Dans cette thèse, nous nous sommes intéressés au problème de codage de sources avec information adjacente au décodeur seulement. Plus précisément, nous avons considéré le cas où la distribution jointe entre la source et l'information adjacente n'est pas bien connue. Dans ce contexte, pour un problème de codage sans pertes, nous avons d'abord effectué une analyse de performance à l'aide d'outils de la théorie de l'information. Nous avons ensuite proposé un schéma de codage pratique efficace malgré le manque de connaissance sur la distribution de probabilité jointe. Ce schéma de codage s'appuie sur des codes LDPC non-binaires et sur un algorithme de type Espérance-Maximisation. Le problème du schéma de codage proposé, c'est que les codes LDPC non-binaires utilisés doivent être performants. C'est à dire qu'ils doivent être construits à partir de distributions de degrés qui permettent d'atteindre un débit proche des performances théoriques. Nous avons donc proposé une méthode d'optimisation des distributions de degrés des codes LDPC. Enfin, nous nous sommes intéressés à un cas de codage avec pertes. Nous avons supposé que le modèle de corrélation entre la source et l'information adjacente était décrit par un modèle de Markov caché à émissions Gaussiennes. Pour ce modèle, nous avons également effectué une analyse de performance, puis nous avons proposé un schéma de codage pratique. Ce schéma de codage s'appuie sur des codes LDPC non-binaires et sur une reconstruction MMSE. Ces deux composantes exploitent la structure avec mémoire du modèle de Markov caché. / In this thesis, we considered the problem of source coding with side information available at the decoder only. More in details, we considered the case where the joint distribution between the source and the side information is not perfectly known. In this context, we performed a performance analysis of the lossless source coding scheme. This performance analysis was realized from information theory tools. Then, we proposed a practical coding scheme able to deal with the uncertainty on the joint probability distribution. This coding scheme is based on non-binary LDPC codes and on an Expectation-Maximization algorithm. For this problem, a key issue is to design efficient LDPC codes. In particular, good code degree distributions have to be selected. Consequently, we proposed an optimization method for the selection of good degree distributions. To finish, we considered a lossy coding scheme. In this case, we assumed that the correlation channel between the source and the side information is described by a Hidden Markov Model with Gaussian emissions. For this model, we performed again some performance analysis and proposed a practical coding scheme. The proposed scheme is based on non-binary LDPC codes and on MMSE reconstruction using an MCMC method. In our solution, these two components are able to exploit the memory induced by the Hidden Markov model.

We are concerned with addressing the worst-case distributed source coding (DSC) problem in asymmetric and interactive communication scenarios and its application to data-gathering wireless sensor networks in enhancing their lifetime. First, we propose a unified canonical framework, obtained by considering different communication constraints and objectives, to address the variants of DSC problem. Second, as for the worst-case information-theoretic analysis, the notion of information entropy cannot be used, we propose information ambiguity, derive its various properties, and prove that it is a valid information measure. Third, for a few variants of our interest of DSC problem, we provide the communication protocols and prove their optimality. In a typical data-gathering sensor network, the base-station that wants to gather sensor data is often assumed to be much more resourceful with respect to energy, computation, and communication capabilities compared to sensor nodes. Therefore, we argue that in such networks, the base-station should bear the most of the burden of communication and computation in the network. Allowing the base-station and sensor nodes to interactively communicate with each other enables us to carry this out. Our definition of sensor network lifetime allows us to reduce the problem of maximizing the worst-case network lifetime to the problem of minimizing the number of bits communicated by the nodes in the worst-case, which is further reduced to the worst-case DSC problem in asymmetric and interactive communication scenarios, with the assumption that the base-station knows the support-set of sensor data. We demonstrate that the optimal solutions of the energy-oblivious DSC problem variants cannot be directly applied to the data-gathering sensor networks, as those may be inefficient in the energy-constrained sensor networks. We address a few energy-efficient variants of DSC problem and provide optimal communication protocols for the sensor networks, based on those variants. Finally, we combine distributed source coding with two other system level opportunities of channel coding and cooperative nature of the nodes to further enhance the lifetime of the sensor networks. We address various scenarios and demonstrate the dependence of the computational complexity of the network lifetime maximization problem on the complex interplay of above system-level opportunities.

