Deep learning with synthetic data for wireless NLOS positioning with a single base station

IF 4.4 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Ad Hoc Networks Pub Date : 2024-10-31 DOI:10.1016/j.adhoc.2024.103696
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Abstract

Traditional wireless positioning methods exhibit limitations in the face of signal distortions prevalent in non-line-of-sight (NLOS) conditions, especially in the case of a single base station (BS). Moreover, the adoption of deep learning (DL) methodologies has lagged behind, largely due to the challenges associated with generating real-world datasets. In this paper, we present a comprehensive approach leveraging DL over large-scale synthetic wireless datasets (the recent WAIR-D in this case, which was co-produced by Huawei) to overcome such challenges and address the case of single-BS NLOS positioning. The aim of the paper is to practically explore the extent to which synthetic wireless datasets can help to achieve the positioning objectives. Towards this direction, we develop a map-based representation of a radio link, demonstrating its synergistic effect with feature-based representations in MLPs. Furthermore, we introduce a UNet-based neural model which incorporates input maps and radio link representations and generates as output a heatmap of potential device positions. This model achieves an 11.3-meter RMSE and 76.5% prediction accuracy on NLOS examples (1.5-meter, 99.9% for LOS) assuming perfect information, surpassing the MLP baseline by 47%. Finally, we provide further insights into the model’s ability to predict top device positions, the characteristics of predicted heatmaps as indicators of confidence, and the crucial role of map availability and radio path angles in model performance, thus revealing an unconventional perspective on incorrect predictions.
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利用合成数据进行深度学习,实现单基站无线 NLOS 定位
面对非视距(NLOS)条件下普遍存在的信号失真,传统的无线定位方法表现出局限性,尤其是在单基站(BS)的情况下。此外,深度学习(DL)方法的采用一直滞后,这主要是由于生成真实世界数据集所面临的挑战。在本文中,我们提出了一种综合方法,利用大规模合成无线数据集上的深度学习(本例中为最近的 WAIR-D,由华为联合制作)来克服这些挑战,并解决单基站 NLOS 定位的情况。本文旨在实际探索合成无线数据集可在多大程度上帮助实现定位目标。为此,我们开发了一种基于地图的无线链路表示法,展示了它与 MLP 中基于特征的表示法之间的协同效应。此外,我们还引入了基于 UNet 的神经模型,该模型结合了输入地图和无线电链路表示法,并生成潜在设备位置的热图作为输出。假设信息完美,该模型在 NLOS 示例(1.5 米,LOS 为 99.9%)上实现了 11.3 米的均方根误差和 76.5% 的预测准确率,比 MLP 基线高出 47%。最后,我们对模型预测顶部设备位置的能力、作为置信度指标的预测热图的特征以及地图可用性和无线电路径角度在模型性能中的关键作用提供了进一步的见解,从而揭示了错误预测的非传统视角。
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来源期刊
Ad Hoc Networks
Ad Hoc Networks 工程技术-电信学
CiteScore
10.20
自引率
4.20%
发文量
131
审稿时长
4.8 months
期刊介绍: The Ad Hoc Networks is an international and archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in ad hoc and sensor networking areas. The Ad Hoc Networks considers original, high quality and unpublished contributions addressing all aspects of ad hoc and sensor networks. Specific areas of interest include, but are not limited to: Mobile and Wireless Ad Hoc Networks Sensor Networks Wireless Local and Personal Area Networks Home Networks Ad Hoc Networks of Autonomous Intelligent Systems Novel Architectures for Ad Hoc and Sensor Networks Self-organizing Network Architectures and Protocols Transport Layer Protocols Routing protocols (unicast, multicast, geocast, etc.) Media Access Control Techniques Error Control Schemes Power-Aware, Low-Power and Energy-Efficient Designs Synchronization and Scheduling Issues Mobility Management Mobility-Tolerant Communication Protocols Location Tracking and Location-based Services Resource and Information Management Security and Fault-Tolerance Issues Hardware and Software Platforms, Systems, and Testbeds Experimental and Prototype Results Quality-of-Service Issues Cross-Layer Interactions Scalability Issues Performance Analysis and Simulation of Protocols.
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