Near-optimal stochastic MIMO signal detection with a mixture of t-distribution prior

Junichiro Hagiwara; Kazushi Matsumura; Hiroki Asumi; Yukiko Kasuga; Toshihiko Nishimura; Takanori Sato; Yasutaka Ogawa; Takeo Ohgane

doi:10.1109/globecom54140.2023.10437162

arXiv · 2301.03196

Near-optimal stochastic MIMO signal detection with a mixture of t-distribution prior

Junichiro Hagiwara·Kazushi Matsumura·Hiroki Asumi·Yukiko Kasuga·Toshihiko Nishimura·Takanori Sato·Yasutaka Ogawa·Takeo Ohgane

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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.

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BibTeX RIS

Junichiro Hagiwara, Kazushi Matsumura, Hiroki Asumi, Yukiko Kasuga, Toshihiko Nishimura, Takanori Sato, Yasutaka Ogawa, Takeo Ohgane. 2023-01-09. Near-optimal stochastic MIMO signal detection with a mixture of t-distribution prior. https://doi.org/10.1109/globecom54140.2023.10437162

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