Characterizing probability distribution function for the input of a communication channel that achieves the maximum possible data rate is one of the most fundamental problems in the field of information theory. In his ground-breaking paper, Shannon showed that the capacity of a point-to-point additive white Gaussian noise channel under an average power constraint at the input, is achieved by Gaussian distribution. Although imposing a limitation on the peak of the channel input is also very important in modelling the communication system more accurately, it has gained much less attention in the past few decades. A rather unexpected result of Smith indicated that the capacity achieving distribution for an AWGN channel under peak constraint at the input is unique and discrete, possessing a finite number of mass points. In this thesis, we study multiple access channel under peak constraints at the inputs of the channel. By extending Smith's argument to our multi-terminal problem we show that any point on the boundary of the capacity region of the channel is only achieved by discrete distributions with a finite number of mass points. Although we do not claim uniqueness of the capacity-achieving distributions, however, we show that only discrete distributions with a finite number of mass points can achieve points on the boundary of the capacity region. First we deal with the problem of maximizing the sum-rate of a two user Gaussian MAC with peak constraints. It is shown that generating the code-books of both users according to discrete distributions with a finite number of mass points achieves the largest sum-rate in the network. After that we generalize our proof to maximize the weighted sum-rate of the channel and show that the same properties hold for the optimum input distributions. This completes the proof that the capacity region of a two-user Gaussian MAC is achieved by discrete input distributions with a finite number of mass points.

Characterizing probability distribution function for the input of a communication channel that achieves the maximum possible data rate is one of the most fundamental problems in the field of information theory. In his ground-breaking paper, Shannon showed that the capacity of a point-to-point additive white Gaussian noise channel under an average power constraint at the input, is achieved by Gaussian distribution. Although imposing a limitation on the peak of the channel input is also very important in modelling the communication system more accurately, it has gained much less attention in the past few decades. A rather unexpected result of Smith indicated that the capacity achieving distribution for an AWGN channel under peak constraint at the input is unique and discrete, possessing a finite number of mass points. In this thesis, we study multiple access channel under peak constraints at the inputs of the channel. By extending Smith's argument to our multi-terminal problem we show that any point on the boundary of the capacity region of the channel is only achieved by discrete distributions with a finite number of mass points. Although we do not claim uniqueness of the capacity-achieving distributions, however, we show that only discrete distributions with a finite number of mass points can achieve points on the boundary of the capacity region. First we deal with the problem of maximizing the sum-rate of a two user Gaussian MAC with peak constraints. It is shown that generating the code-books of both users according to discrete distributions with a finite number of mass points achieves the largest sum-rate in the network. After that we generalize our proof to maximize the weighted sum-rate of the channel and show that the same properties hold for the optimum input distributions. This completes the proof that the capacity region of a two-user Gaussian MAC is achieved by discrete input distributions with a finite number of mass points.

Future cognitive radio networks are expected to come as a disruptive technological advance in the currently saturated field of wireless communications. The idea behind cognitive radios is to think of the wireless channels as a pool of communication resources, which can be accessed on-demand by a primary licensed network or opportunistically preempted (or overlaid) by a secondary network with lower access priority. From a physical layer point of view, the primary network is ideally oblivious of the existence of a co-localized secondary networks. The latter are therefore required to autonomously explore the air in search for resource left-overs, and then to optimally exploit the available resource. The exploration and exploitation procedures, which involve multiple interacting agents, are requested to be highly reliable, fast and efficient. The objective of the thesis is to model, analyse and propose computationally efficient and close-to-optimal solutions to the above operations.Regarding the exploration phase, we first resort to the maximum entropy principle to derive communication models with many unknowns, from which we derive the optimal multi-source multi-sensor Neyman-Pearson signal sensing procedure. The latter allows for a secondary network to detect the presence of spectral left-overs. The computational complexity of the optimal approach however calls for simpler techniques, which are recollected and discussed. We then proceed to the extension of the signal sensing approach to the more advanced blind user localization, which provides further valuable information to overlay occupied spectral resources.The second part of the thesis is dedicaded to the exploitation phase, that is, the optimal sharing of available resources. To this end, we derive an (asymptotically accurate) approximated expression for the uplink ergodic sum rate of a multi-antenna multiple-access channel and propose solutions for cognitive radios to adapt rapidly to the evolution of the primary network at a minimum feedback cost for the secondary networks.

