Stima di Canale con Reti Neurali e Ricetrasmettitori V2X Accelerati da IA

V2AIX adotta Graph Transformer per prevedere interferenze e allineare fasci radio direzionali in movimento ad alta velocità.

Prevedere la fisica wireless con il deep learning on-chip

La stima classica del canale tramite simboli pilota soffre a velocità veicolari superiori a 120 km/h a causa della rapida dispersione Doppler. V2AIX addestra reti neurali ricorrenti on-chip per prevedere gli stati del canale radio con una precisione del 99,4%.

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