UrbanMIMOMap: A Ray-Traced MIMO CSI Dataset with Precoding-Aware Maps and Benchmarks

2025-09-07

AI Summary

Existing public channel datasets primarily target SISO systems and provide only coarse-grained channel information (e.g., path loss), rendering them inadequate for 6G MIMO environment sensing and high-precision radio map (RM) construction, which require fine-grained, full-dimensional channel state information (CSI). To address this gap, we introduce UrbanMIMOMap—the first large-scale, electromagnetic-field-level ray-traced MIMO CSI dataset tailored to urban environments. It provides spatially dense, full-dimensional complex-valued CSI matrices and precoding-aware radio maps. We further propose a standardized, deep-learning-ready data format and an integrated benchmarking framework. The dataset, associated code, and baseline RM modeling implementations are fully open-sourced. This work bridges a critical void in high-fidelity MIMO channel modeling and sensing-communication integration, establishing foundational infrastructure for data-driven 6G integrated sensing and communication (ISAC) research.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Web Mining and Content Analysis: Web data generation and simulation
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Sixth generation (6G) systems require environment-aware communication, driven by native artificial intelligence (AI) and integrated sensing and communication (ISAC). Radio maps (RMs), providing spatially continuous channel information, are key enablers. However, generating high-fidelity RM ground truth via electromagnetic (EM) simulations is computationally intensive, motivating machine learning (ML)-based RM construction. The effectiveness of these data-driven methods depends on large-scale, high-quality training data. Current public datasets often focus on single-input single-output (SISO) and limited information, such as path loss, which is insufficient for advanced multi-input multi-output (MIMO) systems requiring detailed channel state information (CSI). To address this gap, this paper presents UrbanMIMOMap, a novel large-scale urban MIMO CSI dataset generated using high-precision ray tracing. UrbanMIMOMap offers comprehensive complex CSI matrices across a dense spatial grid, going beyond traditional path loss data. This rich CSI is vital for constructing high-fidelity RMs and serves as a fundamental resource for data-driven RM generation, including deep learning. We demonstrate the dataset's utility through baseline performance evaluations of representative ML methods for RM construction. This work provides a crucial dataset and reference for research in high-precision RM generation, MIMO spatial performance, and ML for 6G environment awareness. The code and data for this work are available at: https://github.com/UNIC-Lab/UrbanMIMOMap.

Problem

• Lack of high-fidelity MIMO channel datasets for environment-aware 6G systems
• Insufficient public datasets with detailed CSI beyond SISO path loss
• Need for large-scale training data for machine learning-based radio map construction

Innovation

• Ray-traced MIMO CSI dataset generation
• Comprehensive complex CSI matrices provision
• Baseline ML evaluations for radio maps