5G NR Carrier Frequency Offset Estimation for Ensemble Learning

Supplying signal data for Carrier Frequency Offset (CFO) estimation within 5G New Radio (NR) systems, developed by Mostafa-Korashy and last updated in December 2022. It targets the application of machine learning techniques, specifically gradient boosting and ensemble learning, to wireless communication synchronization problems.

Use Cases

• Training Gradient Boosting Machines to predict CFO values from 5G signal features
• Evaluating ensemble learning performance on wireless communication synchronization tasks
• Benchmarking frequency offset estimation algorithms against 5G NR standards

Strengths

• Focuses on 5G NR (New Radio) standards
• Tailored for Gradient Boosting Machine (GBM) applications
• Addresses specific carrier frequency offset (CFO) synchronization tasks

Limitations

• Unknown record count and data volume
• Lack of explicit column definitions in metadata
• Unclear whether data is synthetic or hardware-captured

Provenance

Source

Mostafa-Korashy via GitHub

Freshness

Last updated December 2022; no scheduled updates indicated.