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

2026-03-06

AI Summary

This study addresses the lack of datasets and generalizable methods for accurate radiation pattern prediction in sub-6 GHz extra-large-scale MIMO (XL-MIMO) systems targeting 6G, particularly across diverse configurations. To this end, the authors construct the first XL-MIMO radiation map dataset encompassing 800 urban scenarios, five frequency bands, and nine array configurations (up to 32×32). They propose a physics-informed “beammap” feature that explicitly decouples array radiation patterns from multipath propagation, enabling zero-shot generalization to arbitrary beamforming configurations without retraining. By shifting generalization from neural network extrapolation to physical computation, the method reduces the mean absolute error by up to 60.0% on unseen array configurations and by 50.5% in novel environments. The complete dataset and code are publicly released.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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/.

Problem

• XL-MIMO
• radiomap prediction
• U6G
• generalization
• array configuration

Innovation

• XL-MIMO
• radiomap prediction
• beam map
• generalization
• U6G