The identification of malicious jamming pattern is significant for wireless communication system to adopt targeted anti-jamming approaches. Efficient identification of malicious jamming patterns requires sufficient sample data and computing resources for the complex electromagnetic environment. However, with the use of portable communication equipment and emergency communications, it is difficult to ensure adequate sample data and computing resources. Against this background, this letter focuses on the jamming identification based on a small sample data-driven Naive Bayes classifier. The main idea is to obtain approximate conditional feature probability via data augment and kernel density estimation (KDE) from a small sample labelled data and construct Naive Bayes classifier, so as to form a fast jamming recognition method. Simulation results show that compared with the methods using decision tree or simple neutral network, the proposed scheme achieves a better average accuracy between six jamming patterns, as well as low-complexity.

The novel technology based on reinforcement learning (RL) is considered as a promising direction to achieve cognitive and even intelligent anti-jamming communication. However, traditional RL converges slowly in the face of huge state-action space, while the performance improvement of deep RL depends on high-cost computing resources. Under unknown and dynamic malicious jamming attacks, it is still a major challenge to realize online anti-jamming communication through fast and autonomous learning. In this paper, we propose a fast anti-jamming scheme based on intra-domain knowledge reuse for distributed wireless networks. First, we introduce the Bisimulation Relation to measure the similarity of different state-action pairs in the communication anti-jamming problem, so as to build a bridge of knowledge reuse between state-action pairs. This similarity stems from the fact that the anti-jamming problem can be divided into two categories according to whether it successfully resists jamming attacks, in which different state-action pairs in the same category have similarities. Then, through the real-time update and reuse of state-action values, each node can independently learn the characteristics of dynamic environment and adjust the transmission strategy quickly, so as to avoid external malicious jamming and internal mutual interference. The simulation results show that our proposed scheme significantly accelerates the convergence speed and improves the network normalized throughput compared with the benchmark against unknown and dynamic jamming, and has remarkable agility and jamming memory.

Pulse jamming is one of the common malicious jamming patterns that can significantly reduce the of wireless communication’s reliability. This paper investigates the problem of anti-jamming communication in a random pulse jamming environment. In order to obtain the countermeasure in time domain, the Markov decision process (MDP) is employed to model and analyze the above problem, and a time-domain anti-pulse jamming algorithm (TDAA) based on reinforcement learning is proposed. The proposed algorithm learns from the dynamic interaction with the jamming environment to gradually approximate the optimal time-domain strategy. The optimal strategy enables the transmitter to switch between two states, i.e. "active" and "silent", to avoid random pulse jamming. In addition, a state estimation and adjustment method for the random pulse jamming environment is introduced to improve the robustness of the proposed TDAA. Simulation results show that, compared with continuous transmission, the proposed TDAA can effectively reduce the jamming collision ratio and significantly improve the normalized throughput. And compared with transmitting terminal Q-learning algorithm (TTQA), the proposed TDAA has higher time utilization ratio and normalized throughput.

Herein, a comprehensive review of the evolution of intelligent communication anti‐jamming techniques is provided. First, a clear definition of the concept and elaboration on the inherent connotations and capability characteristics of intelligent communication anti‐jamming is provided. Additionally, the initial construction of an intelligent communication anti‐jamming system architecture is outlined. Subsequently, the development of intelligent communication anti‐jamming is delved, tracing its progression from early‐stage adaptive anti‐jamming techniques to the more recent advancements in intelligent anti‐jamming, which are primarily based on game theory and machine learning. Moreover, the latest research findings in this domain are thoroughly examined and the existing challenges and bottlenecks that hinder current research progress are highlighted. Finally, several viable research directions for future studies in the field of intelligent anti‐jamming are proposed. The objective is to facilitate readers in gaining a comprehensive understanding of the concept, characteristics, and system architecture of intelligent communication anti‐jamming. Additionally, it is aimed to provide a comprehensive overview of the developmental process and current state of research, thereby furnishing conceptual and theoretical support for the design of more effective and practical intelligent communication anti‐jamming systems.

This paper investigates the cross-layer optimization for anti-jamming in the network and MAC layers, in which the jammer can adjust the jamming policies to maximize the jamming effectiveness. The joint problem of routing selection, channel allocation, and power control is formulated as a Stackelberg game. The jammer leads the game by choosing the optimal jamming power and channels. The user follows by selecting the optimal nodes and corresponding channels, and adjusts its transmitting power to meet the communication requirement. Then, based on Q-learning, a cross-layer anti-jamming learning algorithm is proposed to obtain the Stackelberg equilibrium. Finally, simulation results are presented to verify the effectiveness of the proposed algorithm.

Deep reinforcement learning has been widely applied to solve the anti-jamming problems in wireless communications, achieving good results. However, most research assumes that the communication system can obtain complete Channel State Information (CSI). Under limited CSI conditions, this paper models the system using Partially Observable Markov Decision Processes (POMDPs). In addition, it is challenging to determine the optimal exploration rate decay factor for decision algorithms using exponential decay exploration rate. This paper proposes an exploration rate decay factor automatic adjustment algorithm. Additionally, a Deep Recurrent Q-Network (DRQN) algorithm architecture suitable for the scenario is designed, along with an intelligent anti-jamming decision algorithm. The algorithm first uses Long Short-Term Memory (LSTM) networks to learn the temporal features of input data, flattens the features, and then feeds the result into fully connected layers to get the intelligent anti-jamming strategy. Simulation results demonstrate that the exploration rate decay factor automatic adjustment algorithm can achieve nearly optimal performance when set with a large initial exploration rate decay factor. Under periodic jamming and intelligent blocking jamming, the proposed algorithm reduces the number of time slots required for convergence by 45% and 32% compared to the performance-optimal Double DQN (DDQN) algorithm in comparison algorithms. The normalized throughput after convergence is slightly higher, and the convergence performance is significantly better than that of Deep Q-Network (DQN) and Q-Learning (QL).