Contrastive Self-Supervised Learning-Based Wireless Fingerprint Localization

Recently, deep learning based fingerprint localization has attracted significant interest due to its simplicity in implementation and effectiveness in complex multipath environments, especially for the Internet of Things (IoT) devices in multiple-input multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) system. However, the huge amount of training data collection has become a challenge, which increases the labor burden of fingerprint localization heavily and hinders its large-scale implementation. In this paper, we propose a novel fingerprint localization system, termed as SiamResNet, which can be trained only on the radio map by contrastive self-supervised learning without the need for any other additional data. To be more specific, we first model the fingerprint localization problem as a dictionary look-up task. Subsequently, a channel fingerprint capturing the multipath angle and delay of wireless propagation is introduced, which exhibits excellent uniqueness, stability, and distinguishability. Meanwhile, we propose the corresponding data augmentation strategy to ensure data diversity when generating the training data from the radio map. Thus, the cost of data collection for training can be significantly reduced. Lastly, the Siamese architecture based SiamResNet is applied for location estimation, which can comprehensively extract the features of fingerprints and accurately compare the similarity of any fingerprint to the radio map in the representation space. The performance of the proposed localization method is validated through extensive simulations with a ray-tracing channel model, which demonstrates promising localization accuracy for our SiamResNet with reduced training costs.