We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
基于协同学习的频谱智能感知方法.
- Authors
潘成胜; 蔡韧; 石怀峰; 施建锋; 王钰玥
- Abstract
The spatial and temporal distribution of current heterogeneous network spectrum environment is complex and variable, the data preprocessing of existing multi-user cooperative sensing methods is cumbersome, and the sensing efficiency is low. For above problems, a cooperative learning-based spectrum intelligent sensing algorithm is proposed under a system architecture consisting of user sensing layer and edge fusion layer. The user-aware layer uses a multi-branch convolutional recurrent gated neural network to realize local sensing by using the underlying structural information of the original normalized energy The signal. edge fusion layer performs message propagation based on a self-attention mechanism and fuses the sensing results of each unauthorized user in the user-aware layer to arrive at the final decision. Experiments show that when the signal-to-noise ratio is -20 dB and five users are sensing cooperatively, the proposed method is able to achieve a detection probability of 18. 3% at a false alarm probability of 1. 91%, an improvement of 6. 1% compared with the comparison model, and does not require additional pre-processing of the raw data, thus reducing the complexity of the algorithm.
- Subjects
CONVOLUTIONAL neural networks; COLLABORATIVE learning
- Publication
Telecommunication Engineering, 2023, Vol 63, Issue 12, p1839
- ISSN
1001-893X
- Publication type
Article
- DOI
10.20079/j.issn.1001-893x.220721005