In this paper, we propose hybrid GaN-FDSOI Front-End-Modules (FEMs) combined with lens-based mmWave antenna modules for energy-efficient multi-beamforming systems. X-topology Lattice-based balanced (LB) and unbalanced (LU) differential switches are designed and fabricated using FDSOI platforms for building scalable multi-beamforming FEMs. Unified mmWave and Baseband correlation technologies based on energy density and EVM (Error Vector Magnitude) metrics are introduced for low complexity (leveraging sparsity of MIMO channels) cost-effective multi-beamforming systems. Combination of convolutional accelerators with analog signal processing for linking beam-selection algorithms to stochastic-wave-shaping (SWS) will create new paradigms for eliminating the concept of “elements” in arrays and replacing it by the vision of “radiating current flows” over a textured surface (metasurface or metavolume). The radiating metasurfaces or metavolumes which can be conformal to the patterned optical lenses are fed by a limited number of emitting/receiving points. The use of time-modulated correlation functions will foster new system architectures with critical functionalities including direction of arrival (DOA) estimation and secure communications.

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. Due to the nature of the GNSS signal, that is, due to its wavelength, the distortion of the reflected signal may vary significantly depending on the reflecting surface and on the dynamic and height of the receiver. The latter does range from low earth orbit down to ground-based platforms. In this last case, the vicinity to the ground induces important interference between the direct and the reflected path which makes it difficult to process directly in order to obtain altimetry product. In this study, the feasibility of ground-based single antenna GNSS-R altimetry is studied and solutions are presented depending on the satellite elevation angle. To do so, maximum-likelihood-based algorithms - namely the CLEAN-RELAX Estimator and the Approximate Maximum Likelihood Estimator - are presented and applied to a set of scenarios.

This paper describes new impact analyses carried out on the imperfections of clock signals in the innovative but not well-known N-path Frequency Down-converter. Using an evolution of a previously developed analytical model, these analyses give more insight on the performance of the N-path Frequency Down-converter and allow more precise trouble-shooting of an Integrated Circuit realized on this principle. They are compared with measurements.

At their design time, GPS L5 and GALILEO E5a/E5b signals compatibility with TACAN/DME was studied for aeronautical users with altitude limited up to 40,000 feet, but not for space-borne users. Aircraft would see a few strong pulses and a mitigation technique as simple as pulse blanking would usually work as mitigation means. For space-borne GNSS receivers in Low Earth Orbit (LEO), if the larger free space losses lead to weaker received TACAN/DME signals, the number of beacons in visibility is much higher, reaching in the worst locations a total over two hundred with more than half of them having a peak power above or close to the noise floor, making time blanking a poor mitigation means. Therefore, other mitigation techniques performances need to be assessed in order to determine which techniques are best suited. A simulation tool was developed to compute the post correlation C/No degradation due to TACAN/DME on a LEO with and without mitigation means enabled. The equivalent post-correlation noise (No) increase due to TACAN/DME, or what remains after a mitigation technique is applied, is simulated using the Spectral Separation Coefficient (SSC) methodology to emulate the effect of GNSS signal de-spreading in the receiver correlation process. The part of the useful signal carrier suppressed by the application of a mitigation technique (time blanking and/or frequency notch filtering) is taken into account in the simulation. This study is focusing on radio-occultation (RO) missions which are the more sensitive to TACAN/DME interferences. Indeed, a medium-gain antenna (9–18 dB typical) is steered toward the earth limb resulting in having many TACAN/DME transmitters inside its main lobe. This configuration can lead to a high received power from them, as the LEO RO satellite is also in their main antenna lobe. In this configuration, the C/No degradation, plotted on a geographic map can reach up to 13.8 dB in the absence of mitigation over the European TACAN/DME hotspot. Several promising mitigation techniques have been included in the simulation tool to determine which one shall be implemented on board a LEO RO satellite mission: time domain pulse blanking, Frequency Domain Adaptive Filtering (FDAF) or hybrid blanking. We also considered implementing pulse cancellation, an attractive technique in theory, but not so in practice due to the deviation of the actual transmitted signals with respect to their theoretical models. As anticipated, pulse blanking does not perform well at the LEO orbit. It can be actually worse than doing nothing when there is a large number of TACAN/DME transmitters in visibility since it leads to a high loss in useful GNSS signals during the blanking process. As detailed in this paper, hybrid time domain and frequency domain methods are more effective when frequency notch filtering is applied over a limited time window . For FDAF, windows have fixed boundaries, independently of the presence of interfering pulses, whereas in the hybrid method, the time windows are centered on the detected pulses. The FDAF method reduced the peak interference down to 5.6 dB. The hybrid blanking method has the best performances with a worst degradation which can be reduced to 4.3 dB over the European hotspot.