Ο υπολογισμός της χωρητικότητας Shannon ενός τηλεπικοινωνιακού καναλιού είναι ένα από τα κλασικά προβλήματα της θεωρίας πληροφορίας. Η τιμή της προσδιορίζει το μέγιστο δυνατό ρυθμό αξιόπιστης μετάδοσης μέσα από το κανάλι και αποτελεί ρυθμιστική παράμετρο κατά το σχεδιασμό κάθε τηλεπικοινωνιακού συστήματος. Στις πιο ενδιαφέρουσες περιπτώσεις ο υπολογισμός καταλήγει σε ένα πρόβλημα βελτιστοποίησης για το οποίο δε μπορεί να δοθεί αναλυτική λύση, οπότε καταφεύγουμε στη χρήση προσεγγιστικών μεθόδων ή στη διατύπωση φραγμάτων. Στα πλαίσια της εργασίας μελετάται η χωρητικότητα Shannon τηλεπικοινωνιακών καναλιών ενός ή πολλαπλών χρηστών. Η μελέτη ξεκινά από την απλές περιπτώσεις του διακριτού καναλιού χωρίς μνήμη (DMC) και του καναλιού AWGN και επεκτείνεται στις πιο ενδιαφέρουσες περιπτώσεις των σύμφωνων ή μη (coherence/non-coherence) καναλιών διάλειψης, σε κανάλια με μνήμη, κανάλια πολλαπλών κεραιών και κανάλια πολλαπλών χρηστών. Σε κάθε περίπτωση καταγράφονται τα σημαντικότερα ερευνητικά αποτελέσματα σχετικά με το πρόβλημα προσδιορισμού της χωρητικότητας, τη συμπεριφορά της σε σχέση με τους παράγοντες του τηλεπικοινωνιακού μοντέλου, του αλγοριθμικού υπολογισμού της και τα χαρακτηριστικά που πρέπει να έχει η είσοδος ώστε να επιτυγχάνεται η τιμή της. / Computing the Shannon Capacity of a communication channel is one of the classic problems of information theory. Its value determine the maximum possible rate of reliable transmission through the channel and constitutes a design parameter during the designing of the communication system. In most interesting cases the problem ending to an optimization problem which can’t be solved analytically, so we refuge to approximating methods and we can only state bounds for the region in which capacity belongs. In this thesis we study the Shannon Capacity of single user and multiple user communications systems. The study begins with the simple cases of Discrete Memoryless Channel (DMC) and AWGN channel and goes further to more interesting cases like coherence/non-coherence fading channels, channels with memory, multiple antenna channels and channels with multiple users. In each case, we present the most important scientific results considering the problem of capacity, its behavior in relation to the parameters of the communication model, its algorithmic computation and the characteristics of the optimal input.

