U6G XL-MIMO Radiomap Prediction: Multi-Config Dataset and Beam Map Approach

TL;DR

The beam map is proposed, a physics-informed spatial feature that analytically computes array-specific coverage patterns and reduces mean absolute error by up to 60.0% when generalizing to unseen configurations and up to 50.5% when transferring to unseen environments.

Abstract

The upper 6 GHz (U6G) band with XL-MIMO is a key enabler for sixth-generation wireless systems, yet intelligent radiomap prediction for such systems remains challenging. Existing datasets support only small-scale arrays (up to 8x8) with predominantly isotropic antennas, far from the 1024-element directional arrays envisioned for 6G. Moreover, current methods encode array configurations as scalar parameters, forcing neural networks to extrapolate array-specific radiation patterns, which fails when predicting radiomaps for configurations absent from training data. To jointly address data scarcity and generalization limitations, this paper advances XL-MIMO radiomap prediction from three aspects. To overcome data limitations, we construct the first XL-MIMO radiomap dataset containing 78400 radiomaps across 800 urban scenes, five frequency bands (1.8-6.7 GHz), and nine array configurations up to 32x32 uniform planar arrays with directional elements. To enable systematic evaluation, we establish a comprehensive benchmark framework covering practical scenarios from coverage estimation without field measurements to generalization across unseen configurations and environments. To enable generalization to arbitrary beam configurations without retraining, we propose the beam map, a physics-informed spatial feature that analytically computes array-specific coverage patterns. By decoupling deterministic array radiation from data learned multipath propagation, beam maps shift generalization from neural network extrapolation to physics-based computation. Integrating beam maps into existing architectures reduces mean absolute error by up to 60.0% when generalizing to unseen configurations and up to 50.5% when transferring to unseen environments. The complete dataset and code are publicly available at https://lxj321.github.io/MulticonfigRadiomapDataset/.

Figures (9)

• Figure 1: System model of XL-MIMO radiomap prediction, where the radiomap characterizes spatial signal strength distribution across a $K\times K$ grid observation plane with color indicating received power level (yellow: high, dark blue: low).
• Figure 2: XL-MIMO radiomap dataset generation pipeline comprising geographical acquisition, 3D modeling, ray-tracing simulation, and SSB beamforming stages.
• Figure 3: Geographic diversity of the proposed dataset: (a) sparse urban (0.1% building coverage), (b) mixed urban (16.5%), and (c) dense urban (51.7%) scenes.
• Figure 4: Frequency-dependent coverage characteristics for an $8\times8$ UPA: (a)--(d) radiomaps across four carrier frequencies (2.6--6.7 GHz), and (e) coverage ratio at three RSRP thresholds.
• Figure 5: Impact of array architecture on coverage patterns at 6.7 GHz: (a)--(c) radiomaps for $8\times8$, $16\times16$, and $32\times32$ UPA configurations; (d) peak-to-mean power ratio quantifying beam concentration; and (e) top-5% power concentration measuring spatial energy focusing.