Optimizing Equalizations of FFE, CTLE, and DFE Jointly Through a Single Pulse Response

Yen-Hao Chen;Chun-I Tseng;Ding-Bing Lin
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Abstract

With the increasing data rate of high-speed digital systems, equalization techniques have been widely applied to counteract intersymbol interference and maximize eye opening in today's high-speed serial links. Channel simulations with optimal equalization settings allow engineers to explore design tradeoffs, reduce the need for costly prototypes, and ensure robust performance before manufacturing. Existing equalization optimization methods require either iterative interaction with commercial channel simulators during the optimization process, developing models with extensive training data before optimization, or separately optimizing linear equalizers in the frequency domain. In this article, the method to jointly optimize feedforward equalization, continuous-time linear equalization, and decision feedback equalization at the transmitter, receiver, or both is proposed. The optimization is achieved through the analysis of a nonequalized pulse response, eliminating the computational cost of channel simulations during optimization. Practical examples using non-return-to-zero (NRZ) and pulse amplitude modulation four-level (PAM4) signaling schemes demonstrate the effectiveness of the proposed method. A comparison with the Bayesian optimization approach, a widely discussed method for equalization optimization, shows that while both methods achieve nearly identical optimal eye openings, the proposed method offers significantly higher optimization efficiency.
通过单一脉冲响应优化FFE, CTLE和DFE的联合均衡
随着高速数字系统数据速率的不断提高,均衡技术已被广泛应用于当今的高速串行链路中,以抵消符号间干扰并最大限度地提高开眼率。采用最佳均衡设置的信道模拟可让工程师探索设计权衡,减少对成本高昂的原型的需求,并在生产前确保稳健的性能。现有的均衡优化方法要么需要在优化过程中与商用信道模拟器反复交互,要么需要在优化前开发具有大量训练数据的模型,要么需要在频域中单独优化线性均衡器。本文提出了在发射机、接收机或两者上联合优化前馈均衡、连续时间线性均衡和决策反馈均衡的方法。优化是通过分析非均衡脉冲响应实现的,消除了优化过程中信道模拟的计算成本。使用非归零(NRZ)和脉冲幅度调制四级(PAM4)信令方案的实际例子证明了所提方法的有效性。与贝叶斯优化方法(一种广泛讨论的均衡优化方法)的比较表明,虽然这两种方法都能获得几乎相同的最佳开眼度,但拟议方法的优化效率要高得多。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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