Los canales múltiple-entrada-múltiple-salida (MIMO) han sido ampliamente propuestos para superar los desvanecimientos aleatorios de canal en comunicaciones inalámbricas no selectivas en frecuencia. Basados en equipar tanto transmisores como receptores con múltiple antenas, sus ventajas son dobles. Por un lado, permiten al transmisor: i) concentrar la energía transmitida en una dirección-propia determinada, o ii) codificar entre antenas con el fin de superar desvanecimientos no conocidos de canal. Por otro lado, facilitan al receptor el muestreo de la señal en el dominio espacial. Esta operación, seguida por la combinación coherente de muestras, aumenta la relación señal a ruido de entrada al receptor. De esta forma, el procesado multi-antena es capaz de incrementar la capacidad (y la fiabilidad) de la transmisión en escenarios con alta dispersión.Desafortunadamente, no siempre es posible emplazar múltiples antenas en los dispositivos inalámbricos, debido a limitaciones de espacio y/o coste. Para estos casos, la manera más apropiada de explotar el procesado multi-antena es mediante retransmisión, consistente en disponer un conjunto de repetidores inalámbricos que asistan la comunicación entre un grupo de transmisores y un grupo de receptores, todos con una única antena. Con la ayuda de los repetidores, por tanto, los canales MIMO se pueden imitar de manera distribuida. Sin embargo, la capacidad exacta de las comunicaciones con repetidores (así como la manera en que este esquema funciona con respeto al MIMO equivalente) es todavía un problema no resuelto. A dicho problema dedicamos esta tesis.En particular, la presente disertación tiene como objetivo estudiar la capacidad de canales Gaussianos asistidos por múltiples repetidores paralelos. Dos repetidores se dicen paralelos si no existe conexión directa entre ellos, si bien ambos tienen conexión directa con la fuente y el destino de la comunicación. Nos centramos en el análisis de tres canales ampliamente conocidos: el canal punto-a-punto, el canal de múltiple-acceso y el canal de broadcast, y estudiamos su mejora de funcionamiento con repetidores. A lo largo de la tesis, se tomarán las siguientes hipótesis: i) operación full-duplex en los repetidores, ii) conocimiento de canal tanto en transmisión como en recepción, y iii) desvanecimiento sin memoria, e invariante en el tiempo.En primer lugar, analizamos el canal con múltiples repetidores paralelos, en el cual una única fuente se comunica con un único destino en presencia de N repetidores paralelos. Derivamos límites inferiores de la capacidad del canal por medio de las tasas de transmisión conseguibles con distintos protocolos: decodificar-y-enviar, decodificar-parcialmente-y-enviar, comprimir-y-enviar, y repetición lineal. Asimismo, con un fin comparativo, proveemos un límite superior, obtenido a través del Teorema de max-flow-min-cut. Finalmente, para el número de repetidores tendiendo a infinito, presentamos las leyes de crecimiento de todas las tasas de transmisión, así como la del límite superior.A continuación, la tesis se centra en el canal de múltiple-acceso (MAC) con múltiples repetidores paralelos. El canal consiste en múltiples usuarios comunicándose simultáneamente con un único destino en presencia de N repetidores paralelos. Derivamos una cota superior de la región de capacidad de dicho canal utilizando, de nuevo, el Teorema de max-flow-min-cut, y encontramos regiones de tasas de transmisión conseguibles mediante: decodificar-y-enviar, comprimir-y-enviar, y repetición lineal. Asimismo, se analiza el valor asintótico de dichas tasas de transmisión conseguibles, asumiendo el número de usuarios creciendo sin límite. Dicho estudio nos permite intuir el impacto de la diversidad multiusuario en redes de acceso con repetidores.Finalmente, la disertación considera el canal de broadcast (BC) con múltiples repetidores paralelos. En él, una única fuente se comunica con múltiples destinos en presencia de N repetidores paralelos. Para dicho canal, derivamos tasas de transmisión conseguibles dado: i) codificación de canal tipo dirty paper en la fuente, ii) decodificar-y-enviar, comprimir-y-enviar, y repetición lineal, respectivamente, en los repetidores. Además, para repetición lineal, demostramos que la dualidad MAC-BC se cumple. Es decir, la región de tasas de transmisión conseguibles en el BC es igual a aquélla del MAC con una limitación de potencia suma. Utilizando este resultado, se derivan algoritmos de asignación óptima de recursos basados en teoría de optimización convexa. / Multiple-input-multiple-output (MIMO) channels are extensively proposed as a means to overcome the random channel impairments of frequency-flat wireless communications. Based upon placing multiple antennas at both the transmitter and receiver sides of the communication, their virtues are twofold. On the one hand, they allow the transmitter: i) to concentrate the transmitted power onto a desired eigen-direction, or ii) tocode across antennas to overcome unknown channel fading. On the other hand, they permit the receiver to sample the signal on the space domain. This operation, followed by the coherent combination of samples, increases the signal-to-noise ratio at the input of the detector. In fine, MIMO processing is able to provide large capacity (and reliability) gains within rich-scattered scenarios.Nevertheless, equipping wireless handsets with multiple antennas is not always possible or worthwhile. Mainly, due to size and cost constraints, respectively. For these cases, the most appropriate manner to exploit multi-antenna processing is by means of relaying. This consists of a set of wireless relay nodes assisting the communication between a set of single-antenna sources and a set of single-antenna destinations. With the aid of relays, indeed, MIMO channels can be mimicked in a distributed way. However, the exact channel capacity of single-antenna communications with relays (and how this scheme performs with respect to the equivalent MIMO channel) is a long-standing open problem. To it we have devoted this thesis.In particular, the present dissertation aims at studying the capacity of Gaussian channels when assisted by multiple, parallel, relays. Two relays are said to be parallel if there is no direct link between them, while both have direct link from the source and towards the destination. We focus on three well-known channels: the point-to-point channel, the multi-access channel and the broadcast channel, and study their performance improvement with relays. All over the dissertation, the following assumptions are taken: i) full-duplex operation at the relays, ii) transmit and receive channel state information available at all network nodes, and iii) time-invariant, memory-less fading.Firstly, we analyze the multiple-parallel relay channel, where a single source communicates to a single destination in the presence of N parallel relays. The capacity of the channel is lower bounded by means of the achievable rates with different relaying protocols, i.e. decode-and-forward, partial decode-and-forward, compress-and-forward and linear relaying. Likewise, a capacity upper bound is provided for comparison, derived using the max-flow-min-cut Theorem. Finally, for number of relays growing to infinity, the scaling laws of all achievable rates are presented, as well as the one of the upper bound.Next, the dissertation focusses on the multi-access channel (MAC) with multiple-parallel relays. The channel consists of multiple users simultaneously communicating to a single destination in the presence of N parallel relay nodes. We bound the capacity region of the channel using, again, the max-flow-min-cut Theorem and find achievable rate regions by means of decode-and-forward, linear relaying and compress-and-forward. In addition, we analyze the asymptotic performance of the obtained achievable sum-rates, given the number of users growing without bound. Such a study allows us to grasp the impact of multi-user diversity on access networks with relays.Finally, the dissertation considers the broadcast channel (BC) with multiple parallel relays. This consists of a single source communicating to multiple receivers in the presence of N parallel relays. For the channel, we derive achievable rate regions considering: i) dirty paper encoding at the source, and ii) decode-and-forward, linear relaying and compress-and-forward, respectively, at the relays. Moreover, for linear relaying, we prove that MAC-BC duality holds. That is, the achievable rate region of the BC is equal to that of the MAC with a sum-power constraint. Using this result, the computation of the channel's weighted sum-rate with linear relaying is notably simplified. Likewise, convex resource allocation algorithms can be derived.

