This paper proposes a method for selective digital-to-analog conversion of a stationary band-pass random process submitted to interference. This method simultaneously performs the signal digital-to-analog conversion and the interference rejection from the observed process samples: it does not require any preliminary demodulation of the bandpass observed process and the filtering is performed in the time domain using an explicit expression of the filter taps. The method uses a particular non uniform sampling scheme called Periodic Non-uniform Sampling of order 2 (PNS2). The PNS2 sampling scheme uses two interleaved sample sequences. Appropriate formulas are derived to reconstruct the signal from its non-uniform samples while removing the interference by a selective filtering. The reconstruction from an infinite observation window (an infinite number of samples) is exact. However, in practical applications, the digital-to-analog conversion is generally performed in real time using a finite sliding observation window (a finite number of samples). Thus the reconstruction formulas should also demonstrate a high convergence rate. This paper proposes reconstruction formulas with increasing convergence rates using filters with increasingly regular transfer functions. The proposed method has been tested through simulations according to experimental parameters.

In a wide variety of applications, multifractal analysis has been abundantly used for the characterization and interpretation of scale invariance properties of experimental data. The parameter of multifractality or intermittency plays a central role in such an analysis as it enables the discrimination of large classes of scale invariance model processes. Yet, its estimation remains delicate in situations where sample size is limited since classical estimators, relying on simple linear regressions, exhibit large bias and / or variability, which severely limits the practical use of such estimates. To overcome such limitations, this contribution proposes an alternative estimation procedure. Its originality is twofold : First, we construct a generic semi-parametric model for the statistics of the logarithm of wavelet leaders (a declination, adapted for multifractal analysis, of classically used wavelet coefficients) for multiplicative cascade based multifractal processes. Second, we develop a Bayesian formulation of the estimation problem as well as an MCMC algorithm for approximating the associated posterior distributions and Bayesian estimators. The relevance of the proposed semi-parametric model and the performance of the Bayesian estimators are assessed by means of numerical simulations. We show that the proposed Bayesian procedure yields significantly improved estimation performance.

There has been a renewed interest at the Internet Engineering Task Force (IETF) in using Less-than-Best Effort (LBE) methods for background applications. IETF recently published a RFC for Low Extra Delay Background Transport (LEDBAT), a congestion control algorithm for LBE transmissions. This paper provides an analysis of LEDBAT performance over congested large bandwidth X delay product (LBDP) networks, and assesses the validity of having a fixed target queuing time. In particular, we lead a study of the impact of this target queuing delay when LEDBAT is used over 4G satellite networks. The rationale is to explore the possibility to grab the unused 4G satellite links' capacity to carry non-commercial traffic. We show that this is achievable with LEDBAT. However, depending on the fluctuation of the load, performance improvements could be obtained by properly setting the target value. We generalize this evaluation over different congested LBDP networks and confirm that the target value might need to be adjusted to networks' and traffic's characteristics. Further work will study whether and how this parameter should be dynamically adapted, and LEDBAT's congestion control improved.

Both WSNs (Wireless Sensor Networks) and observation satellites are able to get measurements from a geographic area. To interconnect these technologies, we propose to use a store-carry-and-forward architecture relying on the DTN (Disruption and Delay Tolerant Networking) Bundle Protocol. This architecture aims at being generic, so it is application-agnostic and suits a wide range of scenarios. WSN may collect sporadically large data volume while terrestrial stations communicating with Low Earth Orbit (LEO) satellites have to endure long link disruptions when the satellite is not in the line of sight. These sporadic growths within the WSN coupled with the large latency on satellite links require to schedule data to provide quality of service to several flows. We propose a scheduling policy based on deadline of Bundles and compare it with classical DTN solutions.

This paper focuses on the performance of time of arrival estimators for distress beacon signals which are defined by pulses with smooth transitions. These signals are used in the satellite-based search and rescue Cospas-Sarsat system. We propose a signal model based on sigmoidal functions. Closed-form expressions for the modified Cramér-Rao bounds associated with the parameters of this model are derived. The obtained expressions are easy to interpret since they analytically depend on the system parameters. Simulations conducted on realistic search and rescue signals show good agreement with the theoretical results.

We consider the adaptive beamforming or adaptive detection problem in the case of signal contaminated training samples, i.e., when the latter may contain a signal-like component. Since this results in a significant degradation of the signal to interference and noise ratio at the output of the adaptive filter, we investigate a scheme to jointly detect the contaminated samples and subsequently take this information into account for estimation of the disturbance covariance matrix. Towards this end, a Bayesian model is proposed, parameterized by binary variables indicating the presence/absence of signal-like components in the training samples. These variables, together with the signal amplitudes and the disturbance covariance matrix are jointly estimated using a minimum mean-square error (MMSE) approach. Two strategies are proposed to implement the MMSE estimator. First, a stochastic Markov Chain Monte Carlo method is presented based on Gibbs sampling. Then a computationally more efficient scheme based on variational Bayesian analysis is proposed. Numerical simulations attest to the improvement achieved by this method compared to conventional methods such as diagonal loading. A successful application to real radar data is also presented.

We propose a Bayesian approach to robust adaptive beamforming which entails considering the steering vector of interest as a random variable with some prior distribution. The latter can be tuned in a simple way to reflect how far is the actual steering vector from its presumed value. Two different priors are proposed, namely a Bingham prior distribution and a distribution that directly reveals and depends upon the angle between the true and presumed steering vector. Accordingly, a non-informative prior is assigned to the interference plus noise covariance matrix R, which can be viewed as a means to introduce diagonal loading in a Bayesian framework. The minimum mean square distance estimate of the steering vector as well as the minimum mean square error estimate of R are derived and implemented using a Gibbs sampling strategy. Numerical simulations show that the new beamformers possess a very good rate of convergence even in the presence of steering vector errors.

This paper addresses the problem of demodulating signals transmitted in the automatic identification system. The main characteristics of such signals consist of two points: (i) they are modulated using a trellis-coded modulation, more precisely a Gaussian minimum shift keying modulation; and (ii) they are submitted to a bit stuffing procedure, which makes more difficult the detection of the transmitted information bits. This paper presents several demodulation algorithms developed in different contexts: mono-user and multi-user transmissions, and known/unknown phase shift. The proposed receiver uses the cyclic redundancy check (CRC) present in the automatic identification system signals for error correction and not for error detection only. By using this CRC, a particular Viterbi algorithm, on the basis of a so-called extended trellis, is developed. This trellis is defined by extended states composed of a trellis code state and a CRC state. Moreover, specific conditional transitions are defined to take into account the possible presence of stuffing bits. The algorithms proposed in the multi-user scenario present a small increase of computation complexity with respect to the mono-user algorithms. Some performance results are presented for several scenarios in the context of the automatic identification system and compared with those of existing techniques developed in similar scenarios.

In the signal theory framework the "analytic signal” is a particular polar representation of a signal f (t). It defines an envelope (a modulus) and a phase (an argument) and these parameters are basic in communications. In this paper, we show that amplitude demodulations generally change envelopes and reduced phases. Consequently we show that recent results in laser communications about these parameters are erroneous.