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A Model-Driven Deep Learning Method for Massive MIMO Detection
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DOI：10.1109/LCOMM.2020.2989672.png)
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Abstract
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In this letter, an efficient massive multiple-input multiple-output (MIMO) detector is proposed by employing a deep neural network (DNN). Specifically, we first unfold an existing iterative detection algorithm into the DNN structure, such that the detection task can be implemented by deep learning (DL) approach. We then introduce two auxiliary parameters at each layer to better cancel multiuser interference (MUI). The first parameter is to generate the residual error vector while the second one is to adjust the relationship among previous layers. We further design the training procedure to optimize the auxiliary parameters with pre-processed inputs. The so derived MIMO detector falls into the category of model-driven DL. The simulation results show that the proposed MIMO detector can achieve preferable detection performance compared to the existing detectors for massive MIMO systems.
Keywords:
MIMO communication
Detectors
Mathematical model
Training
Complexity theory
Machine learning
Neural networks
Massive MIMO
MIMO detection
deep learning
model-driven
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