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

2025-06-07

AI Summary

To address the lack of a general-purpose foundation model directly processing raw I/Q signals in wireless communications, this paper proposes IQFM—the first I/Q-domain foundation model designed for multiple tasks: modulation classification, angle-of-arrival (AoA) estimation, beam prediction, and RF fingerprinting. Methodologically, IQFM features: (1) a dedicated I/Q-domain architecture; (2) task-aware data augmentation—including cyclic time-shifting; and (3) contrastive self-supervised pretraining, a lightweight encoder, and LoRA-based fine-tuning. It eliminates reliance on handcrafted features or complex preprocessing and enables single-shot and few-shot cross-task generalization. Experiments demonstrate state-of-the-art performance: 99.67% accuracy in single-shot modulation classification and 65.45% accuracy in single-shot AoA estimation; 94.15% accuracy in beam prediction and 96.05% in RF fingerprinting with only 500 labeled samples—substantially outperforming supervised baselines.

Technology Category

• Machine Learning: Quantum Machine Learning
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Develops first foundational model for raw I/Q data in wireless communications
• Enables multi-task learning without heavy preprocessing or handcrafted features
• Demonstrates superior accuracy with minimal labeled data via self-supervised learning

Innovation

• First foundational model for raw IQ data
• Task-aware augmentation strategy for representation learning
• Lightweight encoder with contrastive self-supervised learning