https://scholars.lib.ntu.edu.tw/handle/123456789/611206
標題: | Neural Network-Aided BCJR Algorithm for Joint Symbol Detection and Channel Decoding | 作者: | AN-YEU(ANDY) WU | 關鍵字: | BCJR algorithm; Channel decoding; Neural network; Symbol detection; Turbo codes | 公開日期: | 2020 | 卷: | 2020-October | 來源出版物: | IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation | 摘要: | 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. © 2020 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096745635&doi=10.1109%2fSiPS50750.2020.9195225&partnerID=40&md5=1350be0de75fd4d4b638ba91fa401080 https://scholars.lib.ntu.edu.tw/handle/123456789/611206 |
ISBN: | 9.78173E+12 | ISSN: | 15206130 | DOI: | 10.1109/SiPS50750.2020.9195225 | SDG/關鍵字: | Channel state information; Decoding; Deep learning; Signal detection; Silicon compounds; Accurate calculations; Channel decoding; Functional block; Global optimum; Hardware complexity; Neural network model; Symbol detection; Trellis diagram; Neural networks |
顯示於: | 電機工程學系 |
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