A Centralized Multi-User Anti-Composite Intelligent Interference Algorithm Based on Improved Q-Learning

This paper proposes a central anti-jamming algorithm (CAJA) based on improved Q-learning to further solve the communication challenges faced by multi-user wireless networks in terms of external complex malicious interference. will also reduce dual factors restricting quality, impact inter-user interference within network, and effect system improve transmission. Firstly, base station that coordinates allocates channels for users network is set up using architecture constitute centralized network. Secondly, modeled single-user Markov decision process which main body. Finally, an used overall transmission income station, user number sequential action avoiding It designed avoid internal performance during early stage communication, achieving improvement. Simulation results show comparison existing independent traditional orthogonal frequency-hopping scheme, proposed significantly improves performance.