Abstract: Aiming at the interference power threshold of the primary user (PU) in the cognitive radio network (CRN), transmission rate limit of the secondary user (SU) and the signaltointerferencenoiseratio (SINR) requirement, we proposed a learning traditional particle swarm optimization (LTPSO) algorithm, in which the evaporation factor was set according to the particle swarm learning factor,  a new particle swarm memory form was established, and the fitness values were screened proportionally. The simulation results show that the LTPSO algorithm achieves better optimization results.

Key words: cognitive radio, power allocation, particle swarm optimization (PSO) algorithm