Derives AoI lower bounds separating sensing and propagation terms for multi-robot systems on graphs, solves sensing allocation optimally via greedy water-filling, and constructs a shortest-path-tree conveyor architecture proven to attain the bound under full-conveyor conditions.
A tutorial on decomposition methods for network utility maximization
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GRMP crafts malicious updates via variational graph autoencoders on overheard benign feature graphs, degrading global LLM accuracy in federated IoA while evading statistical detection.
Proposes a three-layer framework using formal AI reasoning for verification, derivation, and discovery in wireless communications theory.
The paper overviews attention-based learning methods for spectrum cartography in LEO satellite networks to enable adaptive fusion of heterogeneous measurements for inference and resource allocation.
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AoI-Aware Multi-Robot Sensing and Transport on Connected Graphs
Derives AoI lower bounds separating sensing and propagation terms for multi-robot systems on graphs, solves sensing allocation optimally via greedy water-filling, and constructs a shortest-path-tree conveyor architecture proven to attain the bound under full-conveyor conditions.
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Graph Representation-based Model Poisoning on the Heterogeneous Internet of Agents
GRMP crafts malicious updates via variational graph autoencoders on overheard benign feature graphs, degrading global LLM accuracy in federated IoA while evading statistical detection.
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Rethinking Wireless Communications through Formal Mathematical AI Reasoning
Proposes a three-layer framework using formal AI reasoning for verification, derivation, and discovery in wireless communications theory.
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Learning-Based Spectrum Cartography in Low Earth Orbit Satellite Networks: An Overview
The paper overviews attention-based learning methods for spectrum cartography in LEO satellite networks to enable adaptive fusion of heterogeneous measurements for inference and resource allocation.