Fast cognitive engine using case-based reasoning

Cognitive engine must adapt the radio parameters quickly according to the changing environment and user needs. A certain percentage of the particle swarm individuals are initialized based on the parameters of the selected matching cases, and the remaining individuals are randomly initialized, which can make particles of the particle swarm optimization algorithm near the optimal solution in the early search stage and maintain a certain degree of population diversity. A case-based reasoning particle swarm optimization algorithm is got, and the proposed algorithm is used to adjust and optimize the radio parameters to maximize the date throughput、minimize the transmit power and BER. The proposed algorithm is better than current algorithms in both the convergence rate and the optimization capability. Simulation results of multi-carrier system show the effectiveness of the algorithm.