Chinese Optics Letters, Volume. 22, Issue 6, 060602(2024)
Channel estimation-based time-frequency neural network for post-equalization in underwater visible light communication
Fig. 3. (a) BER performance of different PBs and passband weights used by loss function. Spectra of the original, received, and NN equalized signal of (b) using PB of 723 MHz and weight of 1.0 and (c) using PB of 797 MHz and weight of 0.9.
Fig. 4. (a) Error band diagram of bitrate in continuously changing epoch using different loss function and model input. STFT spectrum of (b) received signal, (c) original signal, (d) received signal after mask.
Fig. 5. The bitrate contour plot of different working points with the post-equalizer using (a) LMS-Volterra; (b) DNN; (c) TFDNet; (d) CBV-TFNet. The bit-power loading result in the communication test with the post-equalizer using (e) LMS-Volterra; (f) DNN; (g) TFDNet; (h) CBV-TFNet.
Fig. 6. Comparison of consecutive window input and output capability of DNN based and CBV-TFNet equalizers.
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Haoyu Zhang, Li Yao, Chaoxu Chen, Yuan Wei, Chao Shen, Jianyang Shi, Junwen Zhang, Ziwei Li, Nan Chi, "Channel estimation-based time-frequency neural network for post-equalization in underwater visible light communication," Chin. Opt. Lett. 22, 060602 (2024)
Category: Fiber Optics and Optical Communications
Received: Dec. 1, 2023
Accepted: Feb. 22, 2024
Posted: Feb. 22, 2024
Published Online: Jun. 18, 2024
The Author Email: Nan Chi (nanchi@fudan.edu.cn)