ConsisFormer reduces WFM Transformer complexity by over 83% via adaptive token aggregation and feature interpolation while preserving performance on channel tasks.
Multi-modal l arge models based beam prediction: An example empowered by DeepSeek,
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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.