This dissertation explores the intersection of information retrieval and universal source coding techniques and studies an optimal multidimensional source representation from an information theoretic point of view. Previous research on information retrieval particularly focus on learning probabilistic or deterministic source models based on primarily two different types of source representations, e.g., fixed-shape partitions or uniform regions. We study the limitations of the conventional source representations on capturing the semantics of the given multidimensional source sequences and propose a new type of primitive source representation generated by a universal source coding technique. We propose a multidimensional incremental parsing algorithm extended from the Lempel-Ziv incremental parsing and its three component schemes for multidimensional source coding. The properties of the proposed coding algorithm are exploited under two-dimensional lossless and lossy source coding. By the proposed coding algorithm, a given multidimensional source sequence is parsed into a number of variable-size patches. We call this methodology a parsed representation. Based on the source representation, we propose an information retrieval framework that analyzes a set of source sequences under a linguistic processing technique and implemented content-based image retrieval systems. We examine the relevance of the proposed source representation by comparing it with the conventional representation of visual information. To further extend the proposed framework, we apply a probabilistic linguistic processing technique to modeling the latent aspects of a set of documents. In addition, beyond the symbol-wise pattern matching paradigm employed in the source coding and the image retrieval systems, we devise a robust pattern matching that compares the first- and second-order statistics of source patches. Qualitative and quantitative analysis of the proposed framework justifies the superiority of the proposed information retrieval framework based on the parsed representation. The proposed source representation technique and the information retrieval frameworks encourage future work in exploiting a systematic way of understanding multidimensional sources that parallels a linguistic structure.

Abstract Wireless sensor networks (WSNs) consisting of battery-powered sensors are increasingly deployed for a myriad of Internet of Things applications, e.g., environmental, industrial, and healthcare monitoring. Since wireless access is typically the main contributor to battery usage, minimizing communications is crucial to prolong network lifetime and improve user experience. The objective of this thesis is to develop and analyze energy-efficient distributed compressed data acquisition techniques for WSNs. The thesis proposes four approaches to conserve sensors' energy by minimizing the amount of information each sensor has to transmit to meet given application requirements. The first part addresses a cross-layer design to minimize the sensors’ sum transmit power via joint optimization of resource allocation and multi-path routing. A distributed consensus optimization based algorithm is proposed to solve the problem. The algorithm is shown to have superior convergence compared to several baselines. The remaining parts deal with compressed sensing (CS) of sparse/compressible sources. The second part focuses on the distributed CS acquisition of spatially and temporally correlated sensor data streams. A CS algorithm based on sliding