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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2412.02391 |
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| _version_ | 1866918202626277376 |
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| author | Hagiwara, Junichiro Nishimura, Toshihiko Sato, Takanori Ogawa, Yasutaka Ohgane, Takeo |
| author_facet | Hagiwara, Junichiro Nishimura, Toshihiko Sato, Takanori Ogawa, Yasutaka Ohgane, Takeo |
| contents | Multiple-input multiple-output (MIMO) technology is essential for the optimal functioning of next-generation wireless networks; however, enhancing its signal-detection performance for improved spectral efficiency is challenging. Here, we propose an approach that transforms the discrete MIMO detection problem into a continuous problem while leveraging the efficient Hamiltonian Monte Carlo algorithm. For this continuous framework, we employ a mixture of t-distributions as the prior distribution. To improve the performance in the coded case further, we treat the likelihood's temperature parameter as a random variable and address its optimization. This treatment leads to the adoption of a horseshoe density for the likelihood. Theoretical analysis and extensive simulations demonstrate that our method achieves near-optimal detection performance while maintaining polynomial computational complexity. This MIMO detection technique can accelerate the development of 6G mobile communication systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02391 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hamiltonian Monte Carlo-Based Near-Optimal MIMO Signal Detection Hagiwara, Junichiro Nishimura, Toshihiko Sato, Takanori Ogawa, Yasutaka Ohgane, Takeo Networking and Internet Architecture Multiple-input multiple-output (MIMO) technology is essential for the optimal functioning of next-generation wireless networks; however, enhancing its signal-detection performance for improved spectral efficiency is challenging. Here, we propose an approach that transforms the discrete MIMO detection problem into a continuous problem while leveraging the efficient Hamiltonian Monte Carlo algorithm. For this continuous framework, we employ a mixture of t-distributions as the prior distribution. To improve the performance in the coded case further, we treat the likelihood's temperature parameter as a random variable and address its optimization. This treatment leads to the adoption of a horseshoe density for the likelihood. Theoretical analysis and extensive simulations demonstrate that our method achieves near-optimal detection performance while maintaining polynomial computational complexity. This MIMO detection technique can accelerate the development of 6G mobile communication systems. |
| title | Hamiltonian Monte Carlo-Based Near-Optimal MIMO Signal Detection |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2412.02391 |