Neural Equalizer Design Based on Gated Recurrent Unit and its Variants

Compared to traditional equalizers in high-speed links, neural equalizers offer stronger nonlinear modeling capabilities and better adaptability to varying channel conditions. However, existing neural equalizers face challenges such as high memory consumption and structural complexity, limiting their practical application in hardware implementations. To address these limitations, this paper introduces neural equalizers based on the Gated Recurrent Unit (GRU) and its four variants (GRU-v1, GRU-v2, GRU-v3, and GRU-v4). Through application to a high-speed link example, the results show that GRU-v3, which simplifies the update and reset gates by retaining only the bias terms, delivers the best performance in hardware implementation. It reduces memory usage by 65.68 % and improves signal equalization performance, making it a promising solution for practical applications.