Distributed Diffusion-based LMS for Node-Specific Parameter Estimation over Adaptive Networks

A distributed adaptive algorithm is proposed to solve a node-specific parameter estimation problem where nodes are interested in estimating parameters of local interest and parameters of global interest to the whole network. To address the different node-specific parameter estimation problems, this novel algorithm relies on a diffusion-based implementation of different Least Mean Squares (LMS) algorithms, each associated with the estimation of a specific set of local or global parameters. Although all the different LMS algorithms are coupled, the diffusion-based implementation of each LMS algorithm is exclusively undertaken by the nodes of the network interested in a specific set of local or global parameters. To illustrate the effectiveness of the proposed technique we provide simulation results in the context of cooperative spectrum sensing in cognitive radio networks.Comment: 5 pages, 2 figures, Published in Proc. IEEE ICASSP, Florence, Italy, May 201

Distributed Diffusion-Based LMS for Node-Specific Adaptive Parameter Estimation

A distributed adaptive algorithm is proposed to solve a node-specific parameter estimation problem where nodes are interested in estimating parameters of local interest, parameters of common interest to a subset of nodes and parameters of global interest to the whole network. To address the different node-specific parameter estimation problems, this novel algorithm relies on a diffusion-based implementation of different Least Mean Squares (LMS) algorithms, each associated with the estimation of a specific set of local, common or global parameters. Coupled with the estimation of the different sets of parameters, the implementation of each LMS algorithm is only undertaken by the nodes of the network interested in a specific set of local, common or global parameters. The study of convergence in the mean sense reveals that the proposed algorithm is asymptotically unbiased. Moreover, a spatial-temporal energy conservation relation is provided to evaluate the steady-state performance at each node in the mean-square sense. Finally, the theoretical results and the effectiveness of the proposed technique are validated through computer simulations in the context of cooperative spectrum sensing in Cognitive Radio networks.Comment: 13 pages, 6 figure

