Distributed Anti-Jamming Strategy Based on Local Knowledge Diffusion and Differential Weighted Fusion Mechanisms

In complex jamming environments with multi-user spectrum sharing, existing distributed anti-jamming strategies are constrained by significant communication overhead, limited efficiency in knowledge dissemination, and low collaborative effectiveness. To address these challenges, a distributed anti-jamming strategy based on local knowledge diffusion and differential weighted fusion mechanisms (LKD-DWF-M) is proposed. In this strategy, a local knowledge diffusion mechanism is introduced to facilitate knowledge sharing among communication nodes, enabling each node to gain a comprehensive understanding of its neighbors’ behavior. Subsequently, a knowledge contribution measurement method based on mutual information is proposed, and a differential weighted fusion (DWF) mechanism is designed to effectively integrate the policy and value parameters of neighboring nodes. This integration enables accurate global value estimation while optimizing individual anti-jamming strategies. Additionally, the existence of the Nash equilibrium (NE) for each node’s policy and value parameters is theoretically established using Kakutani’s fixed-point theorem. Furthermore, through the construction of a Lyapunov function, it is demonstrated that the proposed strategy can stabilize and converge to the NE in the long-term jamming counteraction process. Simulation results indicate that, in comparison to anti-jamming strategies employing global knowledge diffusion and differential weighted fusion mechanism (GKD-DWF-M), global knowledge diffusion and average fusion (GKD-AF-M), and local knowledge diffusion and average fusion (LKD-AF-M), the proposed distributed anti-jamming strategy achieves respective improvements of 4%, 17%, and 20% in system normalized throughput under statistical jamming (SJ). Under dynamic sweeping jamming (DSJ), the system normalized throughput improves by 8%, 11%, and 11.5%, respectively; under intelligent comb jamming (ICJ), it increases by 10%, 10.5%, and 19%, respectively; and under intelligent block jamming (IBJ), it increases by 5%, 16%, and 21%, respectively. Moreover, the proposed strategy exhibits superior convergence speed compared to other strategies. When the jammer alternates between SJ, DSJ, ICJ, and IBJ, the proposed distributed anti-jamming strategy responds quickly, demonstrating robustness in dynamic jamming environments.