ConsisFormer reduces WFM Transformer complexity by over 83% via adaptive token aggregation and feature interpolation while preserving performance on channel tasks.
Beam predic- tion based on large language models
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UNVERDICTED 3representative citing papers
A two-stage reinforcement learning system on pretrained LLMs aligns channel state information with user intents to generate adaptive, physically realizable link construction strategies for 6G that outperform conventional methods in experiments.
RadarPLM adapts PLMs for marine radar target detection with lightweight adaptation and selective fine-tuning based on online learning values, reporting at least 6.35% average detection gains in low SCR conditions.
citing papers explorer
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ConsisFormer: Compute-Efficient Transformer for Wireless Foundation Models Based on Channel Consistency
ConsisFormer reduces WFM Transformer complexity by over 83% via adaptive token aggregation and feature interpolation while preserving performance on channel tasks.
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Agentic Link Construction for Environment and Intent Aware 6G Communication
A two-stage reinforcement learning system on pretrained LLMs aligns channel state information with user intents to generate adaptive, physically realizable link construction strategies for 6G that outperform conventional methods in experiments.
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RadarPLM: Adapting Pre-trained Language Models for Marine Radar Target Detection by Selective Fine-tuning
RadarPLM adapts PLMs for marine radar target detection with lightweight adaptation and selective fine-tuning based on online learning values, reporting at least 6.35% average detection gains in low SCR conditions.