Neyman-Pearson detection in sensor networks with dependent observations

In this thesis, within the context of sensor networks, we are interested in the distributed detection problem under the Neyman-Pearson formulation and conditionally dependent sensor observations. In order to exploit all the detection potential of the network, the literature on this issue has faced optimal distributed detection problems, where optimality usually consists in properly designing the parameters of the network with the aim of minimizing some cost function related to the overall detection performance of the network. However, this problem of optimization has usually constraints regarding the possible physical and design parameters that we can choose when maximizing the detection performance of the network. In many applications, some physical and design parameters, for instance the network architecture or the local processing scheme of the sensor observations, are either strongly constrained to a set of possible design alternatives or either cannot be design variables in our problem of optimization. Despite the fact that those parameters can be related to the overall performance of the network, the previous constraints might be imposed by factors such as the environment where the network has to be deployed, the energy budget of the system or the processing capabilities of the available sensors. Consequently, it is necessary to characterize optimal decentralized detection systems with various architectures, different observation processes and different local processing schemes. The mayor part of the works addressing the characterization of distributed detection systems have assumed settings where, under each one of the possible states of our phenomenon of interest, the observations are independent across the sensors. However, there are many practical scenarios where the conditional independence assumption is violated because of the presence of different spatial correlation sources. In spite of this, very few works have faced the aforementioned characterizations under the same variety of settings as under the conditional independence assumption. Actually, when the strategy of the network is not an optimization parameter, under the assumption of conditionally dependent observations the existing literature has only obtained asymptotic characterizations of the detection performance associated with parallel networks whose local processing rules are based on amplify-and-relay schemes. Motivated by this last fact, in this thesis, under the Neyman-Pearson formulation, we undertake the characterization of distributed detection systems with dependent observations, various network architectures and binary quantization rules at the sensors. In particular, considering a parallel network randomly deployed along a straight line, we derive a closed-form error exponent for the Neyman-Pearson fusion of Markov local decisions when the involved fusion center only knows the distribution of the sensor spacings. After studying some analytical properties of the derived error exponent, we carry out evaluations of the closed-form expression in order to assess which kind of trends of detection performance can appear with increasing dependency and under two well-known models of the sensor spacing. These models are equispaced sensors with failures and exponentially spaced sensors with failures. Later, the previous results are extended to a two-dimensional parallel network that, formed by a set of local detectors equally spaced on a rectangular lattice, performs a Neyman-Pearson test discriminating between two di erent two-dimensional Markov causal fields defined on a binary state space. Next, under conditionally dependent observations and under the Neyman-Pearson set up, this thesis dissertation focuses on the characterization of the detection performance of optimal tandem networks with binary communications between the fusion units. We do so by deriving conditions under which, in an optimal tandem network with an arbitrary constraint on the overall probability of false alarm, the probability of misdetection of the system, i.e. at the last fusion node of the network, converges to zero as the number of fusion stages approaches infinity. Finally, after extending this result under the Bayesian set up, we provide two examples where these conditions are applied in order to characterize the detection performance of the network. From these examples we illustrate different dependence scenarios where an optimal tandem network can or cannot achieve asymptotic perfect detection under either the Bayesian set up or the Neyman-Pearson formulation. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------En esta tesis, dentro del contexto de las redes de sensores, estamos interesados en el problema de detección distribuida bajo la formulación de Neyman-Pearson y observaciones condicionalmente dependientes. Con objeto de explotar el potencial de detección de la red, la literatura sobre este tema se ha enfrentado a problemas de detección distribuida óptima, donde la optimalidad normalmente hace referencia al diseño adecuado de diferentes parámetros de la red con el objeto de minimizar alguna función de coste relacionada con las prestaciones globales de detección. Sin embargo, este problema de optimización tiene normalmente restricciones asociadas con los posibles parámetros físicos y de diseño de la red que pueden ser seleccionados a la hora de maximizar las prestaciones de detección de la misma. En muchas aplicaciones algunos parámetros físicos y de diseño, como por ejemplo la arquitectura de la red o los esquemas de procesado local de las observaciones de los sensores, bien están fuertemente restringidos a un conjunto de posibles alternativas de diseño, o bien no pueden ser variables de diseño en nuestro problema de optimización. A pesar de que estos parámetros pueden estar relacionados con las prestaciones de detección de la red, las anteriores restricciones podrán estar impuestas por factores tales como el entorno en el que la red se despliega, el presupuesto de energía disponible de la red o las capacidades de procesado de los sensores. Consecuentemente, es necesario caracterizar sistemas de detección distribuidos óptimos con varias arquitecturas, diferentes procesos de observación y diferentes esquemas de procesado local. La mayor parte de los trabajos tratando la caracterización de sistemas de detección distribuida han asumido escenarios en los que, bajo cada uno de los posibles estados del fenómeno de interés, las observaciones son independientes de un sensor a otro. Sin embargo, hay muchos escenarios prácticos donde la asunción de independencia condicional es violada como consecuencia de la presencia de diferentes fuentes de correlación. A pesar de esto, muy pocos trabajos han tratado las anteriores caracterizaciones bajo la misma variedad de escenarios que bajo la asunción de independencia condicional. De hecho, cuando la estrategia de la red no es un parámetro a optimizar, bajo la asunción de observaciones condicionalmente dependientes la literatura existente sólo ha obtenido caracterizaciones asintóticas de las prestaciones de detección asociadas con redes paralelas cuyas reglas de procesado local se basan en esquemas de amplificación y retransmisión. Motivado por este útimo hecho, en esta tesis, bajo la formulación de Neyman-Pearson, llevamos a cabo la caracterización de sistemas de detección distribuida con observaciones dependientes, varias arquitecturas de red y reglas de cuantificación binaria en los sensores. En particular, considerando una red paralela desplegada aleatoriamente a lo largo de una línea recta, bajo la formulación de Neyman-Pearson derivamos una expresión cerrada del exponente de error asociado a la fusión de decisiones locales Makovianas cuando, con respecto a los espaciados entre sensores, sólo se conoce su distribución. Después de analizar algunas propiedades analíticas del derivado exponente de error, llevamos a cabo evaluaciones de su expresión cerrada con el objeto de determinar las diferentes tendencias de detección que pueden aparecer con dependencia creciente y bajo dos modelos de espaciado entre sensores muy conocidos. Estos dos modelos son sensores equiespaciados con fallos y sensores exponencialmente espaciados con fallos. Más tarde, los anteriores resultados son extendidos a una red paralela bidimensional que, formada por un conjunto de dispositivos equiespaciados sobre una rejilla rectangular, lleva a cabo un test de Neyman-Pearson para discriminar entre dos diferentes campos aleatorios causales de Markov definidos en un espacio de estados binario. Seguidamente, bajo observaciones condicionalmente dependientes y bajo la formulación de Neyman-Pearson, esta tesis se centra en la caracterización de las prestaciones de detección asociada a redes tándem óptimas con comunicación binaria entre los nodos de fusión. Para hacer eso, derivamos condiciones bajo las cuales, en una red t andem óptima con una arbitraria restricci ón en la probabilidad de falsa alarma global, la probabilidad de pérdida de la red, es decir la asociada último nodo de fusión, converge a cero seg un el número de etapas de fusión tiende a infinito. Finalmente, después de extender este resultado bajo la formulación bayesiana, proporcionamos dos ejemplos donde estas condiciones son aplicadas para caracterizar las prestaciones de detección de la red. A partir de estos ejemplos ilustramos diferentes escenarios de dependencia en los que una red t ándem óptima puede o no lograr detección asintóticamente perfecta tanto bajo la formulación bayesiana como bajo la formulación de Neyman-Pearson

On the choice of blind interference alignment strategy for cellular systems with data sharing

