论文标题
神经网络辅助BCJR算法用于联合符号检测和渠道解码
Neural Network-Aided BCJR Algorithm for Joint Symbol Detection and Channel Decoding
论文作者
论文摘要
最近，深度学习辅助的交流系统取得了许多引人注目的结果，并吸引了越来越多的新兴领域的研究人员。提出了BCJRNET符号检测的混合方式来结合BCJR算法和神经网络的优势，而不是完全替换了通信系统的功能块。但是，其独立的块设计不仅会降低系统性能，还会导致其他硬件复杂性。在这项工作中，我们提出了一个BCJR接收器，以进行联合符号检测和通道解码。它可以同时利用格子图和通道状态信息，以更准确地计算分支概率，从而在单独的块设计上使用2.3 dB增益实现全局最佳。此外，提出了一个专用的神经网络模型来替代基于通道模型的BCJR接收器的计算，该计算可以避免完美CSI的要求，并且在CSI不确定性下以1.0 dB增益而更健壮。
Recently, deep learning-assisted communication systems have achieved many eye-catching results and attracted more and more researchers in this emerging field. Instead of completely replacing the functional blocks of communication systems with neural networks, a hybrid manner of BCJRNet symbol detection is proposed to combine the advantages of the BCJR algorithm and neural networks. However, its separate block design not only degrades the system performance but also results in additional hardware complexity. In this work, we propose a BCJR receiver for joint symbol detection and channel decoding. It can simultaneously utilize the trellis diagram and channel state information for a more accurate calculation of branch probability and thus achieve global optimum with 2.3 dB gain over separate block design. Furthermore, a dedicated neural network model is proposed to replace the channel-model-based computation of the BCJR receiver, which can avoid the requirements of perfect CSI and is more robust under CSI uncertainty with 1.0 dB gain.
