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.
Differentiable projection-based learn to optimize in wireless network-part I: Convex constrained (non-)convex programming
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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.