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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2507.08611 |
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| _version_ | 1866918089070739456 |
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| author | Beyraghi, Sina Interdonato, Giovanni Geraci, Giovanni Buzzi, Stefano Lozano, Angel |
| author_facet | Beyraghi, Sina Interdonato, Giovanni Geraci, Giovanni Buzzi, Stefano Lozano, Angel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08611 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating the Performance of Reconfigurable Intelligent Base Stations through Ray Tracing Beyraghi, Sina Interdonato, Giovanni Geraci, Giovanni Buzzi, Stefano Lozano, Angel Information Theory 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. |
| title | Evaluating the Performance of Reconfigurable Intelligent Base Stations through Ray Tracing |
| topic | Information Theory |
| url | https://arxiv.org/abs/2507.08611 |