RayAR: Autoregressive Dominant Path Generation for Low-Overhead MIMO Beam Alignment

Abstract

Beam alignment in multiple-input multiple-output (MIMO) systems requires selecting suitable transmit and receive beam pairs with limited measurement overhead. Although 3GPP Release 18 investigates beam prediction from partial measurements, collecting these measurements still incurs probing overhead. Geometry can focus this probing through the departure and arrival directions of dominant paths, whose identification requires accounting for coupled diffraction interactions. To address this challenge, we propose RayAR, an autoregressive method that generates dominant propagation paths from urban geometry. RayAR conditions candidate ranking on the path prefix, local diffraction geometry, and receiver reachability, updating this context after each selection until the receiver becomes visible. The completed path's departure and arrival directions rank beam pairs for probing, allowing the pair with the highest received power to be selected from a small candidate set. Under 3GPP Release 18 beam-management metrics, RayAR reduces probing by 96.88\% relative to exhaustive sweeping and achieves the highest optimal-pair hit rate among compared methods at the same budget. Real-world measurements further demonstrate the accuracy of this geometric guidance, with simulation-trained RayAR achieving the lowest departure and arrival angular errors among compared methods without fine-tuning and a mean arrival-direction error of 9.07$^\circ$.