DeepSync: A Learning Framework for Pervasive Localization using Code Synchronization on Compressed Cellular Spectrum

TL;DR

DeepSync is a deep learning framework that enables highly accurate, low-power cellular spectrum-based localization by formulating timing estimation as a template matching problem with innovative neural architectures.

Contribution

It introduces a novel deep learning architecture combining temporal CNNs and cross-attention for precise sub-sample timing estimation in compressed spectrum sensing.

Findings

Achieves median localization accuracy of 2.128 meters.

Operates effectively at SNR levels below -10dB.

Provides 10x improvement over existing compressed spectrum methods.

Abstract

Pervasive localization is essential for continuous tracking applications, yet existing solutions face challenges in balancing power consumption and accuracy. GPS, while precise, is impractical for continuous tracking of micro-assets due to high power requirements. Recent advances in non-linear compressed spectrum sensing offer low-power alternatives, but existing implementations achieve only coarse positioning through Received Signal Strength Indicator (RSSI) measurements. We present DeepSync, a deep learning framework that enables precise localization using compressed cellular spectrum. Our key technical insight lies in formulating sub-sample timing estimation as a template matching problem, solved through a novel architecture combining temporal CNN encoders for multi-frame processing with cross-attention mechanisms. The system processes non-linear inter-modulated spectrum through hierarchical feature extraction, achieving robust performance at SNR levels below -10dB -- a regime where conventional timing estimation fails. By integrating real cellular infrastructure data with physics-based ray-tracing simulations, DeepSync achieves 2.128-meter median accuracy while consuming significantly less power than conventional systems. Real-world evaluations demonstrate 10x improvement over existing compressed spectrum approaches, establishing a new paradigm for ultra-low-power localization.