Evaluating the Performance of Reconfigurable Intelligent Base Stations through Ray Tracing

TL;DR

This paper assesses the performance of reconfigurable intelligent base stations using ray tracing to provide more accurate, site-specific predictions compared to traditional statistical models, highlighting the potential benefits of detailed environmental modeling.

Contribution

It introduces a ray-tracing based evaluation method for RIBS performance, demonstrating improved prediction accuracy over statistical models in specific scenarios.

Findings

Ray tracing predicts better performance than statistical models in the evaluated scenario.

Site-specific modeling with ray tracing shows potential advantages for RIBS performance prediction.

Empirical validation is needed to confirm the predicted benefits.

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.