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An automatic modulation classification has a very broad application in wireless communications. Recently, deep learning has been used to solve this problem and achieved superior performance. In most cases, the input size is fixed in convolutional neural network (CNN)-based modulation classification. However, the duration of the actual radio signal burst is variable. When the signal length is greater than the CNN input length, how to make full use of the complete signal burst to improve the classification accuracy is a problem needs to be considered. In this paper, three fusion methods are proposed to solve this problem, such as voting-based fusion, confidence-based fusion, and feature-based fusion. The simulation experiments are done to analyze the performance of these methods. The results show that the three fusion methods perform better than the non-fusion method. The performance of the two fusion methods based on confidence and feature is very close, which is better than that of the voting-based fusion.

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One of the basic capabilities of cognitive radio is to adapt the radio parameters according to the changing environment and user needs. This paper proposes a new adaptation method which uses particle swarm optimization (PSO) to optimize cognitive radio parameters given a set of objectives. The procedure of the proposed method is presented and multicarrier system is used for simulation analysis. Experimental results show that the proposed method performs far better than genetic algorithm (GA)-based adaptation method in terms of convergence speed, converged fitness values, and stability. The proposed method can also provide the tradeoffs of the objective functions, and the resulting parameter configuration is consistent with the weights of the objective functions.