Neural Network-Aided BCJR Algorithm for Joint Symbol Detection and Channel Decoding
Neural Network-Aided BCJR Algorithm for Joint Symbol Detection and Channel Decoding
复制标题
用于联合符号检测和信道解码的神经网络辅助 BCJR 算法
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
A. Wu
中科院分区：
文献类型：
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作者：
Wen;Chieh;Han;A. Wu
近年来，深度学习辅助交流系统取得了许多令人瞩目的成果，吸引了越来越多的研究人员在这个新兴领域。为了结合BCJR算法和神经网络的优点，提出了一种混合的BCJRNet符号检测方法，而不是用神经网络完全取代通信系统的功能块。然而，其单独的块设计不仅降低了系统性能，还导致了额外的硬件复杂性。在这项工作中，我们提出了一种BCJR接收机，用于联合符号检测和信道译码。它可以同时利用网格图和信道状态信息来更准确地计算分支概率，从而实现全局最优，比单独的块设计获得2.3dB的增益。此外，还提出了一种专用的神经网络模型来代替BCJR接收机基于信道模型的计算，避免了对完美CSI的要求，并且在CSI不确定的情况下具有更强的鲁棒性。
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.