How to Generate AI RAN Training Datasets

AI RAN Simulation Toolset
+ AI RAN Simulation Toolset

Create and Curate AI RAN Training Data

Generating an artificial intelligence (AI) training dataset requires representative radio access network (RAN) scenarios, source network data, and a controlled pipeline that converts raw or synthetic data into curated datasets. A network digital twin can generate realistic synthetic data across link, system, and network levels, while emulated testbeds or live network captures can supplement the dataset with additional operating conditions, protocol behavior, and subscriber mobility.

Dataset preparation imports source data, cleans and validates records, applies labeling or annotation, standardizes or normalizes fields, and augments seed data to increase volume and diversity. Engineers can inspect distributions and anomalies, store versioned datasets with traceability, and use the resulting training data in model-development workflows before validating candidate models against network key performance indicators in a digital twin or lab environment.

Synthetic AI RAN Dataset Generation Solution

Building a synthetic radio Access Network (RAN) training dataset requires a defined network scenario, source data, and a repeatable data-curation workflow. The S6221A AI RAN Simulation Toolset combines data from network digital twins, emulated testbeds, and live networks with an AI development platform for data management. The workflow imports source data, performs cleaning and preprocessing, applies labeling, normalization, augmentation, diversity, and scaling operations, and produces curated datasets for artificial intelligence (AI) model training and testing. Dataset and model versions can be stored with metadata and traceability, while benchmarking functions compare candidate models and network key performance indicators against baseline results.

See Block Diagram of Synthetic AI RAN Dataset Generation Solution

Block Diagram of Synthetic AI RAN Dataset Generation Solution

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