Near-optimal stochastic MIMO signal detection with a mixture of t-distribution prior
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866917606823297024 |
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| author | Hagiwara, Junichiro Matsumura, Kazushi Asumi, Hiroki Kasuga, Yukiko Nishimura, Toshihiko Sato, Takanori Ogawa, Yasutaka Ohgane, Takeo |
| author_facet | Hagiwara, Junichiro Matsumura, Kazushi Asumi, Hiroki Kasuga, Yukiko Nishimura, Toshihiko Sato, Takanori Ogawa, Yasutaka Ohgane, Takeo |
| contents | Multiple-input multiple-output (MIMO) systems will play a crucial role in future wireless communication, but improving their signal detection performance to increase transmission efficiency remains a challenge. To address this issue, we propose extending the discrete signal detection problem in MIMO systems to a continuous one and applying the Hamiltonian Monte Carlo method, an efficient Markov chain Monte Carlo algorithm. In our previous studies, we have used a mixture of normal distributions for the prior distribution. In this study, we propose using a mixture of t-distributions, which further improves detection performance. Based on our theoretical analysis and computer simulations, the proposed method can achieve near-optimal signal detection with polynomial computational complexity. This high-performance and practical MIMO signal detection could contribute to the development of the 6th-generation mobile network. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_03196 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Near-optimal stochastic MIMO signal detection with a mixture of t-distribution prior Hagiwara, Junichiro Matsumura, Kazushi Asumi, Hiroki Kasuga, Yukiko Nishimura, Toshihiko Sato, Takanori Ogawa, Yasutaka Ohgane, Takeo Networking and Internet Architecture Information Theory Multiple-input multiple-output (MIMO) systems will play a crucial role in future wireless communication, but improving their signal detection performance to increase transmission efficiency remains a challenge. To address this issue, we propose extending the discrete signal detection problem in MIMO systems to a continuous one and applying the Hamiltonian Monte Carlo method, an efficient Markov chain Monte Carlo algorithm. In our previous studies, we have used a mixture of normal distributions for the prior distribution. In this study, we propose using a mixture of t-distributions, which further improves detection performance. Based on our theoretical analysis and computer simulations, the proposed method can achieve near-optimal signal detection with polynomial computational complexity. This high-performance and practical MIMO signal detection could contribute to the development of the 6th-generation mobile network. |
| title | Near-optimal stochastic MIMO signal detection with a mixture of t-distribution prior |
| topic | Networking and Internet Architecture Information Theory |
| url | https://arxiv.org/abs/2301.03196 |