Energy-efficient satellite joint computation and communication

The emerging interest in satellite networks will be a key driver in the path to 6G. The satellite segment must be conceived beyond a mere relay system, where nodes can process data and offload the terrestrial segment. Besides, evidence suggests that energy consumption is among the most important factors for the design of future communication networks. For this motivation, we introduce Sat2C, an energy-efficient algorithm for satellite joint routing, radio resource allocation and task offloading for latency-constrained services. We develop a novel energy model that incorporates the power amplifier subsystem and changes the geometry of the problem. Regarding the routing task, we propose the SHIELD algorithm, based on the submodularity framework and which achieves Pareto-efficient routes. Besides, the RRM problem is formulated as a log-log convex program. The experimental results reveal that Sat2C has low computational complexity, provides routes with low variance in the mean distance and the transmission powers are optimal to ensure energy minimization

Over the air computing for satellite networks in 6G

6G and beyond networks will merge communication and computation capabilities in order to adapt to changes. As they will consist of many sensors gathering information from its environment, new schemes for managing these large amounts of data are needed. For this purpose, we review Over the Air (OTA) computing in the context of estimation and detection. For distributed scenarios, such as a Wireless Sensor Network, it has been proven that a separation theorem does not necessarily hold, whereas analog schemes may outperform digital designs. We outline existing gaps in the literature, evincing that current state of the art requires a theoretical framework based on analog and hybrid digital-analog schemes that will boost the evolution of OTA computing. Furthermore, we motivate the development of 3D networks based on OTA schemes, where satellites function as sensors. We discuss its integration within the satellite segment, delineate current challenges and present a variety of use cases that benefit from OTA computing in 3D networks.This work has received funding by the Spanish ministry of science and innovation under project IRENE (PID2020-115323RB-C31) funded by MCIN/AEI/10.13039/501100011033.Peer ReviewedPostprint (author's final draft

Edge computing and communication for energy-efficient earth surveillance with LEO satellites

Modern satellites deployed in low Earth orbit (LEO) accommodate processing payloads that can be exploited for edge computing. Furthermore, by implementing inter-satellite links, the LEO satellites in a constellation can route the data end-toend (E2E). These capabilities can be exploited to greatly improve the current store-and-forward approaches in Earth surveillance systems. However, they give rise to an NP-hard problem of joint communication and edge computing resource management (RM). In this paper, we propose an algorithm that allows the satellites to select between computing the tasks at the edge or at a cloud server and to allocate an adequate power for communication. The overall objective is to minimize the energy consumption at the satellites while fulfilling specific service E2E latency constraints for the computing tasks. Experimental results show that our algorithm achieves energy savings of up to 18% when compared to the selected benchmarks with either 1) fixed edge computing decisions or 2) maximum power allocation.Part of the research has been supported by the project SatNEx-V, co-funded by the European Space Agency (ESA). This work has also received funding by the Spanish ministry of science and innovation under project IRENE (PID2020-115323RB-C31 / AEI / 10.13039/501100011033) and grant from the Spanish ministry of economic affairs and digital transformation and of the European union – NextGenerationEU [UNICO-5G I+D/AROMA3D-Space (TSI-063000-2021-70).Peer ReviewedPostprint (author's final draft

