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This work presents a date-driven user localization framework for single-sitemassive Multiple-Input-Multiple-Output (MIMO) systems. The framework is trainedon a geo-tagged Channel State Information (CSI) dataset. Unlike thestate-of-the-art Convolutional Neural Network (CNN) models, which require largetraining datasets to perform well, our method is specifically designed tooperate with small-scale training datasets. This makes our approach morepractical for real-world scenarios, where collecting a large amount of data canbe challenging. Our proposed FC-AE-GPR framework combines two components: aFully-Connected Auto-Encoder (FC-AE) and a Gaussian Process Regression (GPR)model. Our results show that the GPR model outperforms the CNN model whenpresented with small training datasets. However, the training complexity of GPRmodels can become an issue when the input sample size is large. To addressthis, we propose using the FC-AE to reduce the sample size by encoding the CSIbefore training the GPR model. Although the FC-AE model may require a largertraining dataset initially, we demonstrate that the FC-AE is scenarioindependent. This means that it can be utilized in new and unseen scenarioswithout prior retraining. Therefore, adapting the FC-AE-GPR model to a newscenario requires only retraining the GPR model with a small training dataset.

CSI-Based Data-driven Localization Frameworking using Small-scale Training Datasets in Single-site MIMO Systems