# Beam Tracking Ml

> Design and refactor beam tracking ML/RL pipelines (CSI teacher vs RSRP student), enforce shape contracts, and produce inference-safe models.

- Skill: `majiayu000/beam-tracking-ml` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/beam-tracking-ml`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/beam-tracking-ml/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/beam-tracking-ml

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# 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.