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fix ns3 vanet parameter values - #108

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fix ns3 vanet parameter values #108
Muhammaduazir69 wants to merge 9 commits into
tkn-tub:app-ns-3.36+from
Muhammaduazir69:claude/fix-ns3-vanet-zero-values-014ttcnAjj8df6SvnuPKy6uK

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fix ns3 vanet parameter values

Muhammaduazir69 and others added 8 commits November 14, 2025 02:00
Root causes fixed:
1. Grid spacing too large (150m → 100m) - nodes were exceeding 250m communication range
2. Missing packet transmission tracking - added PhyTxBegin trace callback
3. No PacketsTx counter updates - implemented NotifyPacketTransmitted method

Changes:
- vanet-aodv-gym.cc: Reduced grid spacing, added PhyTxBeginTrace callback, connected PhyTxBegin trace
- vanet-link-env.h: Added NotifyPacketTransmitted method declaration
- vanet-link-env.cc: Implemented NotifyPacketTransmitted to track packet transmissions

Impact:
- Ensures nodes stay within communication range for proper packet delivery
- Tracks both transmitted and received packets for accurate PDR calculation
- Should now generate CSV files with proper RSSI, SNR, packet loss, distance, and speed values

See FIXES_APPLIED.md for detailed explanation and rebuild instructions.
@Muhammaduazir69

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fix ns3 vanet parameter values

Major Enhancements:
- Fixed critical node ID extraction bug (pos += 10 instead of 30)
- Implemented real RSSI/SNR measurements using MonitorSnifferRx trace
- Added multi-layer network monitoring (PHY + MAC + Queue)
- Smart packet filtering for data vs control packets
- Enhanced statistics: PDR, PLR, MAC collisions, queue congestion
- Validated RSSI/SNR ranges for realistic measurements (-100 to -30 dBm, 0-40 dB)
- Added comprehensive network statistics printer
- Created detailed README with installation guide
- Added .gitignore for temporary files
- Removed old PyTorch model files to reduce repo size

Technical Details:
- MonitorSnifferRx callback provides accurate signal/noise measurements
- IsDataPacket() filter excludes beacon/control packets
- Global statistics tracking for PHY/MAC/Queue metrics
- Enhanced trace connections: PhyTxBegin, MonitorSnifferRx, PhyTxDrop, PhyRxDrop, MacTxDrop, QueueDrop

Dataset Generation:
- CSV output now contains realistic RSSI/SNR/PacketLoss values
- Real-time link quality metrics for ML training
- Compatible with PyTorch neural network training pipeline
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