Few-Shot Intelligent Anti-Jamming Access with Fast Convergence: A GAN-Enhanced Deep Reinforcement Learning Approach  
[PERSON]  
1,2,2, [PERSON]  
spread spectrum, including technologies such as frequency/power-adaptive frequency hopping and direct-sequence spread spectrum based on narrowband jamming adaptive suppression/power adaptation [3]. Although these adaptive anti-jamming technologies introduce closed-loop parameter adjustment, they are still limited by the fixed strategies of pre-set rule bases and have difficulty coping with rapidly time-varying jamming threats. This limitation has prompted researchers to explore higher-level anti-jamming intelligent technologies. In recent years, with the rapid development of artificial intelligence and machine learning, intelligent communication anti-jamming has become a current research hotspot. Artificial intelligence algorithms represented by DRL provide new methods for intelligent anti-jamming technologies by virtue of their iterative learning ability of \"perception-decision optimization\". At present, DRL-based communication anti-jamming technologies widely adopt the DQN and its improved methods to solve complex communication anti-jamming problems. [PERSON] et al. [4] proposed a dynamic \(\epsilon\)-DQN intelligent decision algorithm that can optimally select \(\epsilon\) values according to the state of the decision network, significantly improving the convergence speed and decision success rate. [PERSON] et al. [5] presented a cross-domain anti-jamming algorithm based on the DQN, enabling mobile nodes to learn optimal position adjustment and power control strategies in unknown dynamic-jamming environments to achieve reliable transmission. [PERSON] et al. [6] proposed a joint frequency-power domain anti-jamming algorithm based on the Prioritized Experience Replay Deep Q-Network, which combines the DQN method with the prioritized replay strategy to realize channel selection and power control in wireless communication systems, improve the fault tolerance of wireless communication systems, and effectively enhance their transmission success rate. [PERSON] et al. [7] modeled the communication anti-jamming process in a limited channel state as a partially observable Markov process and proposed a deep recurrent Q-network algorithm suitable for this scenario, whose convergence performance is significantly better than that of the ordinary DQN algorithm. [PERSON] et al. [8] proposed a practical and fast-converging DRL anti-jamming algorithm that uses soft labels instead of rewards to obtain more information entropy, and which does not require random exploration, significantly accelerating the algorithm convergence speed. [PERSON] et al. [9] proposed a parallel DRL method, which decomposes complex action spaces through a parallel network architecture, significantly improving the convergence speed and the effectiveness of anti-jamming strategies. [PERSON] et al. [10] proposed a sample information entropy-assisted DRL anti-jamming method, which uses information entropy to predict and finely select training samples for the anti-jamming strategy network so as to improve the quality of training samples and enhance the online decision-making ability and generalization performance of anti-jamming strategies. In summary, although AI algorithms markedly enhance anti-jamming efficacy by constructing a three-tier \"environmental cognition-decision reasoning-waveform reconstruction\" architecture, the existing DRL approaches are still confronted by three fundamental challenges in contested communications:  
1. Small-sample training bottleneck: In complex electromagnetic environments, jamming patterns are typically unknown and scarce, causing conventional DQN to suffer from insufficient samples and slow convergence during training [1]. 2. Limited convergence efficiency: In high-dimensional action-state spaces, the exploration -exploitation trade-off is easily destabilized and traditional policies require lengthy exploration steps before discovering optimal strategies.