Benoît Denis

Ibrahim Sbeity, Christophe Villien, Benoît Denis et al.

In the Global Navigation Satellite System (GNSS) context, the growing number of available satellites has lead to many challenges when it comes to choosing the most accurate pseudorange contributions, given the strong impact of biased measurements on positioning accuracy, particularly in single-epoch scenarios. This work leverages the potential of machine learning in predicting link-wise measurement quality factors and, hence, optimize measurement weighting. For this purpose, we use a customized matrix composed of heterogeneous features such as conditional pseudorange residuals and per-link satellite metrics (e.g., carrier-to-noise power density ratio and its empirical statistics, satellite elevation, carrier phase lock time). This matrix is then fed as an input to a recurrent neural network (RNN) (i.e., a long-short term memory (LSTM) network). Our experimental results on real data, obtained from extensive field measurements, demonstrate the high potential of our proposed solution being able to outperform traditional measurements weighting and selection strategies from state-of-the-art.

Alkesandra Malkova, Massih-Reza Amini, Benoit Denis et al.

In this paper, we address the problem of Received Signal Strength map reconstruction based on location-dependent radio measurements and utilizing side knowledge about the local region; for example, city plan, terrain height, gateway position. Depending on the quantity of such prior side information, we employ Neural Architecture Search to find an optimized Neural Network model with the best architecture for each of the supposed settings. We demonstrate that using additional side information enhances the final accuracy of the Received Signal Strength map reconstruction on three datasets that correspond to three major cities, particularly in sub-areas near the gateways where larger variations of the average received signal power are typically observed.

Aleksandra Malkova, Loic Pauletto, Christophe Villien et al.

In this paper, we present a Neural Network (NN) model based on Neural Architecture Search (NAS) and self-learning for received signal strength (RSS) map reconstruction out of sparse single-snapshot input measurements, in the case where data-augmentation by side deterministic simulations cannot be performed. The approach first finds an optimal NN architecture and simultaneously train the deduced model over some ground-truth measurements of a given (RSS) map. These ground-truth measurements along with the predictions of the model over a set of randomly chosen points are then used to train a second NN model having the same architecture. Experimental results show that signal predictions of this second model outperforms non-learning based interpolation state-of-the-art techniques and NN models with no architecture search on five large-scale maps of RSS measurements.

Iulia Tunaru, Benoît Denis, Régis Perrier et al.

As wireless ad hoc and mobile networks are emerging and the transferred data become more sensitive, information security measures should make use of all the available contextual resources to secure information flows. The physical layer security framework provides models, algorithms, and proofs of concept for generating pairwise symmetric keys over single links between two nodes within communication range. In this study, we focus on cooperative group key generation over multiple Impulse Radio - Ultra Wideband (IR-UWB) channels according to the source model. The main idea, proposed in previous work, consists in generating receiver-specific signals, also called s-signals, so that only the intended receiver has access to the non-observable channels corresponding to its non-adjacent links. Herein, we complete the analysis of the proposed protocol and investigate several signal processing algorithms to generate the s-signal expressed as a solution to a deconvolution problem in the case of IR-UWB. Our findings indicate that it is compulsory to add a parameterizable constraint to the searched s-signal and that the Expectation-Maximization algorithm can provide a stable self-parameterizable solution. Compared to physical layer key distribution methods, the proposed key generation protocol requires less traffic overhead for small cooperative groups while being robust at medium and high signal-to-noise ratios.