# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 1 Introduction
A time-domain anti-pulse jamming algorithm (TDAA) based on reinforcement learning is proposed in [13]; this algorithm has a higher time utilization ratio and normalized throughput. The authors in [14] proposed a reinforcement learning-based mobile offload scheme for edge computing to counter jamming attacks, which has shorter computational delay, lower energy consumption, and higher utilization. To cope with the challenge of infinite state of spectrum waterfall, an anti-jamming deep reinforcement learning algorithm was proposed in [15]. The authors in [16] designed the Double Deep Q Network (Double DQN) to model the confrontation between the cognitive radio network and the jammer; the simulation results indicate that this approach can effectively defend against several kinds of jamming attacks. The aforementioned literature mainly used heuristic algorithms to optimize the decision-making mechanism, such as machine learning and other technologies. These methods can improve the effectiveness and accuracy of the decision-making process for wireless communication anti-jamming to a certain extent, but they still face some challenges: first, those existing methods mainly achieved their optimized decision-making based on a few communication system parameters, such as communication frequency and transmission power. Therefore, the resultant optimizing results may not render the best results when more communication parameters need to be considered. Secondly, for those existing methods, historical experience or existing knowledge of the wireless communication systems and environment are difficult to be integrated into the decision-making process. On the one hand, when faced with known jamming patterns, prior knowledge cannot be used to achieve direct and fast decision-making. On the other hand, for unknown jamming patterns, it is difficult for those existing algorithms to connect the jamming features with existing decision-making knowledge. Therefore, when the jamming pattern changes, the system needs to relearn all the parameters from scratch, which will lead to more computational complexity and slower response. In order to reasonably utilize the historical experience and existing knowledge of anti-jamming, this paper introduces a knowledge graph in communication anti-jamming decision-making. Knowledge graphs, a branch of artificial intelligence technology, are a technique that uses graph models to describe knowledge and establish the relationship model among all the things in the world, which is an issue of great significance to explainable artificial intelligence [17]. Generally speaking, a knowledge graph consists of a set of nodes and edges, where the nodes are used to represent entities or abstract concepts (i.e., knowledge), and the edges are used to represent entity attributes or relationships between these entities [18]. Ever since Google Inc. used a knowledge graph for the first time in 2012 to improve the performance and user experience of their search engines, this technique has attracted extensive research interest from both academia and industry [19; 20]. Different from the way traditional database store knowledge, knowledge graphs use nodes and edges to represent all the knowledge. Therefore, this novel knowledge management technology can effectively represent the relationship among different knowledge while storing the knowledge, which can lead to better and more efficient access to the stored knowledge. With the continuous development of knowledge graph technology, it has been more and more widely used in knowledge searches, intelligent answering systems [21; 22], big data analysis [23], natural language processing [24; 25] and auxiliary device interconnection. This paper introduces the knowledge graph into the field of communication anti-jamming for the first time, and improves the intelligence level of anti-jamming decision-making from the perspective of knowledge utilization.