In this work, we introduce a framework that enables the use of Syndrome-Based Neural Decoders (SBND) for highorder Bit-Interleaved Coded Modulations (BICM). To this end, we extend the previous results on SBND, for which the validity is limited to Binary Phase-Shift Keying (BPSK), by means of a theoretical channel modeling of the bit Log-Likelihood Ratio (bit-LLR) induced outputs.We implement the proposed SBND system for two polar codes (64, 32) and (128, 64), using a Recurrent Neural Network (RNN) and a Transformer-based architecture. Both implementations are compared in Bit Error Rate (BER) performance and computational complexity.

Time-delay and Doppler estimation is an operation performed in a plethora of engineering applications. A common hypothesis underlying most of the existing works is that the noise of the true and assumed signal model follows a centered complex normal distribution. However, everyday practice shows that the true signal model may differ from the nominal case and should be modeled by a non Gaussian distribution. In this paper, we analyse the asymptotic performance of the time-delay and Doppler estimation for the non-nominal scenario where the true noise model follows a centered complex elliptically symmetric (CES) distribution and the receiver assumed that the noise model follows a centered complex normal distribution. It turns out that performance bound under the misspecified model is equal to the one obtained for the well specified Gaussian scenario. In order to validate the theoretical outcomes, Monte Carlo simulations have been carried out.

In this talk, I will introduce a method for sequentially estimating both random and deterministic parameters within linear Gaussian discrete-time state space models. Following that, the performance of the obtained joint Maximum-a-posteriori Maximum likelihood estimator is assessed using hybrid Cramér-Rao bounds on two illustrative examples. Moving forward, I will exhibit the link between the Modified Cramér-Rao bound, a ubiquitous bound in non-standard estimation problems, and the hybrid Cramér-Rao bound under a mild constraint. Leveraging this link enables us to derive a recursive estimation scheme of the Modified Cramér-Rao bound (under constraint) for Markovian Dynamic Systems. Finally, I will discuss the extension of the Modified Cramér-Rao bound to matrix Lie groups.

This study investigates the Unsplittable Multi-Commodity Flow (UMCF) as a routing algorithm for LEO constellations. Usually, LEO routing schemes enable the Floyd-Warshall algorithm (Shortest Path) to minimize the end-to-end latency of the flows crossing the constellation. We propose to solve the UMCF problem associated with the system as a solution for routing over LEO. We use a heuristic algorithm based on randomized rounding known in the optimization literature to efficiently solve the UMCF problem. Furthermore, we explore the impact of choosing the first/last hop before entering/exiting the constellation. Using network simulation over Telesat constellation, we show that UMCF maximizes the end-to-end links usage, providing better routing while minimizing the delay and the congestion level, which is an issue today over new megaconstellations.

EVM (Error Vector Measurement) is used to measure the end-to-end quality of digital communication links. It comes from noise, linear and non-linear distortion, and interference if any. I propose a method to discriminate between random noise that is independent of the signal and distortion that depends on the signal. Interference is more complex to discriminate as it is not random but can be either synchronous with the signal or not. Echoes such as multipath cause linear distortion if they are static. However, variable echoes, such as those created in a reverberation chamber must be treated specifically.

Global navigation satellite systems (GNSS) are a key player in a plethora of applications, ranging from navigation and timing, to Earth observation or space weather characterization. For navigation purposes, interference scenarios are among the most challenging operation conditions, which clearly impact the maximum likelihood estimates (MLE) of the signal synchronization parameters. While several interference mitigation techniques exist, a theoretical analysis on the GNSS MLE performance degradation under interference, being fundamental for system/receiver design, is a missing tool. The main goal of this contribution is to provide such analysis, by deriving closed-form expressions of the misspecified Cramér-Rao (MCRB) bound and estimation bias, for a generic GNSS signal corrupted by an interference. The proposed bias and MCRB expressions are validated for a linear frequency modulation chirp signal interference.

Since time-delay and phase estimation is a fundamental task in a plethora of engineering fields, several CRB and MLE expressions have been derived for the past decades. In all these previous works, a common hypothesis is that the wave transmission process introduces an unknown phase which prevents from estimating both delay and transmission phase components. By revisiting this problem, including the derivation of the MLE and the associated CRB, we show that this well-admitted assertion is not true strictly: both informations can be estimated, but generally with a sub-optimal achievable MSE in the asymptotic region. Moreover, since practical problems exist where the transmission phase can be estimated apart, adding this additionnal measure to the observation model provides a setting allowing to explore the contribution of each signal component (carrier frequency, baseband signal and transmission phase measure) to the achievable MSE of time-delay and phase estimation in the asymptotic region.

A fusion method was recently proposed for ultrasound and magnetic resonance images for endometriosis diagnosis. This method combined the advantages of each modality, i.e., the good contrast and signal to noise ratio of the MR image and the good spatial resolution of the US image. The method was based on an inverse problem, performing a superresolution of the MR image and a denoising of the US image. A polynomial function was introduced to model the relationships between the gray levels of the MR and US images. This paper studies the potential interest of replacing this polynomial function by a non-parametric transformation built using the theory of reproducing kernel Hilbert spaces. Simulations conducted on a phantom and synthetic data allow the performance of the resulting fusion method to be appreciated.

In this work, we investigate the problem of neuralbased error correction decoding, and more specifically, the new so-called syndrome-based decoding technique introduced to tackle scalability in the training phase for larger code sizes. We improve on previous works in terms of allowing full decoding of the message rather than codewords, allowing thus the application to nonsystematic codes, and proving that the single-message training property is still viable. The suggested system is implemented and tested on polar codes of sizes (64,32) and (128,64), and a BCH of size (63,51), leading to a significant improvement in both Bit Error Rate (BER) and Frame Error Rate (FER), with gains between 0.3dB and 1dB for the implemented codes in the high Signal-to-Noise Ratio (SNR) regime.