Self-Supervised Learning for User Localization

arXiv:2404.15370v1 Announce Type: cross
Abstract: Machine learning techniques have shown remarkable accuracy in localization tasks, but their dependency on vast amounts of labeled data, particularly Channel State Information (CSI) and corresponding coordinates, remains a bottleneck. Self-supervised learning techniques alleviate the need for labeled data, a potential that remains largely untapped and underexplored in existing research. Addressing this gap, we propose a pioneering approach that leverages self-supervised pretraining on unlabeled data to boost the performance of supervised learning for user localization …

abstract accuracy arxiv cs.ai cs.lg cs.ni data eess.sp information localization machine machine learning machine learning techniques research self-supervised learning state supervised learning tasks type vast