In this paper, we propose an efficient simulation method based on adaptive importance sampling, which can automatically find the optimal proposal within the Gaussian family based on previous samples, to evaluate the probability of bit error rate (BER) or word error rate (WER). These two measures, which involve high-dimensional black-box integration and rare-event sampling, can characterize the performance of coded modulation. We further integrate the quasi-Monte Carlo method within our framework to improve the convergence speed. The proposed importance sampling algorithm is demonstrated to have much higher efficiency than the standard Monte Carlo method in the AWGN scenario.

翻译：本文提出一种基于自适应重要性采样的高效仿真方法，该方法能够根据先前的采样在高斯族内自动寻找最优提议分布，用于评估误比特率或误字率。这两种度量涉及高维黑箱积分与小概率事件采样，能够表征编码调制的性能。我们进一步将准蒙特卡洛方法融入所提框架以提升收敛速度。实验证明，在加性高斯白噪声场景下，所提出的重要性采样算法相比标准蒙特卡洛方法具有显著更高的效率。