Designing low-power hardware for high-precision cellular localization using attention-based machine learning algorithms
This thesis investigates the feasibility and performance of implementing attention-based machine learning models for high-precision cellular localization on low-power hardware. While such models, particularly those using the self-attention mechanism, have demonstrated impressive accuracy in extracting spatial information from wireless signals, they typically rely on GPU acceleration, limiting thei
