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A CSI-Based Data-Driven Localization Framework Using Small-Scale Training Datasets in Single-Site MIMO Systems
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AI
DOI：10.1109/TWC.2024.3440275.png)
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摘要
En 中文
This paper presents a new method for user localization in single-site massive Multiple-Input-Multiple-Output (MIMO) systems, which circumvents the need for large labeled datasets typically required for training data-driven models. Instead, the proposed model utilizes a limited set of geo-tagged Channel State Information (CSI) samples for training. The approach combines a Fully-Connected Auto-Encoder (FC-AE) with a Gaussian Process Regression (GPR) model. The GPR model is efficient, as it requires only a minimal amount of labeled data for training, although it presents challenges in computational complexity. To address this complexity, the FC-AE is introduced, which encodes the Angle-Delay Profile (ADP) transformation of the CSI data. The training dataset for the FC-AE is crafted by employing data augmentation techniques on a small collection of unlabeled data. The simulation results demonstrate that FC-AE is scenario-independent and adaptable to new scenarios with similar ADP characteristics. Additionally, our FC-AE-GPR model surpasses the performance of the Convolutional Neural Network model and the non-parametric grid search method when provided with limited labeled data, applicable in both indoor and outdoor settings.
Keyword:
Training
Location awareness
Computational modeling
Fingerprint recognition
Data models
Wireless communication
Wireless sensor networks
Single-site MIMO
localization
Gaussian processes regression
autoencoders
data augmentation
期刊
IF:
10.7
论文数:
1.3W
被引数:
5.3W
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