LunaNet Track Summary
What This Track Is
LunaNet focuses on LSIS-AFS signal-processing implementation: spreading-code generation, FEC and frame assembly, I/Q signal synthesis, decode recovery, and parsed navigation outputs.
Difficulty Level
- Overall difficulty: Very High
- Why:
- Multi-domain complexity (DSP, coding theory, framing, decoding, parser correctness)
- Tight coupling between constants/tables and algorithm behavior
- Failures can propagate across encode/decode chain and be expensive to debug
Primary Requirements
- Correct Gold/Weil code generation and PRN mapping
- Correct BCH/LDPC/CRC/interleaver behavior with exact frame layout
- Standards-aligned I/Q generation at required rates and formats
- Reliable synchronization and decode, including noisy-condition robustness
- Accurate parser extraction for WN/ITOW/TOI and timing reconstruction
Recommended Stack
Primary recommendation:
- Hybrid stack:
- Rust or C++ for performance-critical DSP/codec core
- Python for analysis harnesses, vector validation, and tooling glue
Why this stack:
- Core path needs deterministic performance and strong control of memory/layout
- Validation and experiment workflows benefit from Python’s data tooling
Supporting tooling:
- Immutable table ingestion with schema/hash validation
- BER/SNR sweep automation and replay fixtures
- Artifact packaging for cross-team interoperability runs
Team Lead Recommendation
Primary lead profile:
- DSP/communications + algorithm-heavy team should lead this track
Best lead roles:
- Engineer A (normative algorithm/spec interpretation)
- Engineer B (decode quality and performance metrics)
- Engineer C (interop operations and exchange governance)
Developer focus:
- Developer A: code generation + encoding
- Developer B: frame + signal synthesis
- Developer C: decoding + parser + automation
Useful Execution Advice
- Treat spec tables as controlled inputs; validate schema and hashes in CI.
- Build round-trip tests early (encode -> signal -> decode -> parse) and run continuously.
- Separate algorithm kernels from orchestration to reduce debugging blast radius.
- Timebox tuning work; preserve reproducibility when optimizing BER/performance.
Risk Profile
Top risks:
- Table drift and constant misuse
- Framing off-by-one errors
- Decoder instability at realistic noise levels
- Late interoperability divergence
Mitigation posture:
- Golden-vector gating, fixed SNR test suites, and mandatory weekly partner exchange.
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