# A New Improved Multi-Sequence Frequency-Hopping Communication Anti-Jamming System
## 1 Introduction
In more complex jamming environments, where fast and intelligent reactive jamming is encountered, this avoidance-based approach struggles to achieve effective anti-jamming communication. In the context of complex jamming environments characterized by fast-reactive jamming, reference [15; 16; 17] has already explored novel approaches that exploit jamming rather than merely evading it. In [15], the authors introduce an anti-jamming scheme based on wireless energy harvesting, referred to as Multisc IA and OIA, which optimizes transmission rates by harnessing energy from jamming signals to aid communication. Furthermore, reference [16] proposes an anti-jamming strategy that combines neural network architectures with environmental backscatter communication techniques. The core of this strategy involves reflecting data sent through jamming signals back to the receiver, while simultaneously harvesting energy from these jamming signals to support communication needs. Additionally, building on [16], reference [17] introduces an intelligent deception strategy that actively transmits false signals to induce jamming, while also harvesting energy from these signals, thereby significantly improving the efficiency of jamming utilization. However, the methods proposed in [15; 16; 17] experience relatively low jamming utilization efficiency due to channel losses in wireless transmission, and the deep learning-based detector introduced in [17] requires high-quality training data. To enhance jamming utilization efficiency, ref. [18] proposes a method that uses jamming signals to transmit user information, employing user signals as a stimulus for guiding the decision on jamming signals. This method, however, relies on the separation of signal source estimation and is only applicable when user signals and jamming signals are uncorrelated. While [19] can address certain fast and complex reactive jamming, in highly complex jamming environments, the bit error rate increases, adversely affecting anti-jamming performance. To ensure the universal applicability of jamming utilization methods, increase the efficiency of jamming utilization, and enhance the resilience of communication systems, further in-depth research is required. This system adopts a neural network-based approach to learn the rule of reactive jamming and utilize it. Specifically, we design an Intelligent Multi-Sequence Frequency Hopping (IMSFH) communication framework, which consists of two stages: the training stage and the communication stage. In the training stage, the transmitter sends a fixed, known frequency-hopping signal, learning the variations in reactive jamming signals as they adapt to changes in user signals. This enables the capture of the dynamic characteristics of reactive jamming. During the communication stage, the extracted reactive jamming patterns are utilized, combined with the neural network model, to enhance frequency-detection performance. Simulation results indicate that, compared to existing methods, this approach can effectively utilize reactive jamming signals in complex jamming environments, thereby improving communication performance. To highlight the distinctions between our work and previous studies, we summarize the primary contributions of this paper as follows:  
* We optimize an intelligent learning-based jamming exploitation method, allowing for the preferable utilization of reactive jamming signals in complex jamming environments. This method improves model performance by optimizing the neural network, enabling better extraction of correlations between reactive jamming signals and user signals. This correlation is then leveraged to assist users in frequency detection.