论文标题
使用边缘后概率统计的软含量检测
Soft MIMO Detection Using Marginal Posterior Probability Statistics
论文作者
论文摘要
将接收的符号的软调谐到位对数可能性比率（LLRS）是多输入 - 型号输出（MIMO）检测的核心。但是，最佳最大后验（MAP）检测器是复杂的，并且在实用系统中使用不可行。在本文中，我们提出了一种基于边际后概率统计（MPP）的软MIMO检测算法。借助最佳运输理论和秩序统计理论，我们将每一层的后验概率分布转化为高斯分布。然后，可以从转化分布的一阶和二阶统计数据中隐式恢复完整的采样路径。轻量级网络旨在学习从复杂性低的时刻统计信息中恢复日志图LLR。仿真结果表明，所提出的算法可以通过褪色和相关通道下的样品减少来显着提高性能。
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 recovery 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.
