The Research of Multi-Node Collaborative Compound Jamming Recognition Algorithm Based on Model-Agnostic Meta-Learning and Time-Frequency Analysis

Abstract

Deep learning has presented its spectacular potential in the jamming recognition field. Yet, sufficient samples required by normal deep learning analysis methods are not always available, especially in the 6G communication field. This situation appears to be more challenging in the communication field. In this article, Model-Agnostic Meta-Learning (MAML) is imported into the jamming recognition field in order to accomplish compound jamming recognition in the circumstances of few-shot learning. Further, the existing research on jamming recognition techniques is mostly based on single-node recognition. This technique cannot make full and efficient use of the jamming information collected. Therefore, this article adds a multi-node collaborative technique into the compound jamming recognition algorithm that is based on MAML and time-frequency analysis. Based on the fact that each cognitive node can recognize independently, the recognition results are be sent to the fusion center. The fusion center completes the fusion of the recognition results according to the majority rule. The experiments demonstrate that, with the fusion of the multi-node collaborative technique, the precision of compound jamming recognition in the condition of few-shot learning has been effectively improved.