Modelat FE-DNN per a la predicció d'activacions EMG induïdes per TMS

Considering the vast amount of degrees of freedom that the Central Nervous System holds, synergies, understood as building blocks led by the linear combination of multiple muscles, a novel hypothesis to understand the generation of movement. This project suggests a Deep Learning approach to build two predictive kinematic model based on muscles and synergies. Both models will be built with Convolutional Neural networks, whose inputs will be Transcranial Magnetic Stimulations and the outputs, the evoked muscle responses monitored through Electromyography. By comparing the predictions from both models, the results evinced that synergies propose a more robust model to understand movement control than direct connections to muscles, even though they are not able to fully characterize the movement by themselves.El Sistema Nervioso Central concentra una gran cantidad de grados de libertad a la hora de generar movimiento. Por eso, la inclusión de las sinergias, entendidas como elementos básicos caracterizados por la combinación lineal de múltiples músculos, es una hipótesis innovadora para entender la concepción del movimiento. Este proyecto sugiere una estrategia basada en Deep Learning con el propósito de elaborar dos modelos predictivos que expliquen el movimiento basados en músculos y sinergias. Ambos modelos se construirán mediante Redes Neuronales Convolucionales, cuyas entradas serán Estimulaciones Magnéticas Transcraneanas y las salidas, las respuestas evocadas a los músculos y monitoreadas con Electromiografía. La comparativa de los resultados obtenidos demuestra que las sinergias proponen un modelo más robusto para entender el control del movimiento que las conexiones directas a los músculos, aunque no son capaces de caracterizar el movimiento por si mismas.El Sistema Nerviós Central concentra una gran quantitat de graus de llibertat a l'hora de generar moviments. Per això, la inclusió de les sinèrgies, enteses com elements bàsics caracteritzats per la combinació lineal de múltiples músculs, és una hipòtesi innovadora per entendre la concepció del moviment. Aquest projecte suggereix una estratègia basada en Deep Learning per tal d'elaborar dos models predictius que expliquin el moviment basat en músculs i sinèrgies. Ambdós models es construiran mitjançant Xarxes Neuronals Convolucionals, les entrades de les quals seran Estimulacions Magnètiques Transcranial i les sortides, les respostes evocades als músculs i monitoritzades amb Electromiografia. La comparativa dels resultats obtinguts demostra que les sinèrgies proposen un model més robust per entendre el control del moviment que les connexions directes als músculs, encara que no són capaces de caracteritzar el moviment per elles soles

Modelat FE-DNN per a la predicció d'activacions EMG induïdes per TMS

Considering the vast amount of degrees of freedom that the Central Nervous System holds, synergies, understood as building blocks led by the linear combination of multiple muscles, a novel hypothesis to understand the generation of movement. This project suggests a Deep Learning approach to build two predictive kinematic model based on muscles and synergies. Both models will be built with Convolutional Neural networks, whose inputs will be Transcranial Magnetic Stimulations and the outputs, the evoked muscle responses monitored through Electromyography. By comparing the predictions from both models, the results evinced that synergies propose a more robust model to understand movement control than direct connections to muscles, even though they are not able to fully characterize the movement by themselves.El Sistema Nervioso Central concentra una gran cantidad de grados de libertad a la hora de generar movimiento. Por eso, la inclusión de las sinergias, entendidas como elementos básicos caracterizados por la combinación lineal de múltiples músculos, es una hipótesis innovadora para entender la concepción del movimiento. Este proyecto sugiere una estrategia basada en Deep Learning con el propósito de elaborar dos modelos predictivos que expliquen el movimiento basados en músculos y sinergias. Ambos modelos se construirán mediante Redes Neuronales Convolucionales, cuyas entradas serán Estimulaciones Magnéticas Transcraneanas y las salidas, las respuestas evocadas a los músculos y monitoreadas con Electromiografía. La comparativa de los resultados obtenidos demuestra que las sinergias proponen un modelo más robusto para entender el control del movimiento que las conexiones directas a los músculos, aunque no son capaces de caracterizar el movimiento por si mismas.El Sistema Nerviós Central concentra una gran quantitat de graus de llibertat a l'hora de generar moviments. Per això, la inclusió de les sinèrgies, enteses com elements bàsics caracteritzats per la combinació lineal de múltiples músculs, és una hipòtesi innovadora per entendre la concepció del moviment. Aquest projecte suggereix una estratègia basada en Deep Learning per tal d'elaborar dos models predictius que expliquin el moviment basat en músculs i sinèrgies. Ambdós models es construiran mitjançant Xarxes Neuronals Convolucionals, les entrades de les quals seran Estimulacions Magnètiques Transcranial i les sortides, les respostes evocades als músculs i monitoritzades amb Electromiografia. La comparativa dels resultats obtinguts demostra que les sinèrgies proposen un model més robust per entendre el control del moviment que les connexions directes als músculs, encara que no són capaces de caracteritzar el moviment per elles soles