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Understanding Deep MIMO Detection
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DOI：10.1109/TWC.2023.3272525.png)
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摘要
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Incorporating deep learning (DL) into multiple-input multiple-output (MIMO) detection has been deemed as a promising technique for future wireless communications. However, most of the DL-based MIMO detection algorithms are lack of interpretation on internal mechanisms. In this paper, we analyze the performance of the DL-based MIMO detection to better understand its strengths and weaknesses. We investigate and compare two different models: data-driven DL detector with neural networks activated by rectifier linear unit (ReLU) function and model-driven DL detector based on traditional detection algorithms. We show that the data-driven DL detector asymptotically approaches to the maximum a posterior (MAP) detector in various scenarios but requires a large amount of training samples to converge in time-varying channels. On the other hand, the model-driven DL detector utilizes the expert knowledge to alleviate the impact of channels and achieves relatively high detection accuracy with a small set of training data. Simulation results confirm our analytical results and demonstrate the effectiveness of the DL-based MIMO detection for both linear and nonlinear signal systems.
Keyword:
Detectors
MIMO communication
Detection algorithms
Wireless communication
Symbols
Training
Iterative algorithms
Explainable deep learning
MIMO detection
deep neural network
model-driven
期刊
IF:
10.7
论文数:
1.3W
被引数:
5.3W
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