An anti-jamming resource scheduling algorithm for directional wireless communication networks based on reinforcement learning

In order to schedule link resources in wireless network efficiently and reduce network interference and improve network capacity, a distributed intelligent scheduling algorithm of link resources using directional communication is proposed. First, the convolution kernel is designed by combining the path loss model of communication. The density matrix of nodes is convolved to measure the strength of interference links, thereby avoiding the huge computational cost of channel estimation for all interference links. Then the link scheduling learning model interacting with the communication environment is designed based on the idea of reinforcement learning. Each link is trained independently by using neural network, and the decision results obtained from training are fed back to the environment for status update. The model runs iteratively in a constantly updated environment to learn the optimal scheduling policy. This method can measure the link interference intensity effectively in wireless network, and distribute the link resources according to the measured results, so as to maximize the network capacity. Simulation results show that the distributed scheduling algorithm can approach the global scheduling algorithms well both in iterative con- vergence and network capacity performance, reaching 92%-100% of it.