A rapid fingerprint positioning method based on deep convolutional neural network for MIMO‑OFDM systems  

作　　者：Chenlin He Xiaojun Wang Jiyu Jiao Yuhua Huang Chengpei Han Yizhuo Zhang Jianping Zhu 

机构地区：[1]National Mobile Communications Research Laboratory,Southeast University,No.2 Southeast University Road,Nanjing,Jiangsu Province 211189,China [2]Advanced Interdisciplinary Studies Research Center,Purple Mountain Laboratories,No.9 Mozhou East Road,Nanjing,Jiangsu Province 211111,China [3]Frontiers Science Center for Mobile Information Communication and Security,Southeast University,No.2 Southeast University Road,Nanjing,Jiangsu Province 211189,China

出　　处：《Urban Lifeline》2024年第1期150-162,共13页城市生命线(英文)

基　　金：supported by the National Key Research and Development Program of China(No.2022YFC3801000);the Fundamental Research Funds for the Central Universities(No.2242022k60001,2242023K40015).

摘　　要：The combination of fingerprint positioning and 5G(the 5th Generation Mobile Communication Technology)offers broader application prospects for indoor positioning technology,but also brings challenges in real-time performance.In this paper,we propose a fingerprint positioning method based on a deep convolutional neural network(DCNN)using a classification approach in a single-base station scenario for massive multiple input multiple outputorthogonal frequency division multiplexing(MIMO-OFDM)systems.We introduce an angle-delay domain fingerprint matrix that simplifies the computation process and increases the location differentiation.The cosine distance is chosen as the fingerprint similarity criterion due to its sensitivity to angular differences.First,the DCNN model is used to determine the sub-area to which the mobile terminal belongs,and then the weighted K-nearest neighbor(WKNN)matching algorithm is used to estimate the position within the sub-area.The positioning performance is simulated in a DeepMIMO indoor environment,showing that the classification DCNN method reduces the positioning time by 77.05%compared to the non-classification method,with only a 1.08%increase in average positioning error.

关 键 词：Fingerprint positioning Rapid positioning Massive multiple input multiple output-orthogonal frequency division multiplexing(MIMO-OFDM) Deep Convolutional Neural Network(DCNN) 

分 类 号：TN9[电子电信—信息与通信工程]