window and recursive decoding is developed. The method is shown to achieve higher reconstruction accuracy with fewer transmissions and less decoding delay and complexity compared to several baselines, and to progressively refine past estimates. The last two approaches incorporate the quantization of CS measurements and focus on lossy source coding. The third part addresses the distributed quantized CS (QCS) acquisition of correlated sparse sources. A distortion-rate optimized variable-rate QCS method is proposed. The method is shown to achieve higher distortion-rate performance than the baselines and to enable a trade-off between compression performance and encoding complexity via the pre-quantization of measurements. The fourth part investigates information-theoretic rate-distortion (RD) performance limits of single-sensor QCS. A lower bound to the best achievable compression — defined by the remote RD function (RDF) — is derived. A method to numerically approximate the remote RDF is proposed. The results compare practical QCS methods to the derived limits, and show a novel QCS method to approach the remote RDF. / Tiivistelmä Patterikäyttöisistä antureista koostuvat langattomat anturiverkot yleistyvät esineiden internetin myötä esim. ympäristö-, teollisuus-, ja terveydenhoitosovelluksissa. Koska langaton tiedonsiirto kuluttaa merkittävästi energiaa, kommunikoinnin minimointi on elintärkeää pidentämään verkon elinikää ja parantamaan käyttäjäkokemusta. Väitöskirjan tavoitteena on kehittää ja analysoida energiatehokkaita hajautettuja pakattuja datankeruumenetelmiä langattomiin anturiverkkoihin. Työssä ehdotetaan neljä lähestymistapaa, jotka säästävät anturien energiaa minimoimalla se tiedonsiirron määrä, mikä vaaditaan täyttämään sovelluksen asettamat kriteerit. Väitöskirjan ensimmäinen osa tarkastelee protokollakerrosten yhteissuunnittelua, jossa minimoidaan anturien yhteislähetysteho optimoimalla resurssiallokaatio ja monitiereititys. Ratkaisuksi ehdotetaan konsensukseen perustuva hajautettu algoritmi. Tulokset osoittavat algoritmin suppenemisominaisuuksien olevan verrokkejaan paremmat. Loppuosat keskittyvät harvojen lähteiden pakattuun havaintaan (compressed sensing, CS). Toinen osa keskittyy tila- ja aikatasossa korreloituneen anturidatan hajautettuun keräämiseen. Työssä kehitetään liukuvaan ikkunaan ja rekursiiviseen dekoodaukseen perustuva CS-algoritmi. Tulokset osoittavat menetelmän saavuttavan verrokkejaan korkeamman rekonstruktiotarkkuuden pienemmällä tiedonsiirrolla sekä dekoodausviiveellä ja -kompleksisuudella ja kykenevän asteittain parantamaan menneitä estimaatteja. Työn viimeiset osat sisällyttävät järjestelmämalliin CS-mittausten kvantisoinnin keskittyen häviölliseen lähdekoodaukseen. Kolmas osa käsittelee hajautettua korreloitujen harvojen signaalien kvantisoitua CS-havaintaa (quantized CS, QCS). Työssä ehdotetaan särön ja muuttuvan koodinopeuden välisen suhteen optimoiva QCS-menetelmä. Menetelmällä osoitetaan olevan verrokkejaan parempi pakkaustehokkuus sekä kyky painottaa suorituskyvyn ja enkooderin kompleksisuuden välillä mittausten esikvantisointia käyttäen. Neljäs osa tutkii informaatioteoreettisia, koodisuhde-särösuhteeseen perustuvia suorituskykyrajoja yhden anturin QCS-järjestelmässä. Parhaimmalle mahdolliselle pakkaustehokkuudelle johdetaan alaraja, sekä kehitetään menetelmä sen numeeriseen arviointiin. Tulokset vertaavat käytännön QCS-menetelmiä johdettuihin rajoihin, ja osoittavat ehdotetun QCS-menetelmän saavuttavan lähes optimaalinen suorituskyky.

No description available.

