Sparse code multiple access (SCMA) and polar codes are key techniques for 5G systems. Towards to the realization of massive machine type communications (mMTC) in Internet of Things (IoT), we propose a joint channel estimation and decoding (JCD) scheme to enhance the performance of polar coded SCMA system. There are three parts in our JCD scheme. First, a sparse Bayesian learning (SBL) algorithm is proposed to measure the initial channel state information (CSI) for the JCD scheme. Then, the JCD receiver performs an iterative channel estimation and detection and decoding (ICED) scheme between the SCMA detector and the polar decoder, where the log-likelihood ratio (LLR) from SCMA detector and polar decoder can iteratively update, and the channel gain is also adjusted by the frozen bit error rate (FBER) from the polar decoder in each ICED iteration. In addition, we propose a soft successive cancellation list (soft-SCL) decoding algorithm by calculating the path metric from an LLR based logarithm method, then the soft-SCL decoder outputs the LLR information and the SCMA detector can update the symbol probability in this ICED scheme. Simulation results demonstrate the superior performance of our JCD scheme over fading channels.

中文翻译：

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衰落信道上极性编码SCMA系统的联合信道估计和解码

稀疏码多路访问（SCMA）和极地码是5G系统的关键技术。为了在物联网（IoT）中实现大规模机器类型通信（mMTC），我们提出了一种联合信道估计和解码（JCD）方案，以增强极性编码SCMA系统的性能。我们的JCD计划分为三个部分。首先，提出了一种稀疏贝叶斯学习（SBL）算法来测量JCD方案的初始信道状态信息（CSI）。然后，JCD接收器在SCMA检测器和极性解码器之间执行迭代的信道估计以及检测和解码（ICED）方案，其中来自SCMA检测器和极性解码器的对数似然比（LLR）可以迭代更新，并且信道增益在每次ICED迭代中，也可以通过极坐标解码器的冻结误码率（FBER）进行调整。另外，我们提出了一种软连续消除列表（soft-SCL）解码算法，该算法通过基于LLR的对数方法计算路径度量，然后soft-SCL解码器输出LLR信息，并且SCMA检测器可以在此更新符号概率。 ICED方案。仿真结果证明了我们的JCD方案优于衰落信道的性能。