The proposed pretraining framework for safe DRL in CF-MIMO resource management doubles initial energy efficiency, achieves 4.7% higher final EE, maintains 1% delay violation rate, and cuts exploration steps by 50% compared to non-pretrained baselines while matching diffusion model performance at 14x
Neely,Stochastic network optimization with application to commu- nication and queueing systems
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4verdicts
UNVERDICTED 4representative citing papers
GELATO combines drift-plus-penalty Lyapunov control with generative entropy early exiting to adaptively offload tokens in device-edge speculative decoding, delivering higher throughput and lower energy use than prior distributed SD systems while preserving output quality.
A decomposed iterative algorithm for SIM-aided ISAC resource allocation yields up to 230% higher energy efficiency than a no-SIM baseline while meeting heterogeneous QoS for communications and sensing with fewer antennas.
Proposes a three-layer framework using formal AI reasoning for verification, derivation, and discovery in wireless communications theory.
citing papers explorer
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Generative Learning Enhanced Intelligent Resource Management for Cell-Free Delay Deterministic Communications
The proposed pretraining framework for safe DRL in CF-MIMO resource management doubles initial energy efficiency, achieves 4.7% higher final EE, maintains 1% delay violation rate, and cuts exploration steps by 50% compared to non-pretrained baselines while matching diffusion model performance at 14x
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GELATO: Generative Entropy- and Lyapunov-based Adaptive Token Offloading for Device-Edge Speculative LLM Inference
GELATO combines drift-plus-penalty Lyapunov control with generative entropy early exiting to adaptively offload tokens in device-edge speculative decoding, delivering higher throughput and lower energy use than prior distributed SD systems while preserving output quality.
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Resource Allocation and AoI-Aware Detection for ISAC with Stacked Intelligent Metasurfaces
A decomposed iterative algorithm for SIM-aided ISAC resource allocation yields up to 230% higher energy efficiency than a no-SIM baseline while meeting heterogeneous QoS for communications and sensing with fewer antennas.
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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.