# A New Improved Multi-Sequence Frequency-Hopping Communication Anti-Jamming System
## 1 Introduction  
Wireless communication's openness and shared nature provide users with easy access and connectivity benefits. Nevertheless, these advantages also expose communication networks to the wireless environment, making them more susceptible to jamming attacks compared to wired networks [1]. Jamming attacks represent a significant threat to wireless communication security, primarily through the emission of electromagnetic signals that disrupt and degrade the reception of user signals, severely affecting communication performance [2]. Particularly with the integration of communication countermeasures and artificial intelligence, wireless jamming techniques and capabilities have evolved. They have progressed from traditional conventional jamming to cognitive jamming, which includes environmental awareness and learning abilities. Eventually, they developed into intelligent jamming, capable of perception, learning, reasoning, and decision-making [3]. For instance, malicious attackers can detect and analyze legitimate users' signal waveforms, using a Universal Software Radio Peripheral (USRP) device to transmit targeted jamming. Facing complex external jamming environments and evolving jamming patterns, users find it challenging to combat these new threats. Simultaneously, as technologies like unmanned aerial vehicles (UAVs), 5G cellular networks, and vehicle-to-everything communications (V2X) become widespread in daily life, the cost of building intelligent jamming devices has significantly decreased. This development makes the jamming environment increasingly complex. Therefore, to ensure the security of wireless communications, this paper studies how to counteract intelligent reactive jamming in more challenging environments. Traditional anti-jamming techniques such as adaptive technologies [4], frequency hopping [5], and antenna mode switching [6] have been widely applied in wireless communication. In traditional frequency hopping (FH) communication systems, such as FH/BPSK, user data are initially modulated using Binary Frequency Shift Keying (BPSK), and then transmitted over frequency channels selected pseudorandomly based on a predefined hopping pattern. However, the performance of FH/BPSK systems significantly declines when facing intelligent reactive jamming [7]. Traditional anti-jamming methods lack the learning ability necessary to adapt to the dynamic and complex spectrum environments introduced by intelligent jamming [8]. To address this issue, reference [9; 10] introduces game theory to simulate the adversarial relationship between legitimate users and malicious jammers. However, these studies typically assume that legitimate users can obtain the jammer's Channel State Information (CSI), which is unrealistic in adversarial environments. Considering the challenges in acquiring intelligence on the states of intelligent jammers, Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) [11; 12; 13; 14] have been introduced to address the issue of anti-intelligent jamming. In [12], the authors propose a rapid anti-jamming algorithm based on generating similarity samples using Deep Reinforcement Learning. Reference [13] introduces a Sequential Deep Reinforcement Learning Algorithm (SDRLA) that operates without prior information, utilizing a spectral waterfall as input. In reference [14], a method based on Deep Double Q-learning is proposed to learn an effective communication strategy. Although these studies are capable of effectively avoiding slow-reactive jamming, they exhibit certain limitations when dealing with fast-reactive jamming. Fast-reactive jamming can change channels almost synchronously with the user, thus diminishing the effectiveness of these algorithms against such rapid changes. Although the aforementioned methods address reactive jamming through intelligent learning, they remain passive anti-jamming strategies.