Hrant Khachatrian , Rafayel Mkrtchyan , Theofanis P. Raptis
{"title":"利用合成数据进行深度学习,实现单基站无线 NLOS 定位","authors":"Hrant Khachatrian , Rafayel Mkrtchyan , Theofanis P. Raptis","doi":"10.1016/j.adhoc.2024.103696","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep learning with synthetic data for wireless NLOS positioning with a single base station\",\"authors\":\"Hrant Khachatrian , Rafayel Mkrtchyan , Theofanis P. Raptis\",\"doi\":\"10.1016/j.adhoc.2024.103696\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":55555,\"journal\":{\"name\":\"Ad Hoc Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ad Hoc Networks\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S157087052400307X\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ad Hoc Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S157087052400307X","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Deep learning with synthetic data for wireless NLOS positioning with a single base station
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.
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.