Title: IQFM A Wireless Foundational Model for I/Q Streams in AI-Native 6G Show Chinese title

Title: 用于AI原生6G IQ流的无线基础模型IQFM

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. Show Chinese abstract

Abstract: 基础模型在自然语言处理和计算机视觉领域展现了显著潜力，但在无线通信领域仍处于起步阶段。尽管已有少数尝试探索基于图像的模态，如信道状态信息（CSI）和频率光谱图，但直接作用于原始IQ数据的基础模型仍未得到充分研究。 本文提出了IQFM，这是首个用于无线通信的I/Q信号基础模型。IQFM支持多种任务：调制分类、到达角（AoA）、波束预测和射频指纹识别，无需繁重的预处理或手工特征设计。我们还引入了一种任务感知增强策略，将变换分为核心增强（如循环时间移位）和特定任务增强。 此策略构成了对比自监督学习（SSL）框架内结构化、任务依赖表示学习的基础。采用该策略后，通过SSL在真实多天线IQ数据上预训练的轻量级编码器，在使用每类仅一个标记样本的情况下，分别在调制分类和AoA分类上达到了高达99.67%和65.45%的准确率，相比有监督基线提升了多达7倍和145倍。 该模型还能泛化到分布外的任务；当通过LoRA以每类仅500个样本和最小参数更新的方式适应新任务时，相同的冻结编码器在波束预测上达到94.15%（对比有监督的89.53%），在RML2016a调制分类上达到50.00%（对比有监督的49.30%），在射频指纹识别上达到96.05%（对比有监督的96.64%）。 这些结果表明，基于原始IQ数据的基础模型具有作为高效、可复用编码器的潜力，可用于AI原生6G系统的多任务学习。