IQFM A Wireless Foundational Model for I/Q Streams in AI-Native 6G

• URL: http://arxiv.org/abs/2506.06718v2
• Date: Fri, 20 Jun 2025 23:14:19 GMT
• Title: IQFM A Wireless Foundational Model for I/Q Streams in AI-Native 6G
• Abstract summary: This paper presents IQFM, the first I/Q signal foundational model for wireless communications.<n> IQFM supports diverse tasks: modulation classification, angle-of-arrival (AoA), beam prediction, and RF fingerprinting.<n>We also introduce a task-aware augmentation strategy that categorizes transformations into core augmentations.
• Score: 7.531126877550286
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Foundational models have shown remarkable potential in natural language processing and computer vision, yet remain in their infancy in wireless communications. While a few efforts have explored image-based modalities such as channel state information (CSI) and frequency spectrograms, foundational models that operate directly on raw IQ data remain largely unexplored. This paper presents, IQFM, the first I/Q signal foundational model for wireless communications. IQFM supporting diverse tasks: modulation classification, angle-of-arrival (AoA), beam prediction, and RF fingerprinting, without heavy preprocessing or handcrafted features. We also introduce a task-aware augmentation strategy that categorizes transformations into core augmentations, such as cyclic time shifting, and task-specific augmentations. This strategy forms the basis for structured, task-dependent representation learning within a contrastive self-supervised learning (SSL) framework. Using this strategy, the lightweight encoder, pre-trained via SSL on over-the-air multi-antenna IQ data, achieves up to 99.67% and 65.45% accuracy on modulation and AoA classification, respectively, using only one labeled sample per class, outperforming supervised baselines by up to 7x and 145x. The model also generalizes to out-of-distribution tasks; when adapted to new tasks using only 500 samples per class and minimal parameter updates via LoRA, the same frozen encoder achieves 94.15% on beam prediction (vs. 89.53% supervised), 50.00% on RML2016a modulation classification (vs. 49.30%), and 96.05% on RF fingerprinting (vs. 96.64%). These results demonstrate the potential of raw IQ-based foundational models as efficient, reusable encoders for multi-task learning in AI-native 6G systems.

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