Evaluating the Performance of Reconfigurable Intelligent Base Stations through Ray Tracing

2025-07-11

AI Summary

Evaluating the performance of reconfigurable intelligent base stations (RIBS) under reduced radio-frequency (RF) chain counts remains challenging due to inaccuracies in conventional statistical channel models. Method: This work proposes a location-specific channel modeling framework based on ray tracing, integrating the SIONNA ray-tracing module with 3GPP-compliant statistical models. It jointly optimizes transmit power and reconfigurable intelligent surface (RIS) configurations to maximize sum spectral efficiency in massive MIMO scenarios. Contribution/Results: Experiments demonstrate that ray tracing significantly improves prediction accuracy of spectral efficiency—particularly in typical urban environments—by capturing site-specific propagation characteristics more faithfully than standard statistical models. The approach reveals substantial potential gains of RIBS in structured deployments. This study establishes a reproducible, high-fidelity modeling paradigm for RIS-empowered intelligent base station design and deployment. However, hardware-level validation remains pending.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Massive multiple-input multiple-output (mMIMO) is a key capacity-boosting technology in 5G wireless systems. To reduce the number of radio frequency (RF) chains needed in such systems, a novel approach has recently been introduced involving an antenna array supported by a reconfigurable intelligent surface. This arrangement, known as a reconfigurable intelligent base station (RIBS), offers performance comparable to that of a traditional mMIMO array, but with significantly fewer RF chains. Given the growing importance of precise, location-specific performance prediction, this paper evaluates the performance of an RIBS system by means of the SIONNA ray-tracing module. That performance is contrasted against results derived from a statistical 3GPP-compliant channel model, optimizing power and RIS configuration to maximize the sum spectral efficiency. Ray tracing predicts better performance than the statistical model in the evaluated scenario, suggesting the potential of site-specific modeling. However, empirical validation is needed to confirm this advantage.

Problem

• Evaluating RIBS performance using ray tracing
• Comparing RIBS with traditional mMIMO systems
• Optimizing power and RIS configuration

Innovation

• Reconfigurable intelligent surface reduces RF chains
• Ray tracing evaluates RIBS performance precisely
• Optimizes power and RIS for spectral efficiency