| Title | Performance of Deep Learning for Multiple Antennas Physical Layer Network Coding |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Liu, Jinghua, Chen, Pingping, Chen, Feng |
| Conference Name | 2021 15th International Symposium on Medical Information and Communication Technology (ISMICT) |
| Keywords | composability, Cyber-physical systems, Deep Learning, deep neural networks (DNNs), MIMO, network coding, Neural networks, physical layer network coding (PNC), Predictive Metrics, pubcrawl, Receivers, Resiliency, simulation, statistical distributions, Training, two way relay channel (TWRC) |
| Abstract | In this paper, we propose a deep learning based detection for multiple input multiple output (MIMO) physical-layer network coding (DeepPNC) over two way relay channels (TWRC). In MIMO-PNC, the relay node receives the signals superimposed from the two end nodes. The relay node aims to obtain the network-coded (NC) form of the two end nodes' signals. By training suitable deep neural networks (DNNs) with a limited set of training samples. DeepPNC can extract the NC symbols from the superimposed signals received while the output of each layer in DNNs converges. Compared with the traditional detection algorithms, DeepPNC has higher mapping accuracy and does not require channel information. The simulation results show that the DNNs based DeepPNC can achieve significant gain over the DeepNC scheme and the other traditional schemes, especially when the channel matrix changes unexpectedly. |
| DOI | 10.1109/ISMICT51748.2021.9434923 |
| Citation Key | liu_performance_2021 |
Performance of Deep Learning for Multiple Antennas Physical Layer Network Coding