze the tradeoff between estimation error (BLUE variance) at the fusion center and the total amount of resources utilized (power and rate) using three different system design approaches or optimization formulations. For all the formulations, we determine optimum quantization bits and transmission power per bit (or optimum actions) for all sensors jointly. Unlike prior efforts, we in- corporate the operating state (characterized by the amount of residual battery power) of the sensors in the optimization framework. We study the e®ect of channel quality, local measurement noise, and operating states of the sensors on their optimum choice for quantization bits and transmit power per bit. In the sequel, we consider a problem in distributed detection and signal processing in the context of biomedical wireless sensors and more specifically pulse- oximeter devices that record photoplethysmographic data. We propose an automated, two-stage PPG data processing method to minimize the effect of motion artifact. Regarding stage one, we present novel and consistent techniques to detect the presence of motion artifact in photoplethysmograms given higher order statistical information present in the data.For stage two, we propose an effective motion artifact reduction method that involves enhanced PPG data preprocessing followed by frequency domain Independent Component Analysis (FD-ICA). Experimental results are presented to demonstrate the efficacy of the overall motion artifact reduction method. Finally, we analyze a wireless ad hoc/sensor network where nodes are connected via random channels and information is transported in the network in a cooperative multihop fashion using amplify and forward relay strategy.

This dissertation develops optimal algorithms for distributed detection and estimation in static and mobile sensor networks. In distributed detection or estimation scenarios in clustered wireless sensor networks, sensor motes observe their local environment, make decisions or quantize these observations into local estimates of finite length, and send/relay them to a Cluster-Head (CH). For event detection tasks that are subject to both measurement errors and communication errors, we develop an algorithm that combines a Maximum a Posteriori (MAP) approach for local and global decisions with low-complexity channel codes and processing algorithms. For event estimation tasks that are subject to measurement errors, quantization errors and communication errors, we develop an algorithm that uses dithered quantization and channel compensation to ensure that each mote's local estimate received by the CH is unbiased and then lets the CH fuse these estimates into a global one using a Best Linear Unbiased Estimator (BLUE). We then determine both the minimum energy required for the network to produce an estimate with a prescribed error variance and show how this energy must be allocated amongst the motes in the network. In mobile wireless sensor networks, the mobility model governing each node will affect the detection accuracy at the CH and the energy consumption to achieve this level of accuracy. Correlated Random Walks (CRWs) have been proposed as mobility models that accounts for time dependency, geographical restrictions and nonzero drift. Hence, the solution to the continuous-time, 1-D, finite state space CRW is provided and its statistical behavior is studied both analytically and numerically. The impact of the motion of sensor on the network's performance is also studied.

abstract: Distributed inference has applications in a wide range of fields such as source localization, target detection, environment monitoring, and healthcare. In this dissertation, distributed inference schemes which use bounded transmit power are considered. The performance of the proposed schemes are studied for a variety of inference problems. In the first part of the dissertation, a distributed detection scheme where the sensors transmit with constant modulus signals over a Gaussian multiple access channel is considered. The deflection coefficient of the proposed scheme is shown to depend on the characteristic function of the sensing noise, and the error exponent for the system is derived using large deviation theory. Optimization of the deflection coefficient and error exponent are considered with respect to a transmission phase parameter for a variety of sensing noise distributions including impulsive ones. The proposed scheme is also favorably compared with existing amplify-and-forward (AF) and detect-and-forward (DF) schemes. The effect of fading is shown to be detrimental to the detection performance and simulations are provided to corroborate the analytical results. The second part of the dissertation studies a distributed inference scheme which uses bounded transmission functions over a Gaussian multiple access channel. The conditions on the transmission functions under which consistent estimation and reliable detection are possible is characterized. For the distributed estimation problem, an estimation scheme that uses bounded transmission functions is proved to be strongly consistent provided that the variance of the noise samples are bounded and that the transmission function is one-to-one. The proposed estimation scheme is compared with the amplify and forward technique and its robustness to impulsive sensing noise distributions is highlighted. It is also shown that bounded transmissions suffer from inconsistent estimates if the sensing noise variance goes to infinity. For the distributed detection problem, similar results are obtained by studying the deflection coefficient. Simulations corroborate our analytical results. In the third part of this dissertation, the problem of estimating the average of samples distributed at the nodes of a sensor network is considered. A distributed average consensus algorithm in which every sensor transmits with bounded peak power is proposed. In the presence of communication noise, it is shown that the nodes reach consensus asymptotically to a finite random variable whose expectation is the desired sample average of the initial observations with a variance that depends on the step size of the algorithm and the variance of the communication noise. The asymptotic performance is characterized by deriving the asymptotic covariance matrix using results from stochastic approximation theory. It is shown that using bounded transmissions results in slower convergence compared to the linear consensus algorithm based on the Laplacian heuristic. Simulations corroborate our analytical findings. Finally, a robust distributed average consensus algorithm in which every sensor performs a nonlinear processing at the receiver is proposed. It is shown that non-linearity at the receiver nodes makes the algorithm robust to a wide range of channel noise distributions including the impulsive ones. It is shown that the nodes reach consensus asymptotically and similar results are obtained as in the case of transmit non-linearity. Simulations corroborate our analytical findings and highlight the robustness of the proposed algorithm. / Dissertation/Thesis / Ph.D. Electrical Engineering 2013

One of the problems with the modern radio communication is the lack of availableradio frequencies. Recent studies have shown that, while the available licensed radiospectrum becomes more occupied, the assigned spectrum is significantly underutilized.To alleviate the situation, cognitive radio (CR) technology has been proposedto provide an opportunistic access to the licensed spectrum areas. Secondary CRsystems need to cyclically detect the presence of a primary user by continuouslysensing the spectrum area of interest. Radiowave propagation effects like fading andshadowing often complicate sensing of spectrum holes. When spectrum sensing isperformed in a cooperative manner, then the resulting sensing performance can beimproved and stabilized. In this thesis, two fully distributed and adaptive cooperative Primary User (PU)detection solutions for CR networks are studied. In the first part of this thesis we study a distributed energy detection schemewithout using any fusion center. Due to reduced communication such a topologyis more energy efficient. We propose the usage of distributed, diffusion least meansquare (LMS) type of power estimation algorithms with different network topologies.We analyze the resulting energy detection performance by using a commonframework and verify the theoretical findings through simulations. In the second part of this thesis we propose a fully distributed detection scheme,based on the largest eigenvalue of adaptively estimated correlation matrices, assumingthat the primary user signal is temporally correlated. Different forms of diffusionLMS algorithms are used for estimating and averaging the correlation matrices overthe CR network. The resulting detection performance is analyzed using a commonframework. In order to obtain analytic results on the detection performance, theadaptive correlation matrix estimates are approximated by a Wishart distribution.The theoretical findings are verified through simulations. / <p>QC 20170908</p>