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Mine MIMO Depth Receiver: An Intelligent Receiving Model Based on Densely Connected Convolutional Networks
| Content Provider | MDPI |
|---|---|
| Author | Wang, Mingbo Wang, Anyi Liu, Zhaoyang Zhang, Heng Chai, Jing |
| Copyright Year | 2021 |
| Description | Multiple-input multiple-output (MIMO) systems suffer from high BER in the mining environment. In this paper, the mine MIMO depth receiver model is proposed. The model uses densely connected convolutional networks for feature extraction and constructs multiple binary classifiers to recover the original information. Compared with conventional MIMO receivers, the model has no error accumulation caused by processes such as decoding and demodulation. The experimental results show that the model has better performance than conventional decoding methods under different modulation codes and variations in the number of transmitting terminals. Furthermore, we demonstrate that the model can still achieve effective decoding and recover the original information with some data loss at the receiver. |
| Starting Page | 8326 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s21248326 |
| Journal | Sensors |
| Issue Number | 24 |
| Volume Number | 21 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-12-13 |
| Access Restriction | Open |
| Subject Keyword | Sensors Information and Library Science Multiple-input Multiple-output System Mine Mine Mimo Depth Receiver Densely Connected Convolutional Networks |
| Content Type | Text |
| Resource Type | Article |
