Name
Abstract | ||
|---|---|---|
Signal detection using deep learning is a challenging and promising research topic. Several learning-based signal detectors have been proposed to produce significant results. However, most of them have ignored interference in their designs. In this paper, we evaluate the performance of learning-based signal detectors in the presence of co-channel interference under different channel conditions. Specifically, fully connected deep neural network (FCDNN) and convolutional neural network (CNN) are examined as the data-driven signal detector for blind signal detection without knowledge of the channel state information. Several important system parameters, including signal-to-interference ratio, number of interferences and type of interference, are considered. Numerical results show that FCDNN and CNN-based detectors have better performance and robustness to different SIRs conditions than traditional detectors in the presence of interference and FCDNN performs better than CNN when SIR is small and the order of interference modulation is high. (C) 2021 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.phycom.2021.101343 | PHYSICAL COMMUNICATION |
Keywords | DocType | Volume |
Co-channel interference, Deep learning, Neural network, Signal detection | Journal | 47 |
ISSN | Citations | PageRank |
1874-4907 | 1 | 0.35 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chenguang Liu | 1 | 1 | 0.35 |
| Yunfei Chen | 2 | 117 | 45.25 |
| Shuang-Hua Yang | 3 | 32 | 7.35 |