Channel Modeling for FR3 Upper Mid-band via Generative Adversarial Networks
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911854271397888 |
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| author | Hu, Yaqi Yin, Mingsheng Mezzavilla, Marco Guo, Hao Rangan, Sundeep |
| author_facet | Hu, Yaqi Yin, Mingsheng Mezzavilla, Marco Guo, Hao Rangan, Sundeep |
| contents | The upper mid-band (FR3) has been recently attracting interest for new generation of mobile networks, as it provides a promising balance between spectrum availability and coverage, which are inherent limitations of the sub 6GHz and millimeter wave bands, respectively. In order to efficiently design and optimize the network, channel modeling plays a key role since FR3 systems are expected to operate at multiple frequency bands. Data-driven methods, especially generative adversarial networks (GANs), can capture the intricate relationships among data samples, and provide an appropriate tool for FR3 channel modeling. In this work, we present the architecture, link state model, and path generative network of GAN-based FR3 channel modeling. The comparison of our model greatly matches the ray-tracing simulated data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17069 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Channel Modeling for FR3 Upper Mid-band via Generative Adversarial Networks Hu, Yaqi Yin, Mingsheng Mezzavilla, Marco Guo, Hao Rangan, Sundeep Information Theory Machine Learning Signal Processing The upper mid-band (FR3) has been recently attracting interest for new generation of mobile networks, as it provides a promising balance between spectrum availability and coverage, which are inherent limitations of the sub 6GHz and millimeter wave bands, respectively. In order to efficiently design and optimize the network, channel modeling plays a key role since FR3 systems are expected to operate at multiple frequency bands. Data-driven methods, especially generative adversarial networks (GANs), can capture the intricate relationships among data samples, and provide an appropriate tool for FR3 channel modeling. In this work, we present the architecture, link state model, and path generative network of GAN-based FR3 channel modeling. The comparison of our model greatly matches the ray-tracing simulated data. |
| title | Channel Modeling for FR3 Upper Mid-band via Generative Adversarial Networks |
| topic | Information Theory Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2404.17069 |