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Deep learning-based symbol detection algorithm in IMDD-OOFDM system
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作者 Zhang Huibin Li Tianzhu +1 位作者 Liu Haojiang Li Zhuotong 《The Journal of China Universities of Posts and Telecommunications》 EI CSCD 2022年第6期36-45,共10页
In the current research on intensity-modulation and direct-detection optical orthogonal frequency division multiplexing(IMDD-OOFDM) system, effective channel compensation is a key factor to improve system performance.... In the current research on intensity-modulation and direct-detection optical orthogonal frequency division multiplexing(IMDD-OOFDM) system, effective channel compensation is a key factor to improve system performance. In order to improve the efficiency of channel compensation, a deep learning-based symbol detection algorithm is proposed in this paper for IMDD-OOFDM system. Firstly, a high-speed data streams symbol synchronization algorithm based on a training sequence is used to ensure accurate symbol synchronization. Then the traditional channel estimation and channel compensation are replaced by an echo state network(ESN) to restore the transmitted signal. Finally, we collect the data from the system experiment and calculate the signal-to-noise ratio(SNR). The analysis of the SNR optimized by the ESN proves that the ESN-based symbol detection algorithm is effective in compensating nonlinear distortion. 展开更多
关键词 echo state network(ESN) channel estimation channel compensation symbol synchronization training sequence
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