Living beings, robotic and software artefacts can all be seen as agents acting and perceiving within an environment. When observed under that perspective, a new concept is accessible: information in the sense of Shannon. It has long been known that information and control are interrelated concepts. However it is only recently that this perspective has been better understood and used in order to study cognition. In this thesis, we build upon such an information-theoretic perspective and add some biologically motivated assumptions. They introduce various constraints on the capture, the processing, or the storage of information by an agent. Using such constraints it is possible to understand some limits on the control abilities of agents, and to derive algorithms that optimize these abilities. More specifically this thesis uses the recently introduced concept of empowerment, i.e. the ability to act upon the environment and perceive back the changes through the sensors. Maximizing this quantity leads to a wide range of cognitively interesting properties. This work studies some of these properties. One of them, the ability to capture information that is relevant for the perception-action loop of the agent, is deeply investigated and algorithms for exploiting this ability are presented. The second part of the thesis deals with the use of the information-theoretic framework when multiple agents are interacting with each other. Empowerment maximization in this context leads to two phenomena: the generation of complex structures, and the emergence of synchronised and potentially cooperative interactions. In this thesis, the first phenomenon is empirically investigated through various spatial scenarios in order to understand the kind of structures that are generated and under which conditions they appear. Connections are made between the second phenomenon and the concept of the multiple-access channel. Using recent developments of this information-theoretic model, it is possible to precisely study the kind of interactions that can occur, and the situations that lead to synchronised or cooperative behaviour. The general aim of this work is to give a comprehensive picture of the information-theoretic framework for studying the perception-action loop, bringing both single and multi-agents aspects together. The concepts presented in this thesis allows one to study some fundamental aspects of cognition, to engineer self-motivated robotic systems, or to drive self-organization in multi-agents systems.

Une communication est impulsive chaque fois que le signal portant des informations est intermittent dans le temps et que la transmission se produit à rafales. Des exemples du concept impulsife sont : les signaux radio impulsifs, c’est-à-dire des signaux très courts dans le temps; les signaux optiques utilisé dans les systèmes de télécommunications; certains signaux acoustiques et, en particulier, les impulsions produites par le système glottale; les signaux électriques modulés en position d’impulsions; une séquence d’événements dans une file d’attente; les trains de potentiels neuronaux dans le système neuronal. Ce paradigme de transmission est différent des communications continues traditionnelles et la compréhension des communications impulsives est donc essentielle. Afin d’affronter le problème des communications impulsives, le cadre de la recherche doit inclure les aspects suivants : la statistique d’interférence qui suit directement la structure des signaux impulsifs; l’interaction du signal impulsif avec le milieu physique; la possibilité pour les communications impulsives de coder l’information dans la structure temporelle. Cette thèse adresse une partie des questions précédentes et trace des lignes indicatives pour de futures recherches. En particulier, nous avons étudié: un système d'accès multiple où les utilisateurs adoptent des signaux avec étalement de spectre par saut temporel (time-hopping spread spectrum) pour communiquer vers un récepteur commun; un système avec un préfiltre à l'émetteur, et plus précisément un transmit matched filter, également connu comme time reversal dans la littérature de systèmes à bande ultra large; un modèle d'interférence pour des signaux impulsifs. / A communication is impulsive whenever the information-bearing signal is burst-like in time. Examples of the impulsive concept are: impulse-radio signals, that is, wireless signals occurring within short intervals of time; optical signals conveyed by photons; speech signals represented by sound pressure variations; pulse-position modulated electrical signals; a sequence of arrival/departure events in a queue; neural spike trains in the brain. Understanding impulsive communications requires to identify what is peculiar to this transmission paradigm, that is, different from traditional continuous communications.In order to address the problem of understanding impulsive vs. non-impulsive communications, the framework of investigation must include the following aspects: the different interference statistics directly following from the impulsive signal structure; the different interaction of the impulsive signal with the physical medium; the actual possibility for impulsive communications of coding information into the time structure, relaxing the implicit assumption made in continuous transmissions that time is a mere support. This thesis partially addresses a few of the above issues, and draws future lines of investigation. In particular, we studied: multiple access channels where each user adopts time-hopping spread-spectrum; systems using a specific prefilter at the transmitter side, namely the transmit matched filter (also known as time reversal), particularly suited for ultrawide bandwidhts; the distribution function of interference for impulsive systems in several different settings.

