Deep neural network-based intercarrier interference detection for optical spectral efficient frequency division multiplexing system
Deep neural network-based intercarrier interference detection for optical spectral efficient frequency division multiplexing system
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DOI：
10.1117/1.oe.61.12.128101
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发表时间：
2022-12
影响因子：
1.3
通讯作者：
Guozhou Jiang;Jintao Wu;Mengyan Li;Liu Yang
中科院分区：
文献类型：
--
作者：
Guozhou Jiang;Jintao Wu;Mengyan Li;Liu Yang
摘要。在过去的几十年里，由于频谱高效频分复用（SEFDM）光通信系统失去正交性而导致的载波间干扰（ICI）问题得到了许多研究。本文引入深度神经网络（DNN）来处理ICI。研究和分析了ICI损伤机制与DNN之间的内在联系。在此基础上，通过对光学SEFDM强度调制/直接检测（IM/DD）通信系统的仿真，比较了DNN-ICI解码器与传统算法的性能。结果表明，在不同的带宽压缩系数下，设计简单的DNN- ici解码器在误码率（BER）方面明显优于其他方案。此外，该方法对光纤长度也具有鲁棒性。结果表明，所提出的DNN-ICI解码器在SEFDM IM/DD光学系统中具有很大的应用潜力。
Abstract. In the past few decades, many efforts have been made to solve the intercarrier interference (ICI) because of the loss of the orthogonality for the spectral efficient frequency division multiplexing (SEFDM) optical communication systems. In this paper, a deep neural network (DNN) is introduced to deal with the ICI. The intrinsic relationship between the mechanism of ICI damage and the DNN is studied and analyzed. Based on this analysis, the performance of DNN-ICI decoder compared with the conventional algorithms is demonstrated by simulation for optical SEFDM intensity modulation/direct detection (IM/DD) communication systems. The results show that the DNN-ICI decoder is greatly superior to other schemes in terms of bit error rate (BER) with a simple designed DNN under different bandwidth compression factors. Besides, the proposed methods are also robust to the fiber lengths. All the results indicate that the proposed DNN-ICI decoder has great potential to be used in SEFDM IM/DD optical systems.