Machine Learning-Based Fingerprinting Positioning in Massive MIMO Networks: Analysis on the Impact of Small Training Sample Size to the Positioning Performance

It is well known that the bigger the training dataset, the higher the performance of deep learning algorithms. But gathering/collecting huge real measured CSI samples to be used as fingerprints to deep learning-based positioning models is a very challenging task both in terms of time and resources. Training deep learning models using very big training dataset is also very costly because it requires access to very powerful computing devices which are very expensive and thus not affordable to everyone. This might be one of many reasons that could hinder research and development of powerful deep learning algorithms to solve different societal problems. This necessitates the need to engage more in research to build high-performing deep learning models capable of giving out satisfactory performance using limited computing resources and small training dataset sizes. In this paper, we analyzed the impact of small training sample size to the positioning performance of CSI-based deep learning fingerprinting positioning models. Results show that with better design of deep learning models, it is possible to achieve high positioning performance using relatively small training sample sizes.