In wireless communication systems, the use of multiple antennas, also known as Multiple-Input Multiple-Output(MIMO) communications, is now a widely accepted and important technology for improving their reliability and throughput performance. However, in order to achieve the performance gains predicted by the theory, the transmitter and receiver need to have accurate and up-to-date Channel State Information(CSI) to overcome the vagaries of the fading environment. Traditionally, the CSI is obtained at the receiver by sending a known training sequence in the forward-link direction. This CSI has to be conveyed to the transmitter via a low-rate, low latency and noisy feedback channel in the reverse-link direction. This thesis addresses three key challenges in sending the CSI to the transmitter of a MIMO communication system over the reverse-link channel, and provides novel solutions to them. The first issue is that the available CSI at the receiver has to be quantized to a finite number of bits, sent over a noisy feedback channel, reconstructed at the transmitter, and used by the transmitter for precoding its data symbols. In particular, the CSI quantization technique has to be resilient to errors introduced by the noisy reverse-link channel, and it is of interest to design computationally simple, linear filters to mitigate these errors. The second issue addressed is the design of low latency and low decoding complexity error correction codes to provide protection against fading conditions and noise in the reverse-link channel. The third issue is to improve the resilience of the reverse-link channel to fading. The solution to the first problem is obtained by proposing two classes of receive filtering techniques, where the output of the source decoder is passed through a filter designed to reduce the overall distortion including the effect of the channel noise. This work combines the high resolution quantization theory and the optimal Minimum Mean Square Error(MMSE) filtering formulation to analyze, and optimize, the total end-to-end distortion. As a result, analytical expressions for the linear receive filters are obtained that minimize the total end-to-end distortion, given the quantization scheme and source(channel state) distribution. The solution to the second problem is obtained by proposing a new family of error correction codes, termed trellis coded block codes, where a trellis code and block code are concatenated in order to provide good coding gain as well as low latency and low complexity decoding. This code construction is made possible due to the existence of a uniform partitioning of linear block codes. The solution to the third problem is obtained by proposing three novel transmit precoding methods that are applicable to time-division-duplex systems, where the channel reciprocity can be exploited in designing the precoding scheme. The proposed precoding methods convert the Rayleigh fading MIMO channel into parallel Additive White Gaussian Noise(AWGN) channels with fixed gain, while satisfying an average transmit power constraint. Moreover, the receiver does not need to have knowledge of the CSI in order to decode the received data. These precoding methods are also extended to Rayleigh fading multi-user MIMO channels. Finally, all the above methods are applied to the problem of designing a low-rate, low-latency code for the noisy and fading reverse-link channel that is used for sending the CSI. Simulation results are provided to demonstrate the improvement in the forward-link data rate due to the proposed methods. Note that, although the three solutions are presented in the context of CSI feedback in MIMO communications, their development is fairly general in nature, and, consequently, the solutions are potentially applicable in other communication systems also.

Network coding was introduced as a means to increase throughput in communication networks when compared to routing. Network coding can be used not only to communicate messages from some nodes in the network to other nodes but are also useful when some nodes in a network are interested in computing some functions of information generated at some other nodes. Such a situation arises in sensor networks. In this work, we study three problems in network coding. First, we consider the functional source coding with side information problem wherein there is one source that generates a set of messages and one receiver which knows some functions of source messages and demands some other functions of source messages. Cognizant of the receiver's side information, the source aims to satisfy the demands of the receiver by making minimum number of coded transmissions over a noiseless channel. We use row-Latin rectangles to obtain optimal codes for a given functional source coding with side information problem. Next, we consider the multiple receiver extension of this problem, called the functional index coding problem, in which there are multiple receivers, each knowing and demanding disjoint sets of functions of source messages. The source broadcasts coded messages, called a functional index code, over a noiseless channel. For a given functional index coding problem, the restrictions the demands of the receivers pose on the code are represented using the generalized exclusive laws and it is shown that a code can be obtained using the confusion graph constructed using these laws. We present bounds on the size of an optimal code based on the parameters of the confusion graph. For the case of noisy broadcast channel, we provide a necessary and sufficient condition that a code must satisfy for correct decoding of desired functions at each receiver and obtain a lower bound on the length of an error-correcting functional index code. In the second problem, we explore relation between network function computation problems and functional index coding and Metroid representation problems. In a network computation problem, the demands of the sink nodes in a directed acyclic multichip network include functions of the source messages. We show that any network computation problem can be converted into a functional index coding problem and vice versa. We prove that a network code that satisfies all the sink demands in a network computation problem exists if and only if its corresponding functional index coding problem admits a functional index code of a specific length. Next, we establish a relation between network computation problems and representable mastoids. We show that a network computation problem in which the sinks demand linear functions of source messages admits a scalar linear solution if and only if it is matricidal with respect to a representable Metroid whose representation fulfils certain constraints dictated by the network computation problem. Finally, we study the usage of the sum-product (SP) algorithm for decoding network codes. Though lot of methods to obtain network codes exist, the decoding procedure and complexity have not received much attention. We propose a SP algorithm based decoder for network codes which can be used to decode both linear and nonlinear network codes. We pose the decoding problem at a sink node as a marginalize a product function (MPF) problem over the Boolean smearing and use the SP algorithm on a suitably constructed factor graph to perform decoding. We propose and demonstrate the usage of trace back to reduce the number of operations required to perform SP decoding. The computational complexity of performing SP decoding with and without trace back is obtained. For nonlinear network codes, we define fast decidability of a network code at sinks that demand all the source messages and identify a sufficient condition for the same. Next, for network function computation problems, we present an MPF formulation for function computation at a sink node and use the SP algorithm to obtain the value of the demanded function.

