QSCL-EWIL: Quantum Stochastic Contrast Learning for Enhanced WiFi-Based Indoor Localization

TL;DR

This work tackles WiFi indoor localization under RSSI variability and limited labels by introducing Quantum Stochastic Contrastive Learning (QSCL). QSCL combines quantum-driven strong augmentations with classical weak augmentations, a Spatio-Temporal Adaptive Attention (STAA) encoder, and a bidirectional contrastive loss to learn robust representations from unlabeled data. Theoretical contributions include four theorems and a lemma establishing probabilistic augmentation, noise resilience, diversity, and semantic preservation under quantum perturbations. Empirical validation on UJIIndoorLoc and UTSIndoorLoc demonstrates improved localization accuracy, floor/building detection, and generalization, along with resilience to quantum noise, suggesting practical benefits for scalable, hardware-aware indoor localization systems.

Abstract

WiFi-based indoor localization is essential for asset tracking, healthcare monitoring, and smart buildings. However, existing systems face challenges such as data variability, environmental noise, and difficulty detecting floor and building levels, compounded by limited labeled data and high received signal strength (RSS) collection costs. This paper introduces quantum stochastic contrast learning (QSCL), a novel framework grounded in rigorous theoretical foundations. We present four theorems and one lemma that establish the probabilistic augmentation, diversity enhancement, relationship preservation, and resilience of QSCL under quantum noise, supported by formal proofs. Leveraging these foundations, QSCL utilizes quantum computing (QC) to generate strong data augmentations with stochastic perturbations, enhancing data diversity, while classical weak augmentations provide subtle variations for robust feature learning. We propose a spatial temporal adaptive attention (STAA) encoder that integrates convolutional layers with adaptive attention mechanisms to capture spatial and temporal dependencies in sequential data. Furthermore, a bidirectional contrastive loss function is introduced to capture forward and reverse relationships between augmented views, ensuring robust representations. Comprehensive evaluations on the UJIIndoorLoc and UTSIndoorLoc datasets validate QSCL, demonstrating superior performance with reduced labeled data and resilience to quantum noise such as bit-flip, dephasing, and measurement noise. The proposed framework significantly improves localization accuracy, floor and building detection, and generalizability in challenging indoor environments.

Figures (7)

• Figure 1: Proposed quantum stochastic contrastive learning framework for WiFi-based indoor localization.
• Figure 2: Proposed quantum-based augmentation circuit.
• Figure 3: Proposed spatiotemporal adaptive attention encoder.
• Figure 4: Propose framework implementation result stepwise.
• Figure 5: 3D visualization of the resultant accuracy for WiFi-based indoor localization with floor and building detection on a) UJIIndoorLoc dataset and b) UTSIndoorLoc dataset.

Theorems & Definitions (10)

• Theorem 1
• proof
• Theorem 2
• proof
• Theorem 3
• proof
• Theorem 4
• proof
• Lemma 1
• proof