论文标题
带有深度学习参数选择的修改EP MIMO检测算法
Modified EP MIMO Detection Algorithm with Deep Learning Parameters Selection
论文作者
论文摘要
由于其出色的性能，基于期望传播（EP）的多输入多输出（MIMO）检测器被视为最先进的MIMO检测器。但是，我们发现EP MIMO检测器无法保证由于经验参数选择（包括初始方差和阻尼因子）而实现最佳性能。根据矩匹配和参数选择对EP MIMO检测器性能的影响，我们提出了一个修改的EP MIMO检测器（MEPD）。为了获得最佳的初始差异和阻尼因素，我们采用了一个深度学习方案，其中我们展开了MEPD的迭代处理，以建立用于参数培训的MEPNET。模拟结果表明，具有离线训练参数的MEPD在各种MIMO场景中的原始参数优于原始参数。此外，在实际情况下，提出的具有深度学习参数选择的MEPD比EPD更强大。
Expectation Propagation (EP)-based Multiple-Input Multiple-Output (MIMO) detector is regarded as a state-of-the-art MIMO detector because of its exceptional performance. However, we find that the EP MIMO detector cannot guarantee to achieve the optimal performance due to the empirical parameter selection, including initial variance and damping factors. According to the influence of the moment matching and parameter selection for the performance of the EP MIMO detector, we propose a modified EP MIMO detector (MEPD). In order to obtain the optimal initial variance and damping factors, we adopt a deep learning scheme, in which we unfold the iterative processing of MEPD to establish MEPNet for parameters training. The simulation results show that MEPD with off-line trained parameters outperforms the original one in various MIMO scenarios. Besides, the proposed MEPD with deep learning parameters selection is more robust than EPD in practical scenarios.
