Towards Explainable Indoor Localization: Interpreting Neural Network Learning on Wi-Fi Fingerprints Using Logic Gates

-- Indoor localization using deep learning (DL) has demonstrated strong accuracy in mapping Wi - Fi RSS fingerprints to physical locations; however, most existing DL frameworks function as black - box models, offering limited insight into how predictions are made or how models respond to real - world noise over time. This lack of interpretability hampers our ability to understand the impact of temporal variations -- caused by environmental dynamics -- and to adapt models for long - term reliability. To address thi s, we introduce LogNet, a novel logic gate - based framework designed to interpret and enhance DL - based indoor localization. LogNet enables transparent reasoning by identifying which access points (APs) are most influential for each reference point (RP) and reveals how environmental noise disrupts DL - driven localization decisions . This interpretability allows us to trace and diagnose model failures and adapt DL systems for more stable long - term deployment s . Evaluations across multiple real - world building floo rplans and over two years of temporal variation show that LogNet not only interprets the internal behavior of DL models but also improves performance -- achieving up to 1. 1 to 2 . Indoor localization has become a cornerstone of modern context - aware technologies, enabling applications in robotics, augmented and virtual reality (AR/VR), asset tracking, and emergency response. One of the earliest indoor localization system, " The Active Badge Location System " introduced in 1992 [1], relied on infrared (IR) pulses emitted by wearable badges and captured by stationary IR receivers [ 1 ].