Department of Rail Transit, Shijiazhuang Institute of Railway Technology, Shijiazhuang, China.
PLoS One. 2024 Oct 11;19(10):e0311897. doi: 10.1371/journal.pone.0311897. eCollection 2024.
With the increasing amount of data in railway communication system, the conventional wireless high-frequency communication technology cannot meet the requirements of modern communication and needs to be improved. In order to meet the requirements of high-speed signal processing, a high-speed communication signal processing method based on visible light is developed and studied. This method combines the adaptive equalization algorithm with deep learning and is applied to railway communication signal processing. In this research, the wavelength division multiplexing (WDM) and orthogonal frequency division multiplexing (OFDM) techniques are used, and fuzzy C equalization algorithm is used to softly divide the received signals, reduce signal distortion and interference suppression. The experimental results showed that increasing the step size could reduce the equalization effect, while increasing the modulation parameter will increase the bit error rate. Through deep learning to achieve channel equalization, visible light communication could effectively mitigate multi-path transmission and reflection interference, thereby reducing the bit error rate to the level of 0.0001. Under various signal-to-noise ratios, the system using the channel compensation method achieved the lowest bit error rate. This outcome was achieved by implementing hybrid modulation scheme, including Wavelength division multiplexing (WDM) and direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) techniques. It has been proved that this method can effectively reduce the channel distortion when the receiver is moving. This study develops a dependable communication system, which enhances signal recovery, reduces interference, and improves the quality and transmission efficiency of railway communication. The system has practical application value in the field of railway communication signal processing.
随着铁路通信系统中数据量的不断增加，传统的无线高频通信技术已经不能满足现代通信的要求，需要进行改进。为了满足高速信号处理的要求，开发并研究了一种基于可见光的高速通信信号处理方法。该方法将自适应均衡算法与深度学习相结合，并应用于铁路通信信号处理中。在本研究中，采用了波分复用（WDM）和正交频分复用（OFDM）技术，并使用模糊 C 均衡算法对接收信号进行软分割，以减少信号失真和干扰抑制。实验结果表明，增加步长会降低均衡效果，而增加调制参数会增加误码率。通过深度学习实现信道均衡，可见光通信可以有效地减轻多径传输和反射干扰，从而将误码率降低到 0.0001 以下。在各种信噪比下，采用信道补偿方法的系统实现了最低的误码率。这一结果是通过实现混合调制方案实现的，包括波分复用（WDM）和直流偏置光正交频分复用（DCO-OFDM）技术。研究结果表明，该方法可以有效地减少接收机移动时的信道失真。本研究开发了一种可靠的通信系统，提高了信号恢复能力，降低了干扰，提高了铁路通信的质量和传输效率。该系统在铁路通信信号处理领域具有实际应用价值。
