## 1 Introduction  
Wireless communication technologies have been widely applied in many fields over the past two decades and have become one of the cornerstones of modern military information systems [1, 2]. Improving anti-jamming capabilities in communications can enhance the reliability and security of military communications, especially in complex electronic warfare environments. For unmanned aerial vehicles (UAVs) and remote control systems, enhanced communication stability is crucial. Autonomous vehicles and intelligent transportation systems rely on reliable communication to prevent accidents, and improving anti-jamming performance can enhance the reliability of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Enhancing anti-jamming abilities can maintain higher operational stability of networks in the face of natural disasters or human attacks. At personal and enterprise levels, improved communication anti-jammingcapabilities can reduce the risk of private information leakage. In remote or complex environments, more reliable communication can provide better connectivity and service quality. In summary, enhancing communication anti-jamming capabilities benefits not only specific industries but also has a profound impact on the security and efficiency of the entire wireless communication field. However, due to its open channel and application environments, wireless communication systems are vulnerable to all kinds of jamming, especially malicious jamming from electronic warfare attacks. And those jamming and attacks have already become one of the primary factors affecting the reliability and efficiency of wireless communication systems [3]. As a result, the issue of communication anti-jamming has become a hot topic in the field of wireless communication technology research. Traditional anti-jamming techniques that have been used in wireless communication systems are mostly based on signal spread spectrum technologies, combined with all kinds of error-correcting coding and signal power/frequency adapting techniques. These methods can be effectively used to counter conventional jamming such as single-tone, multi-tone, and partial-band jamming [4]. However, with the development of machine learning, research on intelligent jamming has gradually deepened. In the confrontation with the communication party, intelligent jamming accumulates \"jamming knowledge\" through experience or exploratory learning, and makes jamming decisions to dynamically adapt to the local changes in the electromagnetic environment. In order to enhance its own benefits, intelligent jamming has achieved a strategic closed loop of \"perception-learning-prediction-decision-feedback.\" Intelligent jamming not only includes the innovation of jamming styles, but also the comprehensive, flexible, and efficient application of basic jamming styles, that is, according to the actual needs of confrontation, based on reasoning, prediction, and decision-making capabilities, comprehensively and flexibly using aiming or blocking interference methods to block or weaken the effective communication of the communication party. It can be seen that traditional anti-jamming measures can no longer solve intelligent jamming with high dynamic characteristics, and various intelligent jamming empowered by artificial intelligence technology urgently needs effective countermeasures. In [5], a deep reinforcement learning-based routing algorithm was proposed to tackle the anti-jamming communication challenges in the heterogeneous Internet of Satellite (IoS), which operates in a highly dynamic communication environment with potential intelligent jamming. The proposed algorithm can obtain a usable subset of routes for the traffic in IoS with superior anti-jamming performance and can lower the routing costs. In addition, the proposed anti-jamming strategy can converge to a Stackelberg equilibrium.