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Le codage réseau est un paradigme qui permet une utilisation efficace du réseau. Il maximise le débit dans un réseau multi-saut en multicast et réduit le retard. Dans cette thèse, nous concentrons notre attention sur l’intégration du codage réseau aux applications multimédias, et en particulier aux systèmes avancès qui fournissent un service vidéo amélioré pour les utilisateurs. Nos contributions concernent plusieurs scénarios : un cadre de fonctions efficace pour la transmission de flux en directe qui utilise à la fois le codage réseau et le codage par description multiple, une nouvelle stratégie de transmission pour les réseaux sans fil avec perte qui garantit un compromis entre la résilience vis-à-vis des perte et la reduction du retard sur la base d’une optimisation débit-distorsion de l'ordonnancement des images vidéo, que nous avons également étendu au cas du streaming multi-vue interactive, un système replication sociale distribuée qui, en utilisant le réseau codage en relation et la connaissance des préférences des utilisateurs en termes de vue, est en mesure de sélectionner un schéma de réplication capable de fournir une vidéo de haute qualité en accédant seulement aux autres membres du groupe social, sans encourir le coût d’accès associé à une connexion à un serveur central et sans échanger des larges tables de métadonnées pour tenir trace des éléments répliqués, et, finalement, une étude sur l’utilisation de techniques de séparation aveugle de source -pour réduire l’overhead encouru par les schémas de codage réseau- basé sur des techniques de détection d’erreur telles que le codage de parité et la génération de message digest. / Network coding is a paradigm that allows an efficient use of the capacity of communication networks. It maximizes the throughput in a multi-hop multicast communication and reduces the delay. In this thesis, we focus our attention to the integration of the network coding framework to multimedia applications, and in particular to advanced systems that provide enhanced video services to the users. Our contributions concern several instances of advanced multimedia communications: an efficient framework for transmission of a live stream making joint use of network coding and multiple description coding; a novel transmission strategy for lossy wireless networks that guarantees a trade-off between loss resilience and short delay based on a rate-distortion optimized scheduling of the video frames, that we also extended to the case of interactive multi-view streaming; a distributed social caching system that, using network coding in conjunction with the knowledge of the users' preferences in terms of views, is able to select a replication scheme such that to provide a high video quality by accessing only other members of the social group without incurring the access cost associated with a connection to a central server and without exchanging large tables of metadata to keep track of the replicated parts; and, finally, a study on using blind source separation techniques to reduce the overhead incurred by network coding schemes based on error-detecting techniques such as parity coding and message digest generation. All our contributions are aimed at using network coding to enhance the quality of video transmission in terms of distortion and delay perceived

Digital Wireless communications has been one of the fastest growing communication techniques during the last decade. Today there exists several different communication systems that use wireless techniques. They share one common property that they transmit data through a radio interface. The radio channel is a tough channel that will both distort and disturb the transmitted signal in various ways. In Jörgen Nordberg's PhD-thesis "Signal Enhancement in Wireless Communications Systems" several different signal enhancement schemes are presented. They have the objective to minimize the impact of the channel. The main part of this thesis presents work on interference cancellation, i.e. how to reduce the impact of other interfering signals on the channel of interest. This is achieved by utilizing the spatial domain, i.e. the receiver is using several antennas to receive the transmitted signals. By using a multitude of antennas techniques like spatial diversity, adaptive antenna arrays, signal separation and beamforming can be applied to combat the interfering signals. In the single antenna case there is often a need to do channel equalization. Since, channel equalization is an inverse filtering, it will often result in estimation of equalization filter parameters of very high order. To reduce the both the complexity and improve the convergence speed of the equalization filter parameter estimation subband processing techniques can be used. In this case the received signal is separated up into different frequency bands (subbands) and decimated according to the bandwidth of the signal. The channel equalization problem is then solved for each subband at a lower sampling rate. Hence, the channel equalization problem is transformed from estimating the parameters of a high order filter into estimating several filter of much lower order. / Ett av områdena inom telekommunikation som har ökat mest de senaste åren är radio kommunikation. Det finns många olika varianter av trådlösa radio system, men de har alla en sak gemensamt, de överför information/data via ett radiogränssnitt. Signaler som sänds över en radiokanal kommer på grund av många olika anledningar att bli störda eller distorderade. I Jörgen Nordbergs doktorsavhandling ?Signal Enhancement in Wireless Communication Systems? presenteras flera metoder för att förbättra kvaliten i de mottagna signalerna vilket ger betydande kvalitetsförbättring. Huvuddelen av denna doktorsavhandling behandlar interferensundertrycking, d.v.s. hur man undertrycker oönskad störning på den egna radiokanalen. Dessa metoder är baserade på användning av flera antenner i mottagaren. Genom att ta emot signalerna med flera antenner så kan metoder såsom diversitetskombinering, adaptiva antenner, lobformning, signal separation användas för att förbättra kvaliteten i den mottagna signalen. Om mottagaren har en antenn så behövs oftast kanalutjämning för att förbättra den mottagna signalen och undertrycka intersymbolinterferens. Eftersom kanalutjämning är en typ av inversfiltrering leder detta ofta till estimering av filterparametrar av hög ordning. Estimeringsproblem av hög ordning leder ofta till komplexitetsproblem och konvergensproblem hos estimerings algoritmen. För att motverka dessa problem så presenteras i denna avhandling en kanalutjämnare som är baserad på subbandsteknik. I denna kanalutjämnare så delas den mottagna signalen upp i flera frekvensband (subband) som decimeras till en takt som motsvarar subbandets bandbredd varefter filterparametrarna estimeras i denna lägre takt. Därvid har estimeringsproblemet delats upp i flera små problem som kan hanteras enklare.