Une communication est impulsive chaque fois que le signal portant des informations est intermittent dans le temps et que la transmission se produit à rafales. Des exemples du concept impulsife sont : les signaux radio impulsifs, c’est-à-dire des signaux très courts dans le temps; les signaux optiques utilisé dans les systèmes de télécommunications; certains signaux acoustiques et, en particulier, les impulsions produites par le système glottale; les signaux électriques modulés en position d’impulsions; une séquence d’événements dans une file d’attente; les trains de potentiels neuronaux dans le système neuronal. Ce paradigme de transmission est différent des communications continues traditionnelles et la compréhension des communications impulsives est donc essentielle. Afin d’affronter le problème des communications impulsives, le cadre de la recherche doit inclure les aspects suivants : la statistique d’interférence qui suit directement la structure des signaux impulsifs; l’interaction du signal impulsif avec le milieu physique; la possibilité pour les communications impulsives de coder l’information dans la structure temporelle. Cette thèse adresse une partie des questions précédentes et trace des lignes indicatives pour de futures recherches. En particulier, nous avons étudié: un système d'accès multiple où les utilisateurs adoptent des signaux avec étalement de spectre par saut temporel (time-hopping spread spectrum) pour communiquer vers un récepteur commun; un système avec un préfiltre à l'émetteur, et plus précisément un transmit matched filter, également connu comme time reversal dans la littérature de systèmes à bande ultra large; un modèle d'interférence pour des signaux impulsifs. / A communication is impulsive whenever the information-bearing signal is burst-like in time. Examples of the impulsive concept are: impulse-radio signals, that is, wireless signals occurring within short intervals of time; optical signals conveyed by photons; speech signals represented by sound pressure variations; pulse-position modulated electrical signals; a sequence of arrival/departure events in a queue; neural spike trains in the brain. Understanding impulsive communications requires to identify what is peculiar to this transmission paradigm, that is, different from traditional continuous communications.In order to address the problem of understanding impulsive vs. non-impulsive communications, the framework of investigation must include the following aspects: the different interference statistics directly following from the impulsive signal structure; the different interaction of the impulsive signal with the physical medium; the actual possibility for impulsive communications of coding information into the time structure, relaxing the implicit assumption made in continuous transmissions that time is a mere support. This thesis partially addresses a few of the above issues, and draws future lines of investigation. In particular, we studied: multiple access channels where each user adopts time-hopping spread-spectrum; systems using a specific prefilter at the transmitter side, namely the transmit matched filter (also known as time reversal), particularly suited for ultrawide bandwidhts; the distribution function of interference for impulsive systems in several different settings.

In this thesis, we consider a vector Gaussian multiple access channel (MAC) with users demanding reliable communication at specific (Shannon-theoretic) rates. The objective is to assign vectors and powers to these users such that their rate requirements are met and the sum of powers received is minimum. We identify this power minimization problem as an instance of a separable convex optimization problem with linear ascending constraints. Under an ordering condition on the slopes of the functions at the origin, an algorithm that determines the optimum point in a finite number of steps is described. This provides a complete characterization of the minimum sum power for the vector Gaussian multiple access channel. Furthermore, we prove a strong duality between the above sum power minimization problem and the problem of sum rate maximization under power constraints. We then propose finite step algorithms to explicitly identify an assignment of vectors and powers that solve the above power minimization and sum rate maximization problems. The distinguishing feature of the proposed algorithms is the size of the output vector sets. In particular, we prove an upper bound on the size of the vector sets that is independent of the number of users. Finally, we restrict vectors to an orthonormal set. The goal is to identify an assignment of vectors (from an orthonormal set) to users such that the user rate requirements is met with minimum sum power. This is a combinatorial optimization problem. We study the complexity of the decision version of this problem. Our results indicate that when the dimensionality of the vector set is part of the input, the decision version is NP-complete.

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