A vision-language LLM forecasts HAP attitudes from telemetry for proactive beamforming, achieving 22.1% higher user service ratio and 12.5% higher sum-rate than baselines in simulations with mean latency of 36 ms.
Generative AI agents with large language model for satellite networks via a mixture of experts transmission
2 Pith papers cite this work. Polarity classification is still indexing.
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Collaborative LLM inference on LEO satellite networks via model splitting, pipeline parallelism, and adaptive compression reduces inference delay by up to 42% and communication overhead by up to 71% with less than 1% accuracy loss.
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Multimodal Large Language Model Enabled Robust Beamforming for HAP Downlink Communications
A vision-language LLM forecasts HAP attitudes from telemetry for proactive beamforming, achieving 22.1% higher user service ratio and 12.5% higher sum-rate than baselines in simulations with mean latency of 36 ms.
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Communication-Efficient Collaborative LLM Inference over LEO Satellite Networks
Collaborative LLM inference on LEO satellite networks via model splitting, pipeline parallelism, and adaptive compression reduces inference delay by up to 42% and communication overhead by up to 71% with less than 1% accuracy loss.