We consider the transmission of a continuous phase modulated (CPM) signal through a Gaussian channel affected by Doppler shifts. We propose a receiver robust to the Doppler shifts derived from a non-coherent detection criterion. We compare its performance to another non-coherent receiver based on a linear approximation of the CPM signal (Laurent decomposition) to which we add a Doppler compensation. Simulation results show that the first algorithm is robust to low-moderate Doppler shifts, while the second is robust to any one. We finally compare these two algorithms to delay-optimized differential detectors which do not require any Doppler shift estimation. We also provide complexity estimations to guide the possible complexity-performance trade-offs.

We consider the transmission of a continuous phase modulated (CPM) signal through a Gaussian channel affected by Doppler shifts. We focus on a receiver robust to the Doppler shift by proposing two different types of receiver derived from a non-coherent detection criterion : one based on a linear approximation of the CPM signal (Laurent decomposition) and the other based on its exact expression. Simulation results show that the first algorithm is robust to low-moderate Doppler shifts, while the second is robust to any one.

Pendant plus de trente ans, les signaux Global Navigation Satellite System (GNSS) ont été utilisés comme signaux d’opportunité comme en GNSS Reflectometry (GNSS-R). L’étude de la réflexion de ces signaux sur le sol peut en effet conduire à l’estimation de paramètres sur la surface de réflexion ou sur la hauteur du récepteur. Lorsque cette hauteur est faible, le récepteur est à site bas et la proximité du sol entraîne de fortes interférences entre les signaux direct et réfléchi ce qui rend difficile une estimation non biaisée des différentes observables. Cette difficulté peut néanmoins être levée grâce à des signaux GNSS occupant des bandes de plus en plus larges. For more than three decades, Global Navigation Satellite System (GNSS) signals have been seen as signals of opportunity as in GNSS Reflectometry (GNSS-R). The study of the reflections from the ground of such signals can indeed lead to many features regarding the reflecting surface and the receiver’s height. When this height is small, the receiver is said ground-based and the vicinity to the ground induces important interferences between the direct and the reflected path which make it difficult to process to obtain an unbiased altimetry product. However, this difficulty can be leveraged thanks to recent wideband GNSS signals.

Cet article étudie une nouvelle architecture récurrente basée sur l’attention, plus légère et moins coûteuse en temps de calcul qu’un réseau d’attention global. Nous détaillons en quoi ce type d’architecture permet d’atteindre de meilleures performances que des réseaux récurrents plus classiques, dans le cas de la régression de séries temporelles. Nous montrons son intérêt pour la prédiction de l’état d’un réseau de communication, et plus particulièrement pour la détection de la congestion.

In this article, we propose a new non-binary modulation which allows both Global Navigation Satellite Systems (GNSS) synchronization and the demodulation of non-binary symbols, without the need of a pilot signal, with the aim to provide a fast first position, velocity and time fix. The waveform is constructed as the product of i) a pseudo-random noise sequence with good auto-correlation and cross-correlation properties, and ii) a chirp spread spectrum family, which allows to demodulate non-binary symbols even if the signal phase is unknown. In order to demodulate the data, a bank of non-coherent matched filters is proposed. Because of the particular modulation structure, the receiver is capable to demodulate the navigation message faster while allowing the basic GNSS signal processing functionalities. Illustrative results are provided to support the discussion.

As a multidimensional extension of matrices, tensors (≥3D) are a natural tool for representing and processing multidimensional data arrays. Capturing and recovering this multidimensional data is a long-term challenge in image processing and related fields. In particular, four-dimensional (4D) spectral videos contain highly redundant information across the spatial (2D), spectral (1D) and temporal (1D) axes which can be exploited through a data-learned sparse basis or dictionary. However, in compressive spectral video acquisition (where the data is compressed), tackling dictionary learning is time-consuming since it increases the computational complexity and presents drawbacks for real-time processing, where offline learning is required. In this talk, I will briefly introduce tensor representation and decomposition, and its application on spectral videos in a compressive sensing scenario. Then, I will present an approach to exploit tensor sparse representation for jointly learning the transform basis and the recovering from compressed measurements of a spectral video. I will show some results of the performance of the developed framework compared with matrix-based recovery approaches, including dictionary learning.

