Existing fingerprint-based localization methods perform well in a specific region. However, transferring the model to a new region or adapting to differences in regional environments poses challenges. Additionally, the substantial training cost of the model, including long training time and the inability to reuse data across different regions, further complicates the implementation process. To address these issues, we propose cross region fusion and fast adaptation (CRFA) framework, a novel approach for fingerprint localization in cell-free massive multiple-input multiple-output systems. We begin by extracting angle domain channel power as fingerprint. Further more, we employ access point selection, cross-region fusion and network localization network to enhance localization accuracy and address cross-regional fingerprint disparities. Through the training process of model-agnostic meta-learning, CRFA acquires meta-parameters that facilitate its deployment to any region through a fine-tuning process. Leveraging cross region fusion and meta-learning, the proposed model achieves higher localization accuracy, fast deployment, and adaptability to various environments. Experimental validation using Wireless Insite software shows that the proposed CRFA method performs better in complex environments compared to traditional methods when rapidly deploying models to indoor, urban and suburban region.

中文翻译：

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无单元 Massive MIMO 系统中基于指纹的多场景定位的跨区域融合和快速适配


现有的基于指纹的定位方法在特定区域表现良好。然而，将模型转移到新区域或适应区域环境的差异会带来挑战。此外，该模型的大量训练成本，包括较长的训练时间和无法跨不同区域重用数据，使实施过程进一步复杂化。为了解决这些问题，我们提出了跨区域融合和快速适应 （CRFA） 框架，这是一种在无细胞大规模多输入多输出系统中进行指纹定位的新方法。我们首先提取角度域通道功率作为指纹。此外，我们采用接入点选择、跨区域融合和网络定位网络来提高定位准确性并解决跨区域指纹差异。通过与模型无关的元学习的训练过程，CRFA 获取元参数，这些参数有助于通过微调过程将其部署到任何区域。利用跨区域融合和元学习，所提出的模型实现了更高的定位精度、快速部署和对各种环境的适应性。使用 Wireless Insite 软件的实验验证表明，在将模型快速部署到室内、城市和郊区时，与传统方法相比，所提出的 CRFA 方法在复杂环境中表现更好。