wireless communications

Radar-Aided Near-Field Beam Prediction via Beam Map Learning for XL-MIMO V2I Communications

arXiv:2607.27643

summary

The paper introduces a passive radar‑aided framework that predicts near‑field beams for XL‑MIMO vehicle‑to‑infrastructure links by learning a mapping from radar Bartlett spectra to communication beam maps using a lightweight encoder‑decoder CNN, achieving higher accuracy and spectral efficiency.

Abstract

Near-field beam training in extremely large-scale multiple-input multiple-output (XL-MIMO) vehicle-to-infrastructure (V2I) systems incurs high overhead due to large range-angle codebooks and rapid channel variation. This paper proposes a passive radar-aided framework for near-field beam prediction based on radar-to-beam map learning. By exploiting the spatial correlation between radar observations and communication signals, the proposed method maps radar Bartlett spectra to communication beam maps using a lightweight encoder-decoder convolutional neural network. Gaussian soft supervision is further introduced to preserve beam-space continuity. Simulations on a synchronized Sionna ray tracing radar-communication dataset show that the proposed method consistently improves Top-k accuracy, distance-based accuracy, beam loss, and spectral efficiency.

Topics & keywords

#xl-mimo#v2i communications#near-field beam prediction#radar-aided learning#deep learningradar-to-beam map learningencoder-decoder convolutional neural networkGaussian soft supervisionBartlett spectrumSionna ray tracing dataset
Radar-Aided Near-Field Beam Prediction via Beam Map Learning for XL-MIMO V2I Communications · wovepaper