Pilot-Assisted SIMO-NOMA Signal Detection With Learnable Successive Interference Cancellation
Pilot-Assisted SIMO-NOMA Signal Detection With Learnable Successive Interference Cancellation
复制标题
具有可学习连续干扰消除功能的导频辅助 SIMO-NOMA 信号检测
DOI：
10.1109/lcomm.2021.3070705
复制
发表时间：
2021-07-01
期刊：
影响因子：
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通讯作者：
You, Xiaohu
中科院分区：
文献类型：
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作者：
Wang, Xiaoming;Zhu, Pan;You, Xiaohu
在这封信中，我们提出了一种基于可学习的连续干扰消除（PA-LSIC）的上行链路单输入多输出（SIMO）非正交多址（NOMA）系统的导频辅助接收机方案。PA-LSIC将连续干扰消除（SIC）结构与模型驱动的深度学习网络相结合。考虑到信道估计中的噪声影响以及SIC过程中检测和消除的不完全性，引入了噪声消除因子和干扰消除因子等新的参数，并利用反向传播算法和随机梯度下降算法对这些参数进行了优化。仿真结果表明，该算法具有上级误码性能和较低的训练和实现复杂度。
In this letter, we propose a pilot-assisted receiver scheme based on learnable successive interference cancellation (PA-LSIC) for uplink single-input multiple-output (SIMO) non-orthogonal multiple access (NOMA) systems. The PA-LSIC combines the successive interference cancellation (SIC) structure with the model-driven deep learning network. Considering the noise impact of channel estimation and the incomplete detection and cancellation in SIC process, we introduce some new parameters, such as noise cancellation factor and interference cancellation factor, which are optimized by using the back-propagation algorithm and random gradient descent algorithm. Numerical results show that the PA-LSIC has superior bit error rate (BER) performance and lower complexity during training and implementation.