In this paper, we investigate the model-driven deep learning (DL) for MIMO detection. In particular, the MIMO detector is specially designed by unfolding an iterative algorithm and adding some trainable parameters. Since the number of trainable parameters is much fewer than the data-driven DL based signal detector, the model-driven DL based MIMO detector can be rapidly trained with a much smaller data set. The proposed MIMO detector can be extended to soft-input soft-output detection easily. Furthermore, we investigate joint MIMO channel estimation and signal detection (JCESD), where the detector takes channel estimation error and channel statistics into consideration while channel estimation is refined by detected data and considers the detection error. Based on numerical results, the model-driven DL based MIMO detector significantly improves the performance of corresponding traditional iterative detector, outperforms other DL-based MIMO detectors and exhibits superior robustness to various mismatches.
翻译:在本文中,我们调查了MIMO探测的模型驱动深度学习(DL) 。 特别是, MIMO 探测器是专门通过开发一种迭代算法和添加一些可训练参数而设计的。 由于可训练参数的数量远远少于数据驱动的DL基信号探测器, 模型驱动的DL 以MIMO 探测器可以用小得多的数据集进行快速培训。 拟议的MIMO 探测器可以很容易地扩大到软投入软产出软产出探测。 此外, 我们调查了MIMO频道联合估计和信号探测(JCESD ), 该探测器使用频道估计错误和频道统计来考虑,而频道估计则通过检测的数据加以完善并考虑探测错误。 根据数字结果, 模型驱动的DL IMO 探测器大大改进了相应的传统迭代探测器的性能, 超越了其他 DL 基 MIMO 探测器, 并显示对各种不匹配的超强性能 。