A machine learning algorithm for accurate, energy-efficient outdoor positioning using 5G mmWave beamformed fingerprints.
Beamformed Fingerprint Learning is a machine learning algorithm that enables accurate outdoor positioning using 5G millimeter wave (mmWave) transmissions. It analyzes beamformed fingerprints—signals shaped by reflections from the environment—to estimate a device's location with high precision and low energy consumption, even in non-line-of-sight conditions.
Researchers and engineers working on 5G positioning systems, wireless communication, and low-power machine learning applications for embedded or mobile devices.
It offers a highly energy-efficient and accurate alternative to GPS by leveraging existing 5G mmWave infrastructure and deep learning, requiring only a single base station and performing well in challenging NLOS urban environments.
🎯 ML-based positioning method from mmWave transmissions - with high accuracy and energy efficiency
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Achieves average errors as low as 1.78 meters with tracking, even in non-line-of-sight conditions, making it reliable in urban environments with obstacles.
Consumes less than 10 mJ per position estimate, which is 47x to 85x more efficient than low-power A-GPS, ideal for battery-constrained IoT devices.
Requires only one base station with a pre-established beamforming codebook, reducing infrastructure costs and complexity compared to multi-station systems.
Includes tools to test model performance and power consumption on low-power embedded systems like Nvidia Jetson TX2, demonstrating practical deployability.
Requires specific hardware (Nvidia GPU), software (TensorFlow 2.4, CUDA 11.0), and dataset preparation using proprietary ray-tracing simulators like Wireless InSite, which can be a significant barrier.
The dataset is based on a specific 3D map of New York and generated with simulation tools; adapting to other locations requires additional simulation work and may not be straightforward.
The README admits that Jetson evaluation tools might not work with the current code base due to lost access to hardware, indicating potential upkeep and version compatibility issues.
mmWave-localization-learning is an open-source alternative to the following products:
Assisted GPS is a system that uses network data to improve the startup performance of GPS positioning in mobile devices.
GPS (Global Positioning System) is a satellite-based navigation system that provides location and time information anywhere on Earth where there is an unobstructed line of sight to GPS satellites.