Dark Matter is a very active field of research in modern particle physics both on the theory and the experimental side. In this seminar, I will give a pedagogical introduction to particle Dark Matter physics and sketch the main challenges of this area of research. I will first present the astrophysical and cosmological evidence for Dark Matter and its general properties that can be inferred from observation. I will then move to the field of particle physics and discuss the general requirements of a viable Dark Matter model as well as the on-going experimental efforts to detect a potential Darl Matter particle. I will finish by showing a specific Dark Matter model, the supersymmetric WIMP, on which I have worked during my thesis.

Satellite constellations have taken a new impetus in recent years with even more ambitious future rojects. The momentum has lasted long enough to raise the interest of the research community in addressing the adequacy of protocols mainly designed and used for terrestrial networks, to these satellite communications. There are thus versions of TCP specially designed for satellite networks [1]-[5]. Nevertheless, these previous works could turn out to be obsolete, in particular due to the recent versions of TCP based, for instance, on hybrid-type congestion control algorithms. The question we tackled in this thesis is : are the recent versions of TCP, such as CUBIC and BBR, able to meet the needs in such an environment ? We evaluated the differences between past and currently deployed TCP stacks. We gave an overview of the evolution of the use of protocols from a transport layer point of view of CUBIC and BBR. We identified the sources of delay variation in satellite constellations in order to study their impact and frequency. Modern variants of TCP accommodate this, especially for long flows. We then looked at the fairness between the flows with or without different variants. We highlighted some levels of unfairness in the heterogeneous contexts that are fairly consistent with those found in the terrestrial context. All of these studies were conducted through discrete event simulations but also emulations in order to obtain more realistic results.

Satellite constellations have taken a new impetus in recent years with even more ambitious future rojects. The momentum has lasted long enough to raise the interest of the research community in addressing the adequacy of protocols mainly designed and used for terrestrial networks, to these satellite communications. There are thus versions of TCP specially designed for satellite networks [1]-[5]. Nevertheless, these previous works could turn out to be obsolete, in particular due to the recent versions of TCP based, for instance, on hybrid-type congestion control algorithms. The question we tackled in this thesis is : are the recent versions of TCP, such as CUBIC and BBR, able to meet the needs in such an environment ? We evaluated the differences between past and currently deployed TCP stacks. We gave an overview of the evolution of the use of protocols from a transport layer point of view of CUBIC and BBR. We identified the sources of delay variation in satellite constellations in order to study their impact and frequency. Modern variants of TCP accommodate this, especially for long flows. We then looked at the fairness between the flows with or without different variants. We highlighted some levels of unfairness in the heterogeneous contexts that are fairly consistent with those found in the terrestrial context. All of these studies were conducted through discrete event simulations but also emulations in order to obtain more realistic results.

Multipath is one of the most challenging propagation conditions affecting Global Navigation Satellite Systems (GNSS), which must be mitigated in order to obtain reliable navigation information. In any case, the random multipath nature makes it difficult to anticipate and overcome. Therefore, for legacy GNSS signal performance assessment, modern GNSS signal design and future GNSS-based applications, robustness to multipath is a fundamental criterion. Different multipath metrics exist in the literature, such as the multipath error envelope, usually leading to analyses only valid for a dedicated receiver/signal combination and only providing information on the bias. This paper presents a general criterion to characterize the multipath robustness of a generic band-limited signal (e.g., GNSS or radar), considering the joint delay-Doppler and phase estimation. This criterion is based on the Cramr-Rao bound, which makes it universal, regardless the receiver architecture and the signal under analysis, and provides information on the actual achievable performance in terms of estimated time-delay (i.e., pseudo-range) and Doppler frequency variances.