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Deep Learning Aided Low Complex Breadth-First Tree Search for MIMO Detection
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DOI：10.1109/TWC.2023.3330816.png)
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Abstract
En 中文
In this paper, we propose a deep learning based breadth-first sphere decoding (SD) scheme to reduce the detection complexity for multiple-input multiple-output (MIMO) communication systems. Specifically, we first design the DenseNet-based deep neural network (DN-DNN) to provide the pruning threshold for SD at each layer. Then, we develop modified number-based SD (MNSD) to reduce the complexity of SD by constraining the number of visited nodes at each layer with the output of DN-DNN. We use a distance-based SD (DSD) to further reduce the complexity of MNSD by constraining the accumulated distance at each layer with the output of DN-DNN. Compared with the traditional M-best SD with $M=16$ , the proposed MNSD achieves similar performance but reduces about 25% complexity for QPSK modulation; the proposed DSD has better performance with up to 75% complexity reduction at the high SNR region for 16QAM.
Keywords:
Complexity theory
MIMO communication
Detectors
Artificial neural networks
Modulation
Wireless communication
Deep learning
MIMO detection
deep learning
sphere decoding
deep neural network
Journal
IF:
10.7
Papers:
1.3W
Citations:
5.3W
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