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Found 6 papers, 0 papers with code
Opportunistic Information-Bottleneck for Goal-oriented Feature Extraction and Communication
This approach is particularly useful every time a device needs to transmit data (or features) to a server that has to fulfil an inference task, as it provides a principled way to extract the most relevant features for the task to be executed, while looking for the best trade-off between the size of the feature vector to be transmitted, inference accuracy, and complexity.
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.
Multi-user Goal-oriented Communications with Energy-efficient Edge Resource Management
A common challenge in running inference tasks from remote is to extract and transmit only the features that are most significant for the inference task.
Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies
Numerical simulations carried out over both synthetic and real data illustrate the good performance of the proposed sampling and reconstruction strategies for (possibly distributed) adaptive learning of signals defined over graphs.
Adaptive Least Mean Squares Estimation of Graph Signals
The aim of this paper is to propose a least mean squares (LMS) strategy for adaptive estimation of signals defined over graphs.