The proceeding at: IEEE International Conference on Communications (ICC), tool place 2014, June, 10-14 in Sidney (Australia).A cooperative blind interference alignment (BIA) strategy is considered for the downlink of cellular systems. The aim is to reduce intercell interference in order to protect users, especially at the cell edge. The strategy consists of appropriately splitting the available bandwidth and is shown to be well-suited to scenarios where the number of cell-edge users is considerable. For a system comprising two cells each with a base station of Nt antennas, it is shown that, compared to a previous augmented code approach where transmission to all users occurs in the same frequency band, the proposed strategy leads to better rates over a wide range of signal-to-noise ratios when the number of cell-edge users in both cells exceeds 2Nt -1.This work has been partially funded by research projects COMONSENS (CSD2008-00010) and GRE3N (TEC2011-29006-C03-02). This research work was partly carried out at the ESAT Laboratory of KU Leuven in the frame of the Belgian Programme on Interuniversity Attractive Poles Programme initiated by the Belgian Science Policy Office: IUAP P7/23 ‘Belgian network on stochastic modeling analysis design and optimization of communication systems’(BESTCOM) 2012-2017.Publicad

Blind Interference Alignment for Cellular Networks

We propose a blind interference alignment scheme for partially connected cellular networks. The scheme cancels both intracell and intercell interference by relying on receivers with one reconfigurable antenna and by allowing users at the cell edge to be served by all the base stations in their proximity. An outer bound for the degrees of freedom is derived for general partially connected networks with single-antenna receivers when knowledge of the channel state information at the transmitter is not available. It is demonstrated that for symmetric scenarios, this outer bound is achieved by the proposed scheme. On the other hand, for asymmetric scenarios, the achievable degrees of freedom are not always equal to the outer bound. However, the penalty is typically small, and the proposed scheme outperforms other blind interference alignment schemes. Moreover, significant reduction of the supersymbol length is achieved compared with a standard blind interference alignment strategy designed for fully connected networks.This work has been partially funded by research projects COMONSENS (CSD2008-00010) and GRE3N (TEC2011-29006-C03-02). This research work was partly carried out at the ESAT Laboratory of KU Leuven in the frame of the Belgian Programme on Interuniversity Attractive Poles Programme initiated by the Belgian Science Policy Office: IUAP P7/23 ‘Belgian network on stochastic modeling analysis design and optimization of communication systems’ (BESTCOM) 2012–2017. The work of D. Toumpakaris was supported by the European Union (European Social Fund—ESF) and Greek national funds through the Operational Program Education and Lifelong Learning of the National Strategic Reference Framework through the Research Funding Program Thales—Investing in knowledge society through the European Social Fund. The work of Syed Jafar was supported in part by NSFgrants CCF-1319104 and CCF-1317351.Publicad

Distruted signal estimation in a wireless sensor network with partially-overlapping node-specific interests or source observability

© 2015 IEEE. We study a distributed node-specific signal estimation problem where the node-specific desired signals and/or the sensor observations can have partially-overlapping latent signal subspaces. First, we provide the minimum number of linear combinations of observed sensor signals that each node can broadcast to still let all other nodes achieve the network-wide Linear Minimum Mean-Square Error (LMMSE) estimate of their node-specific desired signals. Later, for a fully-connected wireless sensor network, we derive a distributed algorithm that, under some settings, allows each node to achieve the LMMSE estimate of its node-specific desired signals by broadcasting the smallest number of signals. Unlike the existing algorithms, the proposed algorithm deals with the problem of partially-overlapping node-specific interests and incomplete observability of all latent sources at the nodes. Finally, the effectiveness of the proposed technique is shown through numerical simulations.status: publishe

Incremental multiple error filtered-X LMS for node-specific active noise control over wireless acoustic sensor networks

© 2016 IEEE. We propose an adaptive distributed algorithm to solve a node-specific Active Noise Control (ANC) problem. In this novel ANC problem, the nodes estimate different but overlapping ANC filters in order to generate secondary signals that cancel a primary noise source as it impinges on their microphones. Different sets of nodes follow a cyclic mode of cooperation in order to implement several coupled Multiple Error Filtered-X Least Mean Squares algorithms, each responsible for the estimation of part of the different node-specific ANC filters. The proposed algorithm outperforms the non-cooperative strategies and achieves the same steady-state noise reduction as a centralized solution that processes all the signals in the network. Finally, computer simulations are provided to illustrate the effectiveness of the proposed algorithm.status: publishe

Multi-Task Wireless Acoustic Sensor Network for Node-Specific Speech Enhancement and DOA Estimation

© 2016 IEEE. We consider the design of a distributed algorithm that is suitable for a wireless acoustic sensor network formed by nodes solving multiple tasks (MDMT). In the network, some of the nodes aim at estimating the node-specific direction-of-arrival of some desired sources. Additionally, there are other nodes that aim at implementing either a multi-channel Wiener filter or a minimum variance distortionless response beamformer in order to estimate node-specific desired signals as they impinge on their microphones. By using compressive filter-and-sum operations that incorporate a low-rank approximation of the sensor signal correlation matrix, the proposed MDMT algorithm let the nodes cooperate to achieve the network-wide centralized solution of their node-specific estimation problems without any knowledge about the tasks of other nodes. Finally, the effectiveness of the algorithm is shown through computer simulations.status: publishe