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• W4315630161 abstract "Soft demodulation of received symbols into bit log-likelihood ratios (LLRs) is at the very heart of multiple-input-multiple-output (MIMO) detection. However, the optimal maximum a posteriori (MAP) detector is complicated and infeasible to be used in a practical system. In this paper, we propose a soft MIMO detection algorithm based on marginal posterior probability statistics (MPPS). With the help of optimal transport theory and order statistics theory, we transform the posteriori probability distribution of each layer into a Gaussian distribution. Then the full sampling paths can be implicitly restored from the first- and second-order moment statistics of the transformed distribution. A lightweight network is designed to learn to recover the log-MAP LLRs from the moment statistics with low complexity. Simulation results show that the proposed algorithm can improve the performance significantly with reduced samples under fading and correlated channels." @default.
• W4315630161 created "2023-01-12" @default.
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• W4315630161 date "2022-12-04" @default.
• W4315630161 modified "2023-10-18" @default.
• W4315630161 title "Soft MIMO Detection Using Marginal Posterior Probability Statistics" @default.
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• W4315630161 doi "https://doi.org/10.1109/globecom48099.2022.10001517" @default.
• W4315630161 hasPublicationYear "2022" @default.
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