Name
Abstract | ||
|---|---|---|
Reservoir computing (RC) is a class of neuromorphic computing approaches that deals particularly well with time-series prediction tasks. It significantly reduces the training complexity of recurrent neural networks and is also suitable for hardware implementation whereby device physics are utilized in performing data processing. In this paper, the RC concept is applied to detecting a transmitted s... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TNNLS.2017.2766162 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
OFDM,Training,Channel estimation,Receivers,Wireless communication,Recurrent neural networks,Reservoirs | MIMO-OFDM,Pattern recognition,Computer science,Communication channel,Neuromorphic engineering,Electronic engineering,Reservoir computing,Echo state network,Artificial intelligence,Nonlinear distortion,Orthogonal frequency-division multiplexing,Bit error rate | Journal |
Volume | Issue | ISSN |
29 | 10 | 2162-237X |
Citations | PageRank | References |
7 | 0.46 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Somayeh Mosleh | 1 | 20 | 1.79 |
| Lingjia Liu | 2 | 799 | 92.58 |
| Cenk Sahin | 3 | 120 | 10.30 |
| Yahong Rosa Zheng | 4 | 885 | 76.15 |
| Yang Yi | 5 | 159 | 26.70 |