评估 DNN 和 LSTM 非线性补偿器,以提高 DCO-OFDM 系统的性能

Gerges M. Salama, Amira A. Mohamed, H. Abdalla
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引用次数: 0

摘要

本研究提出了一种深度神经网络(DNN)和长短期记忆(LSTM)非线性补偿器方法,用于传统室内可见光通信(VLC)中的直流(DC)偏置光正交频分复用(DCO-OFDM),以处理非线性和检索高保真信号,并在性能和复杂性方面进行了比较。与现有深度神经网络方案中快速傅里叶变换后的数据训练不同,本研究提出了一种利用光电二极管输出的时域波形数据进行直接均衡的方案。在接收端对 OFDM 信号进行均衡,可减轻混合线性和非线性损伤,并节省频谱资源,而无需飞行员的协助。与基于不同导频的传统接收器和现有的基于 DL 的接收方法相比,所提出的自适应接收器方法在不同信噪比条件下都能获得更好的误码率性能。这项研究揭示了 LSTM 性能对系统信噪比的极端敏感性。在高信噪比(SNR)情况下,LSTM 的性能优于 DNN,但在低信噪比情况下,即使 LSTM 的复杂度很高,其性能也不及 DNN。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating DNN and LSTM nonlinear compensators for enhanced performance in DCO-OFDM system
This study proposes a deep neural network (DNN) and long-short-term memory (LSTM) nonlinear compensators method for direct current (DC)-biased optical orthogonal frequency division multiplexing (DCO-OFDM) in indoor visible light communication (VLC) conventional to handle the nonlinearity and retrieve the high-fidelity signals, and compared in terms of performance and complexity. Unlike the data training after fast Fourier transform in existing deep neural network schemes, this study proposes a scheme that uses the time domain waveform data output by photodiodes for direct equalization. The OFDM signal at the receiving end is equalized, which can mitigate hybrid linear and nonlinear impairments and save spectrum resources without requiring the pilots’ assistance. Compared with conventional receivers based on different guide frequencies and existing DL-based reception methods, the proposed adaptive receiver approach yields better bit error rate performance at different signal-to-noise ratios. This research reveals the extreme sensitivity of the LSTM’s performance to system SNR. LSTM outperforms DNN in high signal-to-noise ratio (SNR) situations, but at low SNR, even with high complexity, LSTM falls short of DNN’s performance.
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