Frequency Hopping Spread Spectrum (FHSS) signaling is used across many devices operating in both regulated and unregulated bands. In either situation, if there is a malicious device operating within these bands, or more simply a user operating out of the required specifications, the identification this user important to insure communication link integrity and interference mitigation. The identification of a user involves the grouping of that users signal transmissions, and the separation of those transmission from transmissions of other users in a shared frequency band. Traditional signal separation methods often require difficult to obtain hardware fingerprinting characteristics or approximate geo-location estimates. This work will consider the characteristics of FHSS signals that can be extracted directly from signal detection. From estimates of these hopping characteristics, novel source separation with classic clustering algorithms can be performed. Background knowledge derived from the time domain representation of received waveforms can improve these clustering methods with the novel application of cannot-link pairwise constraints to signal separation. For equivalent clustering accuracy, constraint-based clustering tolerates higher parameter estimation error, caused by diminishing received signal-to-noise ratio (SNR), for example. Additionally, prior work does not fully cover the implications of detecting and estimating FHSS signals using image segmentation on a Time-Frequency (TF) waterfall. This work will compare several methods of FHSS signal detection, and discuss how each method may effect estimation accuracy and signal separation quality. The use of constraint-based clustering is shown to provide higher clustering accuracy, resulting in more reliable separation and identification of active users in comparison to traditional clustering methods. / Master of Science / The expansion of technology in areas such as smart homes and appliances, personal devices, smart vehicles, and many others, leads to more and more devices using common wireless communication techniques such as WiFi and Bluetooth. While the number of wirelessly connected users expands, the range of frequencies that support wireless communications does not. It is therefore essential that each of these devices unselfishly share the available wireless resources. If a device is using more resources than the required limits, or causing interference with other’s communications, this device will impact many others negatively and therefore preventative action must be taken to prevent further disruption in the wireless environment. Before action can be taken however, the device must first be identified in a mixture of other wireless activity. To identify a specific device, first, a wireless receiver must be in close enough proximity to detect the power that the malicious device is emitting through its wireless communication. This thesis provides a method that can be used to identify a problem user based only off of its wireless transmission behavior. The performance of this identification is shown with respect to the received signal power which represents the necessary range that a listening device must be to identify and separate a problem user from other cooperative users that are communicating wirelessly.

