AIRIS2 : a Smart Gateway Diversity Algorithm for Very High-Throughput Satellite Systems

Satellite communication systems are shifting to higher frequency bands (Ka, Q/V, W) to support more data-intensive services and alleviate spectral congestion. However, the use of Extremely High Frequencies, typically above 20 GHz, causes significant tropospheric impairments, such as rain attenuation, which can causes system outages. To mitigate these effects, Smart Gateway Diversity (SGD) has emerged as a promising method for maximizing feeder link availability through an adaptive site diversity scheme. However, implementing such technique requires a decision-making policy to dynamically select the optimal set of gateways and prevent outages. This paper introduces AIRIS2, a deep learning algorithm that anticipates short-term rain events from rain attenuation measurement to enable efficient gateway switching. The approach is validated from five years of measured time series collected at Ka and Q/V bands at various sites and climatic conditions.

Improving the Estimation of Attenuation in Q/V Band Systems with a Kalman-Based Scintillation Filter

This paper presents the design and implementation of the Scintillation Filter by Kalman-colored algorithm (SciFi), which is used to remove tropospheric scintillation from Q/V bands total attenuation data series. In contrast to the classical methods using low-pass filters, the SciFi algorithm allows to estimate both the attenuation, its slope and a confidence interval. Moreover, the linear observer structure of the Kalman filter allows it to operate in real time. Therefore, the states and uncertainties estimated by SciFi can be used as input for Fade Mitigation Techniques (FMT) such as Adaptive Coding and Modulation (ACM) or Site Diversity (SD). In this article, we propose a method to tune the noise level based on recommendations approved by the ITU-R. Finally, some results of filtering on Alphasat experimental data are discussed.

Ranging-Based Localizability-Constrained Deployment of Mobile Robotic Networks

In cooperative localization schemes for robotic networks relying on noisy range measurements between agents, the achievable positioning accuracy strongly depends on the network geometry. This motivates the problem of planning robot trajectories in such multi-robot systems in a way that maintains high localization accuracy. We present potential-based planning methods, where localizability potentials are introduced to characterize the quality of the network geometry for cooperative position estimation. These potentials are based on Cram\'er Rao Lower Bounds (CRLB) and provide a theoretical lower bound on the error covariance achievable by any unbiased position estimator. In the process, we establish connections between CRLBs and the theory of graph rigidity, which has been previously used to plan the motion of robotic networks. We develop decentralized deployment algorithms appropriate for large networks, and we use equality-constrained CRLBs to extend the concept of localizability to scenarios where additional information about the relative positions of the ranging sensors is known. We illustrate the resulting robot deployment methodology through simulated examples.