AIXhaul: AI 기반 Xhaul 라우팅 및 신경망 빔포밍 최적화 시스템

AIXhaul은 심층 강화학습(DRL)과 신경망 예측 제어를 적용하여 위성-지상 하이브리드 프론트홀, 미드홀, 백홀(Xhaul) 경로를 동적으로 최적화합니다.

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)

인공지능(AI) 기반 Xhaul 전송망, 심층 강화학습 빔 트래킹 및 동적 네트워크 슬라이싱

Core Engineering Areas

Technical Articles

Related Cluster Architecture Specifications