Evaluating the Performance of Reconfigurable Intelligent Base Stations through Ray Tracing

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. The results indicate that traditional statistical models tend to exaggerate adverse outcomes, as ray tracing consistently delivers better performance. This underscores the benefit of environment-specific solutions.