摘要
射线追踪数据样本的缺失是造成大规模多输入多输出(Massive Multiple-Input MultipleOutput,Massive MIMO)信道特征预测出现较多预测误差较高的用户的主要原因。为了降低高误差用户数及预测误差,提出了一种基于条件变分自编码器(Conditional Variational AutoEncoder,CVAE)的射线样本生成算法来增添缺失区间的射线样本。仿真结果表明,基于所提出的算法在原有射线样本集中扩充新样本后,可将高预测误差用户数降低到原来的46.4%;完善训练集后的神经网络在降低得到信道幅值的时间开销的同时,将信道幅值预测精度提升了6.2%。
The lack of ray-tracing-data samples can cause more high-prediction-error users to appear in the Massive MIMO(Multiple-Input Multiple-Output) channel feature prediction. In order to reduce the number of high-prediction-error users and prediction errors, a ray sample generation algorithm based on CVAE(Conditional Variational Auto-Encoder) is proposed. This algorithm can add ray samples in the missing intervals. The simulation results indicate that the number of high-prediction-error users can be reduced to 46.4% by expanding new samples in the original ray sample set based on the proposed method. Moreover,the extended training set improves the channel amplitude prediction accuracy by 6.2% while obtaining a reduction in the time overhead of predicting the channel amplitude.
作者
朱军
杨军
李凯
于文欣
ZHU Jun;YANG Jun;LI Kai;YU Wenxin(School of Electronic and Information Engineering,Anhui University,Hefei Anhui 230601,China;ShanghaiTech University,Shanghai 201210,China;Huawei Shanghai Research Institute,Shanghai 201206,China)
出处
《通信技术》
2022年第4期409-414,共6页
Communications Technology
基金
国家自然科学基金项目(62071002)。
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
massive multiple-input multiple-output
3D channel model
conditional variational auto-encoder
ray-tracing