In recent years, deep neural networks have obtained state-of-the-art performance in multiple imaging inverse problems ranging from medical imaging to computational photography. Networks are generally trained with pairs of signals and associated measurements. However, in various imaging problems, we usually only have access to compressed measurements of the underlying signals, hindering this learning-based approach. Learning from measurement data only is impossible in general, as the compressed observations do not contain information outside the range of the forward sensing operator. In this talk, I will present a new learning framework, called Equivariant Imaging, which overcomes this limitation by exploiting the invariance to transformations (translations, rotations, etc.) present in natural signals. I will also discuss necessary and sufficient conditions for learning without ground truth. Our proposed learning strategy performs as well as fully supervised methods and can handle noisy data. I will show results on various inverse problems, including sparse-view X-ray computed tomography, accelerated magnetic resonance imaging and image inpainting.

Periodic Nonuniform Samplings of order N (PNSN) are interleavings of periodic samplings. For a base period T, simple algorithms can be used to reconstruct functions of spectrum included in an union of N intervals δk of length 1/T. In this paper we study the behavior of these algorithms when applied to any function. We prove that they result in N (or less) foldings on , each of δk holding at most one folding.

In Global Navigation Satellite Systems, resilience to multipath remains an important open issue, being the limiting factor in several applications due to the environment specific nature of such harsh propagation conditions. In order to assess the multipath impact into the final system performance, accurate metrics are required. The multipath error envelope (MPEE), even if easy to handle, is limited to the study of the bias of a receiver architecture in a noise free environment. Moreover, when it is a flat zero-valued line, the MPEE becomes less informative about the parameter estimation performance. Considering an unbiased estimator, an alternative way to characterize an architecture is to evaluate its mean square error (MSE) and compare it to the corresponding Cram´er-Rao bound (CRB). In this work, a methodology to use both aforementioned tools is presented. First, the MPEE, which is an understandable metric. Secondly, the MSE convergence to the CRB, where one can clearly interpret the estimation performance in terms of signal-to-noise ratio or minimum path separation. These tools are then applied to a range of known multipath mitigation techniques. In addition, a new alternating projection multipath mitigation approach is proposed and analyzed.

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. Due to the nature of the GNSS signal, that is, due to its wavelength, the distortion of the reflected signal may vary significantly depending on the reflecting surface and on the dynamic and height of the receiver. The latter does range from low earth orbit down to ground-based platforms. In this last case, the vicinity to the ground induces important interference between the direct and the reflected path which makes it difficult to process directly in order to obtain altimetry product. In this presentation, after a brief description of the main features of the GNSS-R problem, the feasibility of ground-based single antenna GNSS-R altimetry is studied.

TCP (Transmission Control Protocol) Congestion control mechanism is an essential part of internet communications: it manages how fast the information is sent between two end points. That mechanism aims to achieve a compromise between 3 goals. The first is to achieve the maximum throughput for each flows, the second goal is to reduce the latency between the server and the client, and the last goal is to achieve fairness between each flows. The compromise between these 3 goals is very hard to achieve with human heuristics and basic models because of the ever increasing complexity of internet topologies. We choose to investigate machine learning solution in order optimize the Congestion Control mechanism. In this presentation, the bases of congestion control and the impact of machine learning on that mechanism will be explained.

Earth observation through satellite images is crucial to help economic activities as well as to monitor the impact of human activities on ecosystems. Current satellite systems are subjected to strong computational complexity constraints. Thus, image compression is performed onboard with specifically tailored algorithms while image denoising is performed on the ground. In this letter, we intend to address satellite image compression and denoising with neural networks. The first proposed approach uses a single neural architecture for joint onboard compression and denoising. The second proposed approach sequentially uses a first neural architecture for onboard compression and a second one for on ground denoising. For both approaches, the onboard architectures are lightened as much as possible, following the procedure proposed by Alves de Oliveira et al. (2021). The two approaches are shown to outperform the current satellite imaging system and their respective pros and cons are discussed.