The threat level (specifically in this thesis, for aircraft) in an environment can be determined by analyzing radar signals. This task is critical and has to be solved fast and with high accuracy. The received electromagnetic pulses have to be identified in order to classify a radar emitter. Usually, there are several emitters transmitting radar pulses at the same time in an environment. These pulses need to be sorted into groups, where each group contains pulses from the same emitter. This thesis aims to find a fast and accurate solution to sort the pulses in parallel. The selected approach analyzes batches of pulses in parallel to exploit the advantages of a multi-threaded Central Processing Unit (CPU) or a Graphics Processing Unit (GPU). Firstly, a suitable clustering algorithm had to be selected. Secondly, an optimal batch size had to be determined to achieve high clustering performance and to rapidly process the batches of pulses in parallel. A quantitative method based on experiments was used to measure clustering performance, execution time, system response, and parallelism as a function of batch sizes when using the selected clustering algorithm. The algorithm selected for clustering the data was Density-based Spatial Clustering of Applications with Noise (DBSCAN) because of its advantages, such as not having to specify the number of clusters in advance, its ability to find arbitrary shapes of a cluster in a data set, and its low time complexity. The evaluation showed that implementing parallel batch processing is possible while still achieving high clustering performance, compared to a sequential implementation that used the maximum likelihood method.An optimal batch size in terms of data points and cutoff time is hard to determine since the batch size is very dependent on the input data. Therefore, one batch size might not be optimal in terms of clustering performance and system response for all streams of data. A solution could be to determine optimal batch sizes in advance for different streams of data, then adapt a batch size depending on the stream of data. However, with a high level of parallelism, an additional delay is introduced that depends on the difference between the time it takes to collect data points into a batch and the time it takes to process the batch, thus the system will be slower to output its result for a given batch compared to a sequential system. For a time-critical system, a high level of parallelism might be unsuitable since it leads to slower response times. / Genom analysering av radarsignaler i en miljö kan hotnivån bestämmas. Detta är en kritisk uppgift som måste lösas snabbt och med bra noggrannhet. För att kunna klassificera en specifik radar måste de elektromagnetiska pulserna identifieras. Vanligtvis sänder flera emittrar ut radarpulser samtidigt i en miljö. Dessa pulser måste sorteras i grupper, där varje grupp innehåller pulser från en och samma emitter. Målet med denna avhandling är att ta fram ett sätt att snabbt och korrekt sortera dessa pulser parallellt. Den valda metoden använder grupper av data som analyserades parallellt för att nyttja fördelar med en multitrådad Central Processing Unit (CPU) eller en Central Processing Unit (CPU) or a Graphics Processing Unit (GPU). Först behövde en klustringsalgoritm väljas och därefter en optimal gruppstorlek för den valda algoritmen. Gruppstorleken baserades på att grupperna kunde behandlas parallellt och snabbt, samt uppnå tillförlitlig klustring. En kvantitativ metod användes som baserades på experiment genom att mäta klustringens tillförlitlighet, exekveringstid, systemets svarstid och parallellitet som en funktion av gruppstorlek med avseende på den valda klustringsalgoritmen. Density-based Spatial Clustering of Applications with Noise (DBSCAN) valdes som algoritm på grund av dess förmåga att hitta kluster av olika former och storlekar utan att på förhand ange antalet kluster för en mängd datapunkter, samt dess låga tidskomplexitet. Resultaten från utvärderingen visade att det är möjligt att implementera ett system med grupper av pulser och uppnå bra och tillförlitlig klustring i jämförelse med en sekventiell implementation av maximum likelihood-metoden. En optimal gruppstorlek i antal datapunkter och cutoff tid är svårt att definiera då storleken är väldigt beroende på indata. Det vill säga, en gruppstorlek måste inte nödvändigtvis vara optimal för alla typer av indataströmmar i form av tillförlitlig klustring och svarstid för systemet. En lösning skulle vara att definiera optimala gruppstorlekar i förväg för olika indataströmmar, för att sedan kunna anpassa gruppstorleken efter indataströmmen. Det uppstår en fördröjning i systemet som är beroende av differensen mellan tiden det tar att skapa en grupp och exekveringstiden för att bearbeta en grupp. Denna fördröjning innebär att en parallell grupp-implementation aldrig kommer kunna vara lika snabb på att producera sin utdata som en sekventiell implementation. Detta betyder att det i ett tidskritiskt system förmodligen inte är optimalt att parallellisera mycket eftersom det leder till långsammare svarstid för systemet.

Στην παρούσα διπλωματική εργασία μελετώνται μέθοδοι διαχωρισμού σημάτων σε ανεξάρτητες συνιστώσες. Αφού δοθεί ο ορισμός του προβλήματος και μια αναφορά στις κυριότερες μεθόδους για την αντιμετώπισή του, γίνεται σαφές ότι δεν μπορούν να σχεδιαστούν γενικές μέθοδοι διαχωρισμού σημάτων. Παρά την πληθώρα των πρακτικών προβλημάτων στα οποία βρίσκει εφαρμογή το μαθηματικό πρότυπο, δεν είναι δυνατός ο σχεδιασμός μιας ενιαίας μεθόδου που να αντιμετωπίζει αποτελεσματικά όλες τις περιπτώσεις διαχωρισμού σημάτων. Ο αναγνώστης πληροφορείται για τις περιοχές έρευνας και ανάπτυξης των διαφόρων μεθόδων καθώς και για τις εφαρμογές τους σε διάφορους τομείς της σύγχρονης επιστήμης. Στη συνέχεια, υλοποιούνται κάποιες από αυτές τις μεθόδους και παρουσιάζονται τα αποτελέσματα προσομοίωσης πραγματικών πειραματικών δεδομένων που λήφθηκαν για την εκπόνηση της συγκεκριμένης διπλωματικής εργασίας. Τα αποτελέσματα εξάγονται με την χρήση και υλοποίηση αλγόριθμου επεξεργασίας των δεδομένων στο πρόγραμμα Matlab και μελετώνται εκτενέστερα με το πρόγραμμα Adobe Audition 1.5. Τέλος, παρουσιάζονται τα συμπεράσματα από την εφαρμογή του αλγόριθμου στα πραγματικά δεδομένα και δίνεται μια μαθηματική- θεωρητική βάση για την βελτιστοποίηση των μεθόδων διαχωρισμού σημάτων. / -

