Xiao Han
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Xiao Han has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Fan Zhou
Shenyang Ligong University
1 shared publication - Jinyang Ren
School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China
1 shared publication - Peiying Zhang
China University of Petroleum (East China)
1 shared publication - Shaolin Liao
School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, Guangdong, China
1 shared publication - Wei Wang
National Key Laboratory of Electromagnetic Space Security, Jiaxing 314000, China
1 shared publication - Yang Wang
School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China
1 shared publication
Latest Publications
- Open Access
A Method for Few-Shot Modulation Recognition Based on Reinforcement Metric Meta-Learning
In response to the problem where neural network models fail to fully learn signal sample features due to an insufficient number of signal samples, leading to a decrease in the model’s ability to recognize signal modulation methods, a few-shot signal modulation mode recognition method based on reinforcement metric meta-learning (RMML) is proposed. This approach, grounded in meta-learning techniques, employs transfer learning to building a feature extraction network that effectively extracts the data features under few-shot conditions. Building on this, by integrating the measurement of features of similar samples and the differences between features of different classes of samples, the metric network’s target loss function is optimized, thereby improving the network’s ability to distinguish between features of different modulation methods. The experimental results demonstrate that this method exhibits a good performance in processing new class signals that have not been previously trained. Under the condition of 5-way 5-shot, when the signal-to-noise ratio (SNR) is 0 dB, this method can achieve an average recognition accuracy of 91.8%, which is 2.8% higher than that of the best-performing baseline method, whereas when the SNR is 18 dB, the model’s average recognition accuracy significantly improves to 98.5%.
Open AccessA Method for Few-Shot Modulation Recognition Based on Reinforcement Metric Meta-Learning
In response to the problem where neural network models fail to fully learn signal sample features due to an insufficient number of signal samples, leading to a decrease in the model’s ability to recognize signal modulation methods, a few-shot signal modulation mode recognition method based on reinforcement metric meta-learning (RMML) is proposed. This approach, grounded in meta-learning techniques, employs transfer learning to building a feature extraction network that effectively extracts the data features under few-shot conditions. Building on this, by integrating the measurement of features of similar samples and the differences between features of different classes of samples, the metric network’s target loss function is optimized, thereby improving the network’s ability to distinguish between features of different modulation methods. The experimental results demonstrate that this method exhibits a good performance in processing new class signals that have not been previously trained. Under the condition of 5-way 5-shot, when the signal-to-noise ratio (SNR) is 0 dB, this method can achieve an average recognition accuracy of 91.8%, which is 2.8% higher than that of the best-performing baseline method, whereas when the SNR is 18 dB, the model’s average recognition accuracy significantly improves to 98.5%.