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

Title: IQFM 一种用于人工智能原生 6G 中 I/Q 流的无线基础模型

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的潜力，但在无线通信领域仍处于起步阶段。 虽然有一些研究探索了基于图像的模态，如信道状态信息（CSI）和频谱图，但直接在原始IQ数据上运行的基础模型仍然大多未被探索。 本文提出了IQFM，这是第一个用于无线通信的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，而无需复杂的预处理或手工特征。 我们还引入了一种任务感知的增强策略，将变换分为核心增强，如循环时间偏移，以及任务特定的增强。 该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。 使用该策略，通过在空中多天线IQ数据上进行SSL预训练的轻量编码器，在仅使用每类一个标记样本的情况下，分别在调制和AoA分类上达到最高99.67%和65.45%的准确率，比监督基线高出最多7倍和145倍。 该模型还能推广到分布外任务；当使用每类仅500个样本并通过LoRA进行最小参数更新来适应新任务时，相同的冻结编码器在波束预测上达到94.15%（ vs. 89.53% 监督），在RML2016a调制分类上达到50.00%（ vs. 49.30%），在射频指纹识别上达到96.05%（ vs. 96.64%）。 这些结果展示了基于原始IQ的基础模型在AI原生6G系统中的多任务学习中作为高效、可重用编码器的潜力。