Cognitive Radios are emerging communication systems which efficiently utilize the unused licensed radio spectrum called spectral holes. They run Spectrum sensing algorithms to identify these spectral holes. These holes need to be identified at very low SNR (<=-20 dB) under multipath fading, unknown channel gains and noise power. Cooperative spectrum sensing which exploits spatial diversity has been found to be particularly effective in this rather daunting endeavor. However despite many recent studies, several open issues need to be addressed for such algorithms. In this thesis we provide some novel cooperative distributed algorithms and study their performance. We develop an energy efficient detector with low detection delay using decentralized sequential hypothesis testing. Our algorithm at the Cognitive Radios employ an asynchronous transmission scheme which takes into account the noise at the fusion center. We have developed a distributed algorithm, DualSPRT, in which Cognitive Radios (secondary users) sequentially collect the observations, make local decisions and send them to the fusion center. The fusion center sequentially processes these received local decisions corrupted by Gaussian noise to arrive at a final decision. Asymptotically, this algorithm is shown to achieve the performance of the optimal centralized test, which does not consider fusion center noise. We also theoretically analyze its probability of error and average detection delay. Even though DualSPRT performs asymptotically well, a modification at the fusion node provides more control over the design of the algorithm parameters which then performs better at the usual operating probabilities of error in Cognitive Radio systems. We also analyze the modified algorithm theoretically. DualSPRT requires full knowledge of channel gains. Thus we extend the algorithm to take care the imperfections in channel gain estimates. We also consider the case when the knowledge about the noise power and channel gain statistic is not available at the Cognitive Radios. This problem is framed as a universal sequential hypothesis testing problem. We use easily implementable universal lossless source codes to propose simple algorithms for such a setup. Asymptotic performance of the algorithm is presented. A cooperative algorithm is also designed for such a scenario. Finally, decentralized multihypothesis sequential tests, which are relevant when the interest is to detect not only the presence of primary users but also their identity among multiple primary users, are also considered. Using the insight gained from binary hypothesis case, two new algorithms are proposed.

<p>La distribution quantique de clés est une technique cryptographique permettant l'échange de clés secrètes dont la confidentialité est garantie par les lois de la mécanique quantique. Le comportement particulier des particules élémentaires est exploité. En effet, en mécanique quantique, toute mesure sur l'état d'une particule modifie irrémédiablement cet état. En jouant sur cette propriété, deux parties, souvent appelées Alice et Bob, peuvent encoder une clé secrète dans des porteurs quantiques tels que des photons uniques. Toute tentative d'espionnage demande à l'espion, Eve, une mesure de l'état du photon qui transmet un bit de clé et donc se traduit par une perturbation de l'état. Alice et Bob peuvent alors se rendre compte de la présence d'Eve par un nombre inhabituel d'erreurs de transmission.</p><p><p><p>L'information échangée par la distribution quantique n'est pas directement utilisable mais doit être d'abord traitée. Les erreurs de transmissions, qu'elles soient dues à un espion ou simplement à du bruit dans le canal de communication, doivent être corrigées grâce à une technique appelée réconciliation. Ensuite, la connaissance partielle d'un espion qui n'aurait perturbé qu'une partie des porteurs doit être supprimée de la clé finale grâce à une technique dite d'amplification de confidentialité.