Beam Tracking ML Skill

Use this Skill when:

• translating the RL架構 diagram into code
• refactoring sionna_beam_tracking_v2.py ideas into modular components
• designing observation/action schemas

Guardrails

• Always define and test shapes (B,N_BEAMS) etc.
• Keep student (online) policy lightweight and deterministic.
• Treat CSI-heavy path as offline only unless we explicitly design compression.

Where to put code

• Models: beam_tracking/model/
• Training scripts: scripts/ (do not bloat runtime xApp)
• Interfaces: beam_tracking/schemas.py

Suggested distillation workflow

1. Train teacher on CSI dataset (offline).
2. Run teacher over same trajectories, log action distributions.
3. Train student to match teacher (KL divergence).
4. Optionally fine-tune student with small online data.