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Found 12 papers, 0 papers with code
Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach
Recent advances in AI technologies have notably expanded device intelligence, fostering federation and cooperation among distributed AI agents.
Enabling Edge Artificial Intelligence via Goal-oriented Deep Neural Network Splitting
In this work, we explore the opportunity of DNN splitting at the edge of 6G wireless networks to enable low energy cooperative inference with target delay and accuracy with a goal-oriented perspective.
Goal-oriented Communications for the IoT: System Design and Adaptive Resource Optimization
Internet of Things (IoT) applications combine sensing, wireless communication, intelligence, and actuation, enabling the interaction among heterogeneous devices that collect and process considerable amounts of data.
6G goal-oriented communications: How to coexist with legacy systems?
Specifically, we address the scenario of an eMBB service, i. e., a user uploading a video stream, interfering with an edge inference system, in which a user uploads images to a Mobile Edge Host that runs a classification task.
Lyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces
In this paper, we propose a novel algorithm for energy-efficient, low-latency, accurate inference at the wireless edge, in the context of 6G networks endowed with reconfigurable intelligent surfaces (RISs).
Blue Communications for Edge Computing: the Reconfigurable Intelligent Surfaces Opportunity
In line with this vision, this paper proposes an online adaptive method to mitigate the EMFE under end-to-end delay constraints of a computation offloading service, in the context of RIS and multi-access edge computing (MEC)-aided wireless networks.
Energy-Efficient Dynamic Edge Computing with Electromagnetic Field Exposure Constraints
To the best of our knowledge, this is the first work addressing the problem of energy and exposure aware computation offloading.
Energy-Efficient Classification at the Wireless Edge with Reliability Guarantees
Learning at the edge is a challenging task from several perspectives, since data must be collected by end devices (e. g. sensors), possibly pre-processed (e. g. data compression), and finally processed remotely to output the result of training and/or inference phases.
Effective Goal-oriented 6G Communications: the Energy-aware Edge Inferencing Case
Also, ensemble inference is shown to improve system-wide energy efficiency and even achieve higher goal effectiveness, as compared to the standalone case for some system parameterizations.
Dynamic Edge Computing empowered by Reconfigurable Intelligent Surfaces
In this paper, we propose a novel algorithm for energy-efficient, low-latency dynamic mobile edge computing (MEC), in the context of beyond 5G networks endowed with Reconfigurable Intelligent Surfaces (RISs).
Energy Efficient Edge Computing: When Lyapunov Meets Distributed Reinforcement Learning
Then, based on Lyapunov stochastic optimization tools, we decouple the formulated problem into a CPU scheduling problem and a radio resource allocation problem to be solved in a per-slot basis.
Discontinuous Computation Offloading for Energy-Efficient Mobile Edge Computing
We propose a novel strategy for energy-efficient dynamic computation offloading, in the context of edge-computing-aided beyond 5G networks.
