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Diffractive RSS Based Multinetwork Aided 3D Positioning for Distributed Massive MIMO Systems
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AI
DOI：10.1109/TCOMM.2022.3173003.png)
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摘要
En 中文
Massive multiple-input multiple-output (M-MIMO) aided positioning technologies have been recently recognized as promising solutions to fulfil the high accuracy requirement of indoor localization systems. In this paper, exploiting a distributed M-MIMO framework, we propose to employ a deep belief network (DBN) to analyze the received signal strengths (RSS) generated by a diffraction model, where the impact from interfering persons on the targeted user equipment (UE) is considered. Next, the preliminary DBN estimates are forwarded to a long-short term memory network (LSTMN), where the trajectory information of the targeted UE can be extracted based on much less historical trajectory information than existing solutions. Then, the three-dimension (3D) coordinates of the UE's positions can be estimated with a back propagation neural network (BPNN) which combines the outputs of DBN and LSTMN. Finally, extensive simulation results are provided to demonstrate the effectiveness of the proposed BPNN scheme.
Keyword:
Diffraction
Trajectory
Wireless communication
Training
Three-dimensional displays
Symbols
Data mining
Distributive massive multiple-input multiple-output (DM-MIMO)
3D positioning
deep learning
deep belief network (DBN)
long-short term memory network (LSTMN)
back propagation neural network (BPNN)
期刊
IF:
8.3
论文数:
1.2W
被引数:
3.6W
机构
引用论文
分享收藏An Indoor Positioning Method Based on CSI by Using Features Optimization Mechanism With LSTM.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
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