ReddRadar
Agent skill
beam-tracking-ml
Design and refactor beam tracking ML/RL pipelines (CSI teacher vs RSRP student), enforce shape contracts, and produce inference-safe models.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/development/beam-tracking-ml
SKILL.md
Beam Tracking ML Skill
Use this Skill when:
- translating the RL架構 diagram into code
- refactoring
sionna_beam_tracking_v2.pyideas 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
- Train teacher on CSI dataset (offline).
- Run teacher over same trajectories, log action distributions.
- Train student to match teacher (KL divergence).
- Optionally fine-tune student with small online data.
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