Stacked Intelligent Metasurfaces for Multicarrier Cognitive Radio ISAC
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908660559511552 |
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| author | Fadakar, Alireza Molisch, Andreas F. |
| author_facet | Fadakar, Alireza Molisch, Andreas F. |
| contents | The fusion of cognitive radio (CR) and integrated sensing and communication (ISAC), enabled by stacked intelligent metasurfaces (SIMs), offers a promising path for multi-functional programmable front ends in 6G and beyond. In this paper we propose a novel CR-ISAC framework that leverages an SIM integrated with the secondary base station (SB) to learn and realize optimal beampatterns that simultaneously (i) minimize the Bayesian Cramér-Rao bound (BCRB) for localizing a secondary user equipment (SU) and (ii) limit averaged interference at primary user equipments (PUs) so that spectral efficiency loss is constrained, with the target of at most a few percent degradation. We propose an efficient alternating optimization-based algorithm to obtain the optimal end-to-end transmission response of the SIM for all orthogonal frequency division multiplexing (OFDM) subcarriers. Drawing an analogy between the layered SIM architecture and deep neural networks, we define a beampattern- matching loss, derive analytical gradients for backpropagation, and implement a learning-based optimization of the SIM coefficients using a mini-batch Adam optimizer. A complexity analysis is provided, and extensive numerical experiments are performed to evaluate the proposed CR-ISAC framework. The results show that the proposed SIM coefficient optimization methods attain near-optimal performance in terms of both the SU BCRB localization metric and the PUs average spectral efficiency when the SIM has a sufficient number of layers, and they substantially outperform traditional single-layer reconfigurable intelligent surface (RIS) designs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13933 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stacked Intelligent Metasurfaces for Multicarrier Cognitive Radio ISAC Fadakar, Alireza Molisch, Andreas F. Signal Processing The fusion of cognitive radio (CR) and integrated sensing and communication (ISAC), enabled by stacked intelligent metasurfaces (SIMs), offers a promising path for multi-functional programmable front ends in 6G and beyond. In this paper we propose a novel CR-ISAC framework that leverages an SIM integrated with the secondary base station (SB) to learn and realize optimal beampatterns that simultaneously (i) minimize the Bayesian Cramér-Rao bound (BCRB) for localizing a secondary user equipment (SU) and (ii) limit averaged interference at primary user equipments (PUs) so that spectral efficiency loss is constrained, with the target of at most a few percent degradation. We propose an efficient alternating optimization-based algorithm to obtain the optimal end-to-end transmission response of the SIM for all orthogonal frequency division multiplexing (OFDM) subcarriers. Drawing an analogy between the layered SIM architecture and deep neural networks, we define a beampattern- matching loss, derive analytical gradients for backpropagation, and implement a learning-based optimization of the SIM coefficients using a mini-batch Adam optimizer. A complexity analysis is provided, and extensive numerical experiments are performed to evaluate the proposed CR-ISAC framework. The results show that the proposed SIM coefficient optimization methods attain near-optimal performance in terms of both the SU BCRB localization metric and the PUs average spectral efficiency when the SIM has a sufficient number of layers, and they substantially outperform traditional single-layer reconfigurable intelligent surface (RIS) designs. |
| title | Stacked Intelligent Metasurfaces for Multicarrier Cognitive Radio ISAC |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2511.13933 |