  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.
