Aiming at efficiently establishing the optimal transmission path in a wireless communication network in a malicious jamming environment, this paper proposes an anti-jamming algorithm based on Dyna-Q. Based on previous observations of the environment, the algorithm selects the optimal sequential node by searching the Q table to reduce the packet loss rate. The algorithm can accelerate the updating of the Q table based on previous experience. The Q table converges to the optimal value quickly. This is beneficial for the optimal selection of subsequent nodes. Simulation results show that the proposed algorithm has the advantage of faster convergence speed compared with the model-free reinforcement learning algorithm.

Aiming at the existing intelligent anti-jamming communication methods that fail to consider the problem that sensing is inaccurate, this paper puts forward an intelligent anti-jamming method for wireless communication under non-ideal spectrum sensing (NISS). Under the malicious jamming environment, the wireless communication system uses Q-learning (QL) to learn the change law of jamming, and considers the false alarm and missed detection probability of jamming sensing, and selects the channel with long-term optimal reporting in each time slot for communication. The simulation results show that under linear sweep jamming and intelligent blocking jamming, the proposed algorithm converges faster than QL with the same decision accuracy. Compared with wide-band spectrum sensing (WBSS), an algorithm which failed to consider non-ideal spectrum sensing, the decision accuracy of the proposed algorithm is higher with the same convergence rate.

Aiming at the problem of mutual interference between nodes and external malicious jamming in wireless communication networks, this paper proposes an intelligent communication anti-jamming algorithm based on distributed double Q-learning. First of all, the proposed algorithm anti-jamming problem is modeled as a time-frequency two-dimensional optimization problem. The decoupled relationship between subframe and channel is used. Each node uses two Q-learning. According to the automatically received feedback signal, users choose their subframe and channel to avoid malicious external jamming and mutual interference between users. It maximizes the sum of all user throughput. Simulation results show that the proposed algorithm can effectively shorten the convergence time and improve the performance of the system.