MG-HGNN: A Heterogeneous GNN Framework for Indoor Wi-Fi Fingerprint-Based Localization

Abstract--Received signal strength indicator (RSSI) is the primary representation of Wi-Fi fingerprints and serves as a crucial tool for indoor localization. However, existing RSSI-based positioning methods often suffer from reduced accuracy due to environmental complexity and challenges in processing multi-source information. T o address these issues, we propose a novel multi-graph heterogeneous GNN framework (MG-HGNN) to enhance spatial awareness and improve positioning performance. In this framework, two graph construction branches perform node and edge embedding, respectively, to generate informative graphs. Subsequently, a heterogeneous graph neural network is employed for graph representation learning, enabling accurate positioning. The MG-HGNN framework introduces the following key innovations: 1) multi-type task-directed graph construction that combines label estimation and feature encoding for richer graph information; 2) a heterogeneous GNN structure that enhances the performance of conventional GNN models. Evaluations on the UJIIndoorLoc and UTSIndoorLoc public datasets demonstrate that MG-HGNN not only achieves superior performance compared to several state-of-the-art methods, but also provides a novel perspective for enhancing GNN-based localization methods. Ablation studies further confirm the rationality and effectiveness of the proposed framework. Index T erms--Fingerprint-based localization, graph neural network, heterogeneous network, received signal strength indicator (RSSI). NDOOR localization technologies aim to estimate the position of mobile users or devices in indoor environments where satellite-based systems such as GPS are ineffective [1]. Over the past decade, a variety of wireless indoor localization techniques have been developed based on different sensing modalities, including Bluetooth Low Energy (BLE) [2], Ultra Wideband (UWB) [3], Radio Frequency Identification (RFID) [4], magnetic field sensing [5], and Wi-Fi [6], [7]. Among them, Wi-Fi based localization has attracted a lot of attention due to the ubiquity of Wi-Fi infrastructure, low deployment cost, and compatibility with existing mobile devices without requiring additional hardware [1]. This work has been submitted to the IEEE for possible publication. This work is supported by the National Key Research and Development Program of China [Grant No. 2024QY1103], the Shandong Provincial Natural Science Foundation, China [Grant No. ZR2024QF138].(Corresponding Yibu Wang, Zhaoxin Zhang, Ning Li, and Tianzi Zhao are with the School of Computer Science and Technology, Harbin Institute of Technology, China (e-mail: 24b903081@stu.hit.edu.cn; Xinlong Zhao is with the China Mineral Resources Group Big Data Co., Ltd, China (e-mail: xinlong.zhao@qq.com).