This paper devises a decision-making method to address the varying communication requirements across diverse scenarios. The proposed method can objectively measure the performance of communication, and the weights of various indicators can be customized to adapt to different communication scenarios. Furthermore, in order to enhance communication performance, this paper has investigated a reinforcement learning models suitable for waveform and power decision-making, and proposes an explored action deletion algorithm based on the Q-learning method. The simulation results demonstrate that the proposed method outperforms existing methods in terms of convergence speed and algorithm accuracy.
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