AI 中文总结
SCALE-X提出一种复杂度感知的低精度数字预失真方法，通过系数剪枝和定点优化，在降低计算开销的同时保持精度，提升整体效率。
AI 中文摘要
本文提出SCALE-X，一种系统性、复杂度感知、低精度、基于模型的数字预失真（DPD）技术，用于增强功率放大器的性能。该方法采用新颖的复杂度感知系数剪枝方法，结合优化的定点建模，能够更准确地捕获量化效应，并为实现提供更明智的决策支持。通过目标化配置模型阶数、记忆深度和系数位宽，可在不大幅牺牲精度的前提下大幅降低计算开销。现场可编程门阵列实现展示了实际权衡，整体效率得到提升。
英文摘要
This letter presents SCALE-X, a systematic, complexity-aware, low-precision, model-based digital predistortion (DPD) technique for power amplifiers with enhanced capabilities. The proposed approach employs a novel complexity-aware coefficient pruning method, which, when combined with optimized fixed-point modeling, enables more accurate capture of quantization effects and supports better informed decisions for implementation. Targeted configuration of model order, memory depth, and coefficient bitwidth delivers dramatic cuts in computational overhead without much sacrificing accuracy. Field-programmable gate array implementation demonstrates practical trade-offs with improved overall efficiency.
