Negentropy-Aware Loss Function for Trainable Belief Propagation in Coded MIMO Detection
Negentropy-Aware Loss Function for Trainable Belief Propagation in Coded MIMO Detection
复制标题
编码 MIMO 检测中可训练置信传播的负熵感知损失函数
DOI：
10.1109/globecom46510.2021.9685863
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Sampei Seiichi
中科院分区：
文献类型：
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作者：
Shirase Daichi;Takahashi Takumi;Ibi Shinsuke;Muraoka Kazushi;Ishii Naoto;Sampei Seiichi
我们考虑在编码多用户MIMO（MU-MIMO）系统中通过深度展开辅助置信传播（BP）的大规模多用户检测（MUD）。通过嵌入式内部参数的数据驱动调整优化（训练）的BP检测器实现了低复杂度和高精度的MUD，同时补偿了实际的缺陷。但在实际实现中，这些参数需要根据系统参数进行优化，例如，调制和编码方案（MCS）。特别是，当使用信道编码时，重要的是不仅要最小化均方误差（MSE），而且要增强输出对数似然比（LLR）的高斯性，以便最大化后续软判决解码器的纠错能力。为此，提出了一种新的损失函数的基础上加权平均的负熵，这是一个关键的措施来评估高斯性，和检测器输出的MSE。仿真结果表明，采用该负熵损失函数优化的可训练高斯BP（T-GaBP）检测器显著提高了解码器输出的误比特率（BER）性能，且性能明显优于采用典型MSE损失函数优化的T-GaBP检测器.
We consider large multi-user detection (MUD) via deep unfolding-aided belief propagation (BP) in coded multi-user MIMO (MU-MIMO) systems. A BP detector optimized (trained) by data-driven-tuning of embedded internal parameters achieves low-complexity and high-accuracy MUD while compensating practical imperfections. However, in actual implementation, these parameters should be optimized according to system parameters, e.g., modulation and coding scheme (MCS). In particular, when channel coding is used, it is vital not only to minimize the mean square error (MSE) but also to enhance the Gaussianity of the output log-likelihood ratio (LLR), in order to maximize the error correction capability of the subsequent soft-decision decoder. To that end, a novel loss function based on a weighted average of negentropy, which is a key measure to evaluate the Gaussianity, and MSE of the detector output is proposed. Simulation results show that the trainable Gaussian BP (T-GaBP) detector optimized with the proposed negentropy-aware loss function significantly improves the bit error rate (BER) performance of the decoder output and substantially outperforms the T-GaBP optimized with the typical MSE loss function.