# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 1 Introduction  
With the development of wireless communication technology, wireless communication systems are playing more and more important roles in many fields, such as in people's daily life, economy and society. In recent years, traffic on wireless communications system and networks are increasing rapidly. Due to the openness of the wireless communication channel and the complicated electromagnetic environment that wireless communication systems are facing, today's wireless communications systems are more vulnerable to the threats from all kinds of interference, including unintentional interference from other communication systems and malicious jamming from adversaries in the battlefield setting. Therefore, in order to ensure the reliability of wireless communication systems, reliable and robust anti-jamming measures are greatly need. In the past a few decades, many technical measures were proposed to increase the anti-jamming performance of wireless communications systems, which mainly include direct sequence spread spectrum, frequency hopping (FH), time hopping, adopting ultra-wideband signals, and so on. These traditional measures have effectively improved the performance of wireless communication systems [1; 2; 3]. However, with the increasingly complex electromagnetic environment, the aforementioned anti-jamming technology has obvious shortcomings. Some of them cannot even ensure the reliable operation of wireless communication systems anymore. Therefore, enhancing the performance of anti-jammingmeasures and developing new and intelligent anti-jamming methods to improve the adaptability of wireless communication systems is becoming more and more prominent [4]. In recent years, researchers have combined traditional anti-jamming technologies with anti-jamming decision-making techniques, which greatly improved the performance of anti-jamming measures used in wireless communication systems. The authors in [5] proposed a dynamic multi-domain communication anti-jamming decision-making method based on the combination of rough set theory and a genetic algorithm. In the proposed method, the decision-making rules were extracted based on the rough set theory. As a result, the convergence speed of the proposed algorithm has been improved without compromising the performance of the algorithm. In [6, 7], a cognitive anti-jamming fast decision-making engine based on the artificial bee colony algorithm was proposed, which can make effective anti-jamming decisions under different jamming patterns, different jamming power, and different channel settings. However, it is inevitable that the multi-domain decision space is too large. In order to solve this problem, the authors in [8] proposed an anti-jamming decision method based on the artificial bee colony algorithm with a mutation search mechanism, which could compress the decision space before generating the initial population. In [9], the author proposed an anti-jamming decision-making technology based on the deep learning mechanism. By using the pattern classification function of convolutional networks, the high-order feature extraction process for input samples was optimized and the system's performance in recognition decision-making accuracy was improved. The authors in [10] proposed a real-time anti-jamming decision model based on the back-propagation (BP) neural network, which can improve the system's decision-making speed and has the ability of generalization and fault tolerance. Additionally, an intelligent anti-jamming decision method based on the neural network and an optimization algorithm was proposed, which can improve the spectral efficiency and reduce the transmission power for wireless communication systems, while still sustaining the required bit error rate performance. With the continuous development of artificial intelligence technology, machine learning technology represented by reinforcement learning has also begun to be used in the field of communication anti-jamming [11, 12].