Blind-Distance Estimation with Adaptive Privacy Preservation in Non-Line-of-Sight THz-Based GPS-Denied Networks
Kulaea Taueveeve Pauu, Jun Wu · 2025
The rapid advancement of sixth-generation (6G) wireless networks has accelerated the adoption of terahertz (THz) communication due to its ultra-high data rates and abundant spectrum. However, distance estimation in non-line-of-sight (NLoS) THz environments remains challenging due to severe path loss, molecular absorption, and multipath effects. Moreover, precise localization raises privacy concerns, exposing nodes to adversarial tracking. To address these challenges, we propose a novel blind-distance estimation with adaptive privacy-preservation scheme for non-line-of-sight THz-based GPS-denied networks. We propose a novel blind-distance estimation method using a single THz frequency without requiring prior system knowledge. Second, we formulate a distance estimation model that integrates molecular absorption effects and multipath propagation to improve distance estimation accuracy. Third, we design a novel adaptive privacy-preserving perturbation mechanism that dynamically adjusts noise based on multipath propagation and obstacle density, ensuring feasible perturbations while balancing privacy and localization accuracy. Simulation results demonstrate that our scheme effectively balances localization accuracy and privacy preservation, adapting to both ideal and NLoS conditions in structured (urban) and unstructured (disaster) environments.