AIXhaul: AI-Driven Xhaul Routing & Neural Beam Forming Optimization
AIXhaul applies deep reinforcement learning (DRL) and neural predictive controllers to dynamically optimize hybrid satellite-terrestrial fronthaul, midhaul, and backhaul (Xhaul) routing pipelines.
AIXhaul Deep Reinforcement Learning Cross-Domain Routing Pipeline
Continuous closed-loop telemetry feedback tuning physical RF beams and optical crosslinks
Central System: Network Telemetry Collector - Samples SNR, packet loss, and buffer occupancy at 1 kHz
Network Nodes
- Network Telemetry Collector
- Deep Q-Network / PPO Agent
- Neural Beamforming Controller
- P4 Programmable Hardware Switch
Architectural Protocol Layers
- Spaceborne PHY/MAC Layer (3GPP Rel-17 TS 38.300): Doppler compensation, HARQ disabling, and timing advance extension.
- Orbital Mesh Routing (CCSDS / Delay-Tolerant Networking): Dynamic topology routing over intermittent laser crosslinks.
- Regenerative Core Processing (OpenRAN / 3GPP CU-DU Split): Onboard user plane function (UPF) local breakout.
Key Metrics: < 18ms (LEO Orbit-to-Ground Roundtrip) | 100 Gbps (Optical Crosslink Throughput) | ±48 kHz (Doppler Shift Compensation) | 99.999% (Orbital Constellation Availability)
AI-Driven Xhaul Transport, Deep Reinforcement Learning Beam Tracking & Network Slicing
Core Engineering Areas
Technical Articles
Related Cluster Architecture Specifications
- NonTerrestrialNetwork — 3GPP 5G/6G NTN Satellite Architecture, Direct-to-Cell & Non-Terrestrial Infrastructure
- NonTerrestrialNetworks — Multi-Orbit Heterogeneous Constellation Interworking (LEO / MEO / GEO / HAPS)
- AirspaceSync — Doppler Compensation, Relativistic Timing & Airspace-Space Synchronization
- FSOComm — Free Space Optics, High-Bandwidth Space Laser Communications & Atmospheric Mitigation