Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication(ISAC)systems,enabling precise navigation in environments where Global Positioning System(GPS)signals are unavailable...Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication(ISAC)systems,enabling precise navigation in environments where Global Positioning System(GPS)signals are unavailable.Existing methods,such as map-based navigation or site-specific fingerprinting,often require intensive data collection and lack generalization capability across different buildings,thereby limiting scalability.This study proposes a cross-site,map-free indoor localization framework that uses low-frequency sub-1 GHz radio signals and a Transformer-based neural network for robust positioning without prior environmental knowledge.The Transformer’s self-attention mechanisms allow it to capture spatial correlations among anchor nodes,facilitating accurate localization in unseen environments.Evaluation across two validation sites demonstrates the framework’s effectiveness.In crosssite testing(Site-A),the Transformer achieved a mean localization error of 9.44 m,outperforming the Deep Neural Network(DNN)(10.76 m)and Convolutional Neural Network(CNN)(12.02 m)baselines.In a real-time deployment(Site-B)spanning three floors,the Transformer maintained an overall mean error of 9.81 m,compared with 13.45 m for DNN,12.88 m for CNN,and 53.08 m for conventional trilateration.For vertical positioning,the Transformer delivered a mean error of 4.52 m,exceeding the performance of DNN(4.59 m),CNN(4.87 m),and trilateration(>45 m).The results confirm that the Transformer-based framework generalizes across heterogeneous indoor environments without requiring site-specific calibration,providing stable,sub-12 m horizontal accuracy and reliable vertical estimation.This capability makes the framework suitable for real-time applications in smart buildings,emergency response,and autonomous systems.By utilizing multipath reflections as an informative structure rather than treating them as noise,this work advances artificial intelligence(AI)-native indoor localization as a scalable and efficient component of future 6G ISAC networks.展开更多
在面向卫星物联网的免授权随机接入(Grant-free Random Access,GFRA)系统中,受大规模连接和设备随机激活的影响,前导碰撞成为用户接入性能提升的主要制约因素。鉴于此,借助正交与非正交序列在前导检测和冲突抑制方面的各自优势,提出一...在面向卫星物联网的免授权随机接入(Grant-free Random Access,GFRA)系统中,受大规模连接和设备随机激活的影响,前导碰撞成为用户接入性能提升的主要制约因素。鉴于此,借助正交与非正交序列在前导检测和冲突抑制方面的各自优势,提出一种基于混合ZC(Zadoff-Chu)序列的大容量前导设计和检测方法。该方法利用正交ZC序列与其循环移位映射的不同根ZC序列级联来构建前导序列,并采用一种基于假设检验的两阶段干扰消除活跃用户检测算法,以确保大规模接入场景下的高精度用户识别。此外,对所提前导结构进行扩展,将相位旋转因子与多段非正交序列相结合,在不增加峰均比的前提下进一步扩大前导集容量。所提方法较现有复合和正交前导方法具有显著改善的多用户识别性能,在相同活跃用户下,成功检测概率最大提升约30.3%。展开更多
基金funded by the Ministry of Science and Technology,Taiwan,under grant number MOST 114-2224-E-A49-002was received by En-Cheng Liou.
文摘Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication(ISAC)systems,enabling precise navigation in environments where Global Positioning System(GPS)signals are unavailable.Existing methods,such as map-based navigation or site-specific fingerprinting,often require intensive data collection and lack generalization capability across different buildings,thereby limiting scalability.This study proposes a cross-site,map-free indoor localization framework that uses low-frequency sub-1 GHz radio signals and a Transformer-based neural network for robust positioning without prior environmental knowledge.The Transformer’s self-attention mechanisms allow it to capture spatial correlations among anchor nodes,facilitating accurate localization in unseen environments.Evaluation across two validation sites demonstrates the framework’s effectiveness.In crosssite testing(Site-A),the Transformer achieved a mean localization error of 9.44 m,outperforming the Deep Neural Network(DNN)(10.76 m)and Convolutional Neural Network(CNN)(12.02 m)baselines.In a real-time deployment(Site-B)spanning three floors,the Transformer maintained an overall mean error of 9.81 m,compared with 13.45 m for DNN,12.88 m for CNN,and 53.08 m for conventional trilateration.For vertical positioning,the Transformer delivered a mean error of 4.52 m,exceeding the performance of DNN(4.59 m),CNN(4.87 m),and trilateration(>45 m).The results confirm that the Transformer-based framework generalizes across heterogeneous indoor environments without requiring site-specific calibration,providing stable,sub-12 m horizontal accuracy and reliable vertical estimation.This capability makes the framework suitable for real-time applications in smart buildings,emergency response,and autonomous systems.By utilizing multipath reflections as an informative structure rather than treating them as noise,this work advances artificial intelligence(AI)-native indoor localization as a scalable and efficient component of future 6G ISAC networks.
文摘在面向卫星物联网的免授权随机接入(Grant-free Random Access,GFRA)系统中,受大规模连接和设备随机激活的影响,前导碰撞成为用户接入性能提升的主要制约因素。鉴于此,借助正交与非正交序列在前导检测和冲突抑制方面的各自优势,提出一种基于混合ZC(Zadoff-Chu)序列的大容量前导设计和检测方法。该方法利用正交ZC序列与其循环移位映射的不同根ZC序列级联来构建前导序列,并采用一种基于假设检验的两阶段干扰消除活跃用户检测算法,以确保大规模接入场景下的高精度用户识别。此外,对所提前导结构进行扩展,将相位旋转因子与多段非正交序列相结合,在不增加峰均比的前提下进一步扩大前导集容量。所提方法较现有复合和正交前导方法具有显著改善的多用户识别性能,在相同活跃用户下,成功检测概率最大提升约30.3%。
基金Supported by the National Basic Research Program of China under Grant No.2002CB312101(国家重点基础研究发展计划(973))the National Natural Science Foundation of China under Grant Nos.60373036,60333010(国家自然科学基金)+1 种基金the Doctoral Program of Higher Education(Specialized Research Fund)of China under Grant No.20050335069(国家教育部高等学校博士学科点专项科研基金)the Natural Science Foundation of Zhejiang Province of China under Grant No.R106449(浙江省自然科学基金)
