车载通信是高速串行器与解串器（serializer and deserializer, SerDes）技术应用的一个重要领域。针对复杂车载环境中链路高频衰减导致的符号间干扰在高阶调制方式下更为严重的问题，引入深度学习方法，设计一种基于Transformer编码器结构的低复杂度信道均衡方案，以提高接收信号质量。该方案将输入序列转换为抽象的表示向量，然后利用编码器层提取表示向量的特征信息，最后全连接层根据特征信息对信号进行分类，从而实现高速SerDes信道均衡。实验结果表明：与传统自适应算法和全连接神经网络模型相比，所提方案能够有效降低高频衰减导致的信号失真，在计算复杂度降低19%和24%的情况下接收信噪比增益分别为1.8 dB和0.9 dB。通过在高速SerDes系统中应用所提信道均衡方案，可以提高信号传输质量以及增强系统的鲁棒性。

Vehicle communication is recognized as a significant application field for high-speed serializer and deserializer(SerDes) technology. To address the issue of severe inter-symbol interference caused by high-frequency attenuation in complex vehicle environments, particularly under higher-order modulation schemes, a deep learning approach was introduced. A low-complexity channel equalization scheme based on the Transformer encoder structure was designed to enhance the quality of received signals. In this scheme, the input sequence was transformed into abstract representation vectors, followed by the extraction of feature information from these vectors through encoder layers. Subsequently, a fully connected layer classified the signals based on the extracted feature information, thereby achieving high-speed SerDes channel equalization. Experimental results demonstrate that compared to traditional adaptive algorithms and fully connected neural network models, the proposed scheme effectively reduces signal distortion caused by high-frequency attenuation. The scheme achieves a computational complexity reduction of 19% and 24%, respectively, while providing received signal-to-noise ratio gains of 1.8 dB and 0.9 dB. By applying the proposed channel equalization scheme in high-speed SerDes systems, the signal transmission quality has been improved, and the system's robustness has been enhanced.