Localization-based services aim to provide almost zero latency and more agile networks. Conventional localization methods are precise, but at the same time, they bring in high energy consumption to ...the system. Moreover, they are location-dependent. With the recent rise of Artificial Intelligence (AI)/machine learning (ML), we can utilize these models to overcome the aforementioned challenges. More specifically, AI/ML methods take past data to make predictions of user locations. In this paper, we first investigate three different datasets to cover different scenarios in wireless communication systems. Then, we revisit two classic regression-based ML models, these models are low in complexity, which makes them perfect to deploy in the edge nodes. Finally, we provide some engineering insights with a focus on dataset selection and feature grouping for localization tasks.
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