1. Near-optimal stochastic MIMO signal detection with a mixture of t-distribution prior
- Author
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Hagiwara, Junichiro, Matsumura, Kazushi, Asumi, Hiroki, Kasuga, Yukiko, Nishimura, Toshihiko, Sato, Takanori, Ogawa, Yasutaka, Ohgane, Takeo, Hagiwara, Junichiro, Matsumura, Kazushi, Asumi, Hiroki, Kasuga, Yukiko, Nishimura, Toshihiko, Sato, Takanori, Ogawa, Yasutaka, and Ohgane, Takeo
- Abstract
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., Comment: Published in the 2023 IEEE Global Communications Conference (GLOBECOM)
- Published
- 2023
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