Cooperative Self-Learning: A Framework for Few-Shot Jamming Identification

Jamming identification is the key objective behind effective anti-jamming methods. Due to the requirement of low-complexity and the limited number of labeled shots for real jamming identification, it is highly challenging to identify jamming patterns with high accuracy. To this end, we first propose a general framework of cooperative jamming identification among multiple nodes. Moreover, we further propose a novel fusion center (FC) aided self-learning scheme, which uses the guidance of the FC to improve the effectiveness of the identification. Simulation results show that the proposed framework of the cooperative jamming identification can significantly enhance the average accuracy with low-complexity. It is also demonstrated that the proposed FC aided self-learning scheme has the superior average accuracy compared with other identification schemes, which is very effective especially in the few labeled jamming shots scenarios.