提升无线感知中环境识别的跨域适应能力，助力6G智能感知。
IIns-VAE+: A Robust Transfer Learning Framework for Environmental Identification in Wireless Sensing
- 融合IIns-VAE与极小化风险分类器，增强模型泛化性。
- 在三类跨环境迁移场景下，性能显著优于基线模型。
- 适合构建未来6G系统中鲁棒的感知网络，尤其适用于复杂场景。
无线感知中的环境识别对6G集成感知与通信（ISAC）系统实现可靠态势感知至关重要。然而，现有深度学习模型在不同环境间存在分布偏移时泛化能力不足。虽然互实例变分自编码器（IIns-VAE）能学习丰富表征，但其神经分类器仍易受分布变化影响。本文提出IIns-VAE+，一种结合IIns-VAE框架与极小化风险分类器（MRC）的混合模型，以提升迁移学习场景下的适应性。我们在真实世界数据集上评估了该框架在三种迁移学习场景下的表现：从通用房间环境到特定房间、高标签分辨率到低标签分辨率、混合环境到特定环境。实验结果表明，IIns-VAE+显著优于基线模型，验证了其在构建未来6G系统中可适应、鲁棒感知网络方面的关键价值。
原文摘要 · Abstract (English)
Environmental identification in wireless sensing is essential for 6G integrated sensing and communication (ISAC) systems to achieve reliable situational awareness. However, deep learning (DL) models for this task often fail to generalize under domain shift across diverse environments. While the Inter-Instance Variational Auto-encoder (IIns-VAE) learns features of rich representation, its neural classifier remains vulnerable to these distribution changes. In this paper, we propose IIns-VAE+, a hybrid model that combines the IIns-VAE framework with Minimax Risk Classifiers (MRC) to improve adaptability in transfer learning scenarios. We use real-world datasets to evaluate our framework across three transfer learning scenarios, including general to specific room environments, high to low label resolutions, and mixed to specific environments. The experimental results indicate that IIns-VAE+ significantly outperforms baselines, demonstrating its critical value in building adaptable and robust perceptive networks in future 6G systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。