Quantum Deep Learning for Massive MIMO User Scheduling
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915428465377280 |
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| author | Huang, Xingyu Fan, Ruining Chakraborty, Mouli Nag, Avishek Mukherjee, Anshu |
| author_facet | Huang, Xingyu Fan, Ruining Chakraborty, Mouli Nag, Avishek Mukherjee, Anshu |
| contents | We introduce a hybrid Quantum Neural Networks (QNN) architecture for the efficient user scheduling in 5G/Beyond 5G (B5G) massive Multiple Input Multiple Output (MIMO) systems, addressing the scalability issues of traditional methods. By leveraging statistical Channel State Information (CSI), our model reduces computational overhead and enhances spectral efficiency. It integrates classical neural networks with a variational quantum circuit kernel, outperforming classical Convolutional Neural Networks (CNNs) and maintaining robust performance in noisy channels. This demonstrates the potential of quantum-enhanced Machine Learning (ML) for wireless scheduling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03327 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Deep Learning for Massive MIMO User Scheduling Huang, Xingyu Fan, Ruining Chakraborty, Mouli Nag, Avishek Mukherjee, Anshu Signal Processing We introduce a hybrid Quantum Neural Networks (QNN) architecture for the efficient user scheduling in 5G/Beyond 5G (B5G) massive Multiple Input Multiple Output (MIMO) systems, addressing the scalability issues of traditional methods. By leveraging statistical Channel State Information (CSI), our model reduces computational overhead and enhances spectral efficiency. It integrates classical neural networks with a variational quantum circuit kernel, outperforming classical Convolutional Neural Networks (CNNs) and maintaining robust performance in noisy channels. This demonstrates the potential of quantum-enhanced Machine Learning (ML) for wireless scheduling. |
| title | Quantum Deep Learning for Massive MIMO User Scheduling |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2508.03327 |