基于增强CVAE的三维射线重构方法  

3D Ray Reconstruction Method Based on Enhanced CVAE

作　　者：朱军[1] 杨军 李凯 于文欣 ZHU Jun;YANG Jun;LI Kai;YU Wenxin(School of Electronic and Information Engineering,Anhui University,Hefei 230601,China;School of Information Science and Technology,ShanghaiTech University,Shanghai 201210,China;LTE Public Performance Development Department,Huawei Shanghai Research Institute,Shanghai 201206,China)

机构地区：[1]安徽大学电子信息工程学院,合肥230601 [2]上海科技大学信息科学与技术学院,上海201210 [3]华为上海研究所LTE公共性能开发部,上海201206

出　　处：《北京邮电大学学报》2022年第5期36-41,共6页Journal of Beijing University of Posts and Telecommunications

基　　金：国家自然科学基金项目(62071002)。

摘　　要：射线追踪数据的样本空间不完备性是造成大规模多输入多输出信道幅值预测出现高预测误差用户较多的主要原因。为了更全面地表征所有用户的信道传播特征,提出了一种基于扩展概率分布的条件变分自编码器(CVAE)的三维射线重构方法。该方法基于用户射线样本的稀疏度选择先验概率分布,通过增强CVAE为高误差用户生成新的射线样本训练集,使射线追踪数据的隐变量分布更符合高误差用户的特征。仿真结果表明,基于所提出的方法在原有射线样本训练集中扩充新样本后,可将高预测误差用户数降低到原来的53.59%;使用新训练集训练的神经网络在得到大幅降低预测信道幅值时间开销的同时,将信道幅值预测精度提升了7.8%。The incomplete sample space of ray-tracing-data may increase high-prediction-error users in the massive multiple-input multiple-output channel amplitude prediction.To characterize the channel propagation features of all users,a method for 3D ray reconstruction is proposed based on extended probability distribution conditional variational auto-encoder(CVAE).The prior probability distribution is selected based on the sparsity of user ray samples.A new training set of ray samples is generated for high-prediction-error users by enhancing CVAE to make the latent variable distribution of ray-tracing-data fit the features of high-prediction-error users better.The simulation results show that the number of high-prediction-error users can be reduced to 53.59%by new training set based on the proposed method.Moreover,the new set improves the channel amplitude prediction accuracy by 7.8%while significantly reducing the time overhead of predicting the channel amplitude.

关 键 词：大规模多输入多输出 三维信道模型 条件变分自编码器 射线追踪 

分 类 号：TN911[电子电信—通信与信息系统]