Name
Abstract | ||
|---|---|---|
Effective recognition of communication jamming is of vital importance in improving wireless communication system’s anti-jamming capability. Motivated by the major challenges that the jamming data sets in wireless communication system are often small and the recognition performance may be poor, we introduce a novel jamming recognition method based on distributed few-shot learning in this paper. Our proposed method employs a distributed recognition architecture to achieve the global optimization of multiple sub-networks by federated learning. It also introduces a dense block structure in the sub-network structure to improve network information flow by the feature multiplexing and configuration bypass to improve resistance to over-fitting. Our key idea is to first obtain the time-frequency diagram, fractional Fourier transform and constellation diagram of the communication jamming signal as the model-agnostic meta-learning network input, and then train the distributed network through federated learning for jamming recognition. Simulation results show that our proposed method leads to excellent recognition performance with a small data set. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/JSTSP.2021.3137028 | IEEE Journal of Selected Topics in Signal Processing |
Keywords | DocType | Volume |
Federated learning,few-shot learning,jamming recognition,model-agnostic meta-learning | Journal | 16 |
Issue | ISSN | Citations |
3 | 1932-4553 | 0 |
PageRank | References | Authors |
0.34 | 19 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingqian Liu | 1 | 0 | 0.34 |
| Zilong Liu | 2 | 0 | 0.34 |
| Weidang Lu | 3 | 309 | 55.86 |
| Yunfei Chen | 4 | 117 | 45.25 |
| Xiaoteng Gao | 5 | 0 | 0.34 |
| Nan Zhao | 6 | 1591 | 123.85 |