Satellite transmissions can suffer from high channel impairments, especially on the link between a satellite and a mobile end-user. To cope with these errors, physical and link layer reliability schemes have been introduced at the price of an end-to-end delay increase resulting in high jitter. Unfortunately, both the delay and the jitter negatively impacts on multimedia traffic. As a matter of fact, not taking into account the channel state greatly decreases the Quality of Experience (QoE) of VoIP users. In this paper, we propose to solve this issue by scheduling data transmission as a function of the channel condition. We first investigate existing scheduling mechanisms and analyze their performance for VoIP traffic with the objective to lower both latency and jitter, which are the most important metrics to achieve a consistent VoIP service. We select the best candidate among several schedulers and propose a novel algorithm specifically designed to carry VoIP over LEO constellations. Our simulations show that in some scenarios, we double the QoE of VoIP users.

In this paper we propose a new random access (RA) channel technique for the return link of satellite communications. It concerns slotted transmissions. This proposed method called Shared POsition Technique for Interfered random Transmissions (SPOTiT), is based on a shared knowledge between the receiver and each of the terminals. The shared information is about the time slot locations on which the terminal transmits its replicas as well as the preamble to use. The presented random version of SPOTiT aims to reduce the complexity of replicas localization process of the legacy technique Multireplica Decoding using Correlation based Localisation (MARSALA). It presents a less complex system without degrading performance and with no extra signaling information. Thus, SPOTiT is applied at the same level as MARSALA, i.e. when Contention Resolution Diversity Slotted Aloha (CRDSA) fails in retrieving more packets. This technique combined with CRDSA significantly reduces the number of data localization correlations, while maintaining the same performance as in CRDSA/MARSALA in terms of packet loss ratio and throughput.

This letter introduces a new low-complexity frequency-domain equalizer for continuous phase modulations (CPM). The derivation of a fractionally spaced representation for circular block-based CPM leads, without any approximation, to a simple yet efficient frequency-domain equalization. The proposed equalizer is compared to the state-of-the-art approaches. Simulation results show the equivalence in terms of performance with a lower or similar complexity.

This paper studies a statistical approach to detect and diagnose a particular type of vibration impacting the control surfaces of civil aircraft. The considered phenomenon is called Limit Cycle Oscillation (LCO). It consists of an unwanted sustained oscillation of a control surface due to the combined effect of aeroelastic phenomena and an increased level of mechanical free play in the elements that connect the control surface to the aerodynamic surface. The stateof-the-art for LCO prevention is mainly based on regular free play checks performed on ground during maintenance operations. The detection is mainly achieved by the crew, and especially the pilot who can fill in a so-called “vibration reporting sheet” to describe the phenomena felt during the flight. Thus, the pilot sensitivity to vibration is still the only reference for LCO detection. In the Flight Control System (FCS) of modern aircraft there exist already several certified algorithms for the detection of vibrations of different nature, which use dedicated local sensors to monitor the control surface behaviour. The same kind of sensors have been chosen in a local approach, which eases the isolation of the vibration sources. This paper studies a new statistical approach based on the Generalized Likelihood Ratio Test (GLRT) in order to improve the state-of-the-art for LCO detection and diagnosis. The test and its theoretical performance are derived and validated. A straightforward method compliant with real-time implementation constraint for LCO prediction is proposed. A Monte Carlo test campaign is performed in order to assess the robustness and the detection/diagnosis performance of the proposed algorithm under different operating conditions.

In this paper, we consider single carrier continuous phase modulations (CPM) over frequency selective time-varying channels. In this context, we propose a new low-complexity frequency-domain equalizer based on the minimum mean square error (MMSE) criterion exploiting efficiently the band structure of the associated channel matrix in the frequency domain. Simulations show that this band-MMSE equalizer exhibits a good performance complexity trade-off compared to existing solutions.

A control logic has a central role in many echo cancellation systems for optimizing the performance of adaptive filters, while estimating the echo path. For reliable control, accurate double-talk and channel change detectors are usually incorporated to the echo canceler. This work expands the usual detection strategy to define a classification problem characterizing four possible states of the echo canceler operation. The new formulation allows the use of decision theory to continuously control the transitions among the different modes of operation. The classification rule reduces to a low-cost statistics, for which it is possible to determine the probability of error under all hypotheses, allowing the classification performance to be accessed analytically. Monte Carlo simulations using synthetic and real data illustrate the reliability of the proposed method.

Texture characterization of natural images using the mathematical framework of multifractal (MF) analysis, enables the study of the fluctuations in the regularity of image intensity. Although successfully applied in various contexts, the use of MF analysis has so far been limited to the independent analysis of a single image, while the data available in applications are increasingly multivariate. This paper addresses this limitation and proposes a joint Bayesian model and associated estimation procedure for MF parameters of multivariate images. It builds on a recently introduced generic statistical model that enabled the Bayesian estimation of MF parameters for a single image and relies on the following original key contributions : First, we develop a novel Fourier domain statistical model for a single image that permits the use of a likelihood that is separable in the MF parameters via data augmentation. Second, a joint Bayesian model for multivariate images is formulated in which prior models based on gamma Markov random fields encode the assumption of the smooth evolution of MF parameters between the image components. The design of the likelihood and of conjugate prior models is such that exploitation of the conjugacy between the likelihood and prior models enables an efficient estimation procedure that can handle a large number of data components. Numerical simulations conducted using sequences of multifractal images demonstrate that the proposed procedure significantly outperforms previous univariate benchmark formulations at a competitive computational cost.

Trellis-based detector is an effective method to demodulate non-coherent continuous-phase modulated sequences. Most of them have been derived for the maximum likelihood sequence estimation setting, while only few contributions have been proposed for the maximum a posteriori (MAP) symbol detection, required when soft information is needed for iterative detection and decoding. In this letter, we derive a new symbol MAP non-coherent receiver with reduced state space representation compared with the existing extended state-space-based approaches. While having the same performance, it enables a lower complexity.