基于深度神经网络的高速信道自适应均衡器

高速串行接口是提高高性能互连网络带宽的关键技术，而信道均衡器则是提高信号完整性的核心部件。利用现代数字信号处理(DSP)结构，提出了基于深度神经网络(DNN)的高速信道均衡研究方法，此方法在面向未来50 GB以上的高速信道时，克服了传统判决反馈均衡器（DFE）的判决速度受限于反馈回路的固有缺陷问题。仿真结果表明，在采用PAM4编码方式，高速信道波特率为28 GB，信道损耗为15 dB，或者波特率为56 GB，信道损耗为30 dB时，与传统的15阶FFE 组合2阶DFE的均衡器结构相比，本文所提出的3层DNN结构，具有更好的均衡效果，以及更快的均衡收敛速度。

An adaptive high-speed channel equalizer based on deep neural network

High-speed serial interface is a key technology to improve the bandwidth of high-performance interconnection networks, and the channel equalizer is the core component to improve signal integrity. This paper uses the modern digital signal processing (DSP) structure to propose a deep neural network (DNN)-based high-speed channel equalization research method. This method overcomes the inherent shortcoming that the decision speed of the traditional decision feedback equalizer (DFE) is limited by the feedback loop when facing high-speed channels above 50GB in the future. The simulation results show that, compared with the traditional 2-tap DFE architecture with 15-tap FFE, the proposed 3-layer DNN structure has better equalization effect and faster equalization convergence speed, when the PAM4 encoding method is used, the high-speed channel baud rate is 28GB, and the channel loss is 15dB, or the baud rate is 56GB and the channel loss is 30dB.