Page 371 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

frameworks TensorFlow for RNNs, Stable-Baselines3 for RL policies, and OpenMMLab [7]
for data augmentation—to train and deploy models on GPU clusters. All code, simulation
configurations, and data schemas will be made publicly available on GitHub under an MIT
license. 4.3 - 5G

Standards Development: Close collaboration with ITU and other standardization bodies will be
undertaken to establish guidelines for AI-driven network optimization under adverse weather.
This will include developing industry benchmarks that ensure seamless integration with existing
infrastructures and promote the adoption of resilient communication networks.

3 Use Case Requirements

• REQ-01: It is critical that real-time weather ingestion is enabled via continuous high-
resolution meteorological feeds (e.g., rain rate, humidity, visibility) through APIs or IoT
streams.
• REQ-02: It is expected that standardized propagation models are incorporated within
the inference pipeline to provide baseline estimates of signal impairment.
• REQ-03: It is critical that the system captures raw Channel State Information (CSI) including
amplitude and phase, along with per-beam RSSI/SINR data from mmWave transceivers.
• REQ-04: It is critical that GPU-accelerated edge or cloud compute infrastructure is used
to achieve sub-100 ms latency for RNN or reinforcement learning-based inference.
• REQ-05: It is of added value that data is structured using standardized JSON or CSV
formats with consistent field naming, SI units, and per-epoch manifest records to enhance
interoperability.

4 Sequence Diagram

5 References

[1] N. Patel, “Weather Type Classification,” Kaggle dataset, 2023. [Online]. Available: https://
www .kaggle .com/ datasets/ nikhil7280/ weather -type -classification/ data

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