In this abstract, we present a nonlinear curvelet-based sparsitypromoting formulation for the primary-multiple separation problem. We show that these coherent signal components can be separated robustly by explicitly exploting the locality of curvelets in phase space (space-spatial frequency plane) and their ability to compress data volumes that contain wavefronts. This work is an extension of earlier results and the presented algorithms are shown to be stable under noise and moderately erroneous multiple predictions.

Separating unknown signal mixtures into their constituent parts is a difficult problem in signal processing called blind source separation. One of the benchmark problems in this area is the extraction of the fetal heartbeat from an electrocardiogram in which it is overshadowed by a strong maternal heartbeat. This thesis presents a study of a signal separation technique called independent component analysis (ICA), in order to assess its suitability for the maternal-fetal ECG separation problem. This includes an analysis of ICA on deterministic, stochastic, simulated and recorded ECG signals. The experiments presented in this thesis demonstrate that ICA is effective on linear mixtures of known simulated or recorded ECGs. The performance of ICA was measured using visual comparison, heart rate extraction, and energy, information theoretic, and fractal-based measures. ICA extraction of clinically recorded maternal-fetal ECGs mixtures, in which the source signals were unknown, were successful at recovering the fetal heart rate.

This report intends to verify the possibility that the FastICA algorithm could be applied to the GPS system to eliminate the impulsive noise from the receiver end. As the impulsive noise is so unpredictable in its pattern and of great energy level to swallow the signal we need, traditional signal selection methods exhibit no much use in dealing with this problem. Blind Source Separation seems to be a good way to solve this, but most of the other BSS algorithms beside FastICA showed more or less degrees of dependency on the pattern of the noise. In this thesis, the basic mathematic modelling of this advanced algorithm, along with the principles of the commonly used fast independent component analysis (fastICA) based on fixed-point algorithm are discussed. To verify that this method is useful under industrial use environment to remove the impulsive noises from digital BPSK modulated signals, an observation signal mixed with additive impulsive noise is generated and separated by fastICA method. And in the last part of the thesis, the fastICA algorithm is applied to the GPS receiver modeled in the SoftGNSS project and verified to be effective in industrial applications. The results have been analyzed. / 6

This dissertation introduces three novel multiuser detection approaches in Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems by blind signal separation (BSS) techniques. The conventional methodologies for multiuser detection have to retransmit channel state information (CSI) constantly from the transmitter in MIMO ODFM systems at the cost of economic efficiency, because they require more channel resources to improve the communication quality. Compared with the traditional methodologies, the proposed BSS methods are relatively efficient approaches without the unnecessary retransmission of channel state information. The current methodologies apply the space-time coding or the spatial multiplexing to implement an MIMO OFDM system, which requires relatively complex antenna design and allocation in the transmitter. The proposed Spatial Division Multiple Access (SDMA) method enables different mobile users to share the same bandwidth simultaneously in different geographical locations, and this scheme requires only one antenna for each mobile user. Therefore, it greatly simplifies the antenna design and allocation. The goal of this dissertation is to design and implement three blind multiuser detection schemes without knowing the channel state information or the channel transfer function in the SDMA-based uplink MIMO OFDM system. The proposed scenarios include: (a) the BSS-only scheme, (b) the BSS-Minimum Mean Square Error (MMSE) scheme, and (c) the BSS-Minimum Bit Error Ratio (MBER) scheme. The major contributions of the dissertation include: (a) the three proposed schemes save the commercially expensive cost of channel resources; (b) the proposed SDMA-based uplink MIMO OFDM system simplifies the requirements of antennas for mobile users; (c) the three proposed schemes obtain high parallel computing efficiency through paralleled subcarriers; (d) the proposed BSS-MBER scheme gains the best BER performance; (e) the proposed BSS-MMSE method yields the best computational efficiency; and (f) the proposed BSS-only scenario balances the BER performance and computational complexity.