神经网络物理信道自适应估计与人工智能增强型 V2X 空口收发信机

V2AIX 创新应用图注意力网络 (GAT) 建模多车空间拓扑，超前预测车流盲区多径反射干扰并在相对高速对向驶过时毫秒级动态锁定天线赋形主瓣。

利用片上深度学习预测无线信道物理特性

当车速超过 120 公里/小时时，传统的导频符号信道估计会因快速多普勒色散而失效。V2AIX 在芯片上训练循环神经网络，以 99.4% 的准确率预测无线信道状态。

Core Engineering Areas

• Neural Network Channel Estimation in High-Doppler Environments — Overcoming severe high-speed multipath fading on modern automotive highways.
• Deep Reinforcement Learning for Autonomous Sidelink MAC — Eliminating packet collisions in congested urban vehicle platoons.
• Edge AI Beam Tracking for Millimeter and Sub-THz Sidelink — Overcoming the pencil-beam pointing problem without wasting frames on exhaustive sector sweeps.

Technical Articles

• Deep Reinforcement Learning for Autonomous V2X MAC Layers: Real-World Testbed Results
• Deep Learning Architectures for High-Speed Millimeter-Wave Beamforming

Related Cluster Architecture Specifications

• AutomotiveV2X — Connected Vehicle-to-Everything & Cooperative ITS Architecture
• CellularV2X — 3GPP C-V2X Architecture, PC5 Direct Sidelink & Cellular Uu Infrastructure
• NRV2X — 5G NR V2X Sidelink, Sub-Millisecond Latency & High-Rate Sensor Sharing
• ISACV2X — Integrated Sensing and Communication (ISAC) for Automotive V2X