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

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...

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Bibliographic Details
Published in:arXiv.org 2024-03
Main Authors: Hagiwara, Junichiro, Matsumura, Kazushi, Asumi, Hiroki, Kasuga, Yukiko, Nishimura, Toshihiko, Sato, Takanori, Ogawa, Yasutaka, Ohgane, Takeo
Format: Article
Language:English
Subjects:
Markov chains
MIMO communication
Mixtures
Monte Carlo simulation
Normal distribution
Polynomials
Probability distribution
Signal detection
Statistical analysis
Transmission efficiency
Wireless communications
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2301.03196