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Sparsely Connected Neural Network for Massive MIMO Detection

EasyChair Preprint no. 376

6 pagesDate: July 24, 2018


Deeping learning can achieve high parallelism and robustness, which is especially suitable for massive multiple-input multiple-output (MIMO) detection. There are already some well-developed deep learning models applied to MIMO detection, in which detection network is a typical representative model with excellent performance, but its complexity is high. This paper aims to simplify the detection network model, and the simplification runs through the entire data processing. This simplification includes three improvements. First, the number of inputs is reduced to simplify inputs; Second, the network connection structure is simplified by changing network from full connectivity to sparsely connectivity and reducing the number of network layers by half. Third, the loss function optimizes to avoid irreversible problems with the matrix. Base on the above improvements, the complexity of the network is reduced from O(64n2) to O(3n). The simulation results indicates that the proposed structure has better performance than the existing detection network.

Keyphrases: deep learning, MIMO detection, Sparsely Connected Neural Network

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Guili Gao and Chao Dong and Kai Niu},
  title = {Sparsely Connected Neural Network for Massive MIMO Detection},
  howpublished = {EasyChair Preprint no. 376},
  doi = {10.29007/rjdc},
  year = {EasyChair, 2018}}
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