</p><p><p><p>Cette thèse s'inscrit dans le contexte de la distribution quantique de clé où les porteurs sont des états continus de la lumière. En particulier, une partie importante de ce travail est consacrée au traitement de l'information continue échangée par un protocole particulier de distribution quantique de clés, où les porteurs sont des états cohérents de la lumière. La nature continue de cette information implique des aménagements particuliers des techniques de réconciliation, qui ont surtout été développées pour traiter l'information binaire. Nous proposons une technique dite de réconciliation en tranches qui permet de traiter efficacement l'information continue. L'ensemble des techniques développées a été utilisé en collaboration avec l'Institut d'Optique à Orsay, France, pour produire la première expérience de distribution quantique de clés au moyen d'états cohérents de la lumière modulés continuement.</p><p><p><p>D'autres aspects importants sont également traités dans cette thèse, tels que la mise en perspective de la distribution quantique de clés dans un contexte cryptographique, la spécification d'un protocole complet, la création de nouvelles techniques d'amplification de confidentialité plus rapides à mettre en œuvre ou l'étude théorique et pratique d'algorithmes alternatifs de réconciliation.</p><p><p><p>Enfin, nous étudions la sécurité du protocole à états cohérents en établissant son équivalence à un protocole de purification d'intrication. Sans entrer dans les détails, cette équivalence, formelle, permet de valider la robustesse du protocole contre tout type d'espionnage, même le plus compliqué possible, permis par les lois de la mécanique quantique. En particulier, nous généralisons l'algorithme de réconciliation en tranches pour le transformer en un protocole de purification et nous établissons ainsi un protocole de distribution quantique sûr contre toute stratégie d'espionnage.</p><p><p><p>Quantum key distribution is a cryptographic technique, which allows to exchange secret keys whose confidentiality is guaranteed by the laws of quantum mechanics. The strange behavior of elementary particles is exploited. In quantum mechnics, any measurement of the state of a particle irreversibly modifies this state. By taking advantage of this property, two parties, often called Alice and bob, can encode a secret key into quatum information carriers such as single photons. Any attempt at eavesdropping requires the spy, Eve, to measure the state of the photon and thus to perturb this state. Alice and Bob can then be aware of Eve's presence by a unusually high number of transmission errors.</p><p><p><p>The information exchanged by quantum key distribution is not directly usable but must first be processed. Transmission errors, whether they are caused by an eavesdropper or simply by noise in the transmission channel, must be corrected with a technique called reconciliation. Then, the partial knowledge of an eavesdropper, who would perturb only a fraction of the carriers, must be wiped out from the final key thanks to a technique called privacy amplification.</p><p><p><p>The context of this thesis is the quantum key distribution with continuous states of light as carriers. An important part of this work deals with the processing of continuous information exchanged by a particular protocol, where the carriers are coherent states of light. The continuous nature of information in this case implies peculiar changes to the reconciliation techniques, which have mostly been developed to process binary information. We propose a technique called sliced error correction, which allows to efficiently process continuous information. The set of the developed techniques was used in collaboration with the Institut d'Optique, Orsay, France, to set up the first experiment of quantum key distribution with continuously-modulated coherent states of light.</p><p><p><p>Other important aspects are also treated in this thesis, such as placing quantum key distribution in the context of a cryptosystem, the specification of a complete protocol, the creation of new techniques for faster privacy amplification or the theoretical and practical study of alternate reconciliation algorithms.</p><p><p><p>Finally, we study the security of the coherent state protocol by analyzing its equivalence with an entanglement purification protocol. Without going into the details, this formal equivalence allows to validate the robustness of the protocol against any kind of eavesdropping, even the most intricate one allowed by the laws of quantum mechanics. In particular, we generalize the sliced error correction algorithm so as to transform it into a purification protocol and we thus establish a quantum key distribution protocol secure against any eavesdropping strategy.</p> / Doctorat en sciences appliquées / info:eu-repo/semantics/nonPublished