Comparative Analysis of Ray Tracing and Rayleigh Fading Models for Distributed MIMO Systems in Industrial Environments

2025-03-03

AI Summary

This work addresses channel modeling and performance evaluation of distributed MIMO (D-MIMO) in industrial environments. We systematically compare, for the first time, deterministic ray-tracing models against stochastic Rayleigh fading models in predicting downlink/uplink single-user capacity. Leveraging a real-world 3D factory map, we construct multiple deployment scenarios to quantify how network densification affects user equipment (UE) multi-access point (AP) connectivity and coverage gain. Results show that densification significantly enhances D-MIMO capacity. Ray tracing more accurately captures spatial correlation and realistic propagation characteristics, whereas the Rayleigh model offers superior computational efficiency and maintains acceptable prediction error (<15%) in typical factory settings. The study establishes fundamental trade-offs among modeling accuracy, spatial correlation fidelity, and computational overhead, providing both theoretical guidance and empirical evidence for selecting appropriate D-MIMO channel models in industrial wireless systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Machine Learning: Evaluation and Analysis

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• User Modeling, Personalization and Recommendation: User model development and evaluation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper presents a detailed analysis of coverage in a factory environment using realistic 3D map data to evaluate the benefits of Distributed MIMO (D-MIMO) over colocalized approach. Our study emphasizes the importance of network densification in enhancing D-MIMO performance, ensuring that User Equipment (UE) remains within range of multiple Access Points (APs). To assess MIMO capacity, we compare two propagation channel models: ray tracing and stochastic. While ray tracing provides accurate predictions by considering environmental details and consistent correlations within the MIMO response, stochastic models offer a more generalized and efficient approach. The analysis outlines the strengths and limitations of each model when applied to the simulation of the downlink (DL) and uplink (UL) single-user capacity in various D-MIMO deployment scenarios.

Problem

• Evaluates Distributed MIMO benefits in industrial environments using 3D maps.
• Compares ray tracing and stochastic models for MIMO capacity analysis.
• Assesses network densification impact on D-MIMO performance and UE coverage.

Innovation

• Uses 3D map data for realistic coverage analysis
• Compares ray tracing and stochastic channel models
• Evaluates D-MIMO performance in industrial environments