AI 中文总结
研究不同系统配置间基于学习的预编码适配难题，提出跨系统神经预编码器XNP，利用预编码问题结构一致性复用更新规则，仅学习轻量级非线性映射，用少量参数实现高效适配，实验证明其优于基于图神经网络的基线。
AI 中文摘要
由于多种类型的变量和约束，在不同系统配置间适配基于学习的预编码具有挑战性。虽已提出大规模神经网络用于跨任务适配，但这种适应性是否需要大模型尚不明晰。本文识别出一类预编码问题的结构属性：当其他变量固定时，交替优化（AO）中与每种变量相关的子问题在不同系统间共享共同计算结构。基于此，我们提出跨系统神经预编码器（XNP），其每层实现受AO启发的更新方程，定义层间输入输出映射。通过复用共同更新结构并仅学习轻量级非线性映射，XNP仅用数千个可训练参数就能在系统间高效适配。仿真结果表明，预训练的XNP能以比基于图神经网络的基线显著更少的训练样本和轮次快速适应新配置。这表明可通过利用共享计算结构而非依赖大模型来实现跨系统适应性。
英文摘要
Adapting learning-based precoding across different system configurations is challenging due to multiple types of variables and constraints. While large-scale neural networks have been proposed for cross-task adaptation, whether such adaptability requires large models remains unclear. In this paper, we identify a structural property of a class of precoding problems: the subproblems associated with each type of variable in alternative optimization (AO) share a common computational structure across systems when other variables are fixed. This structural consistency enables the reuse of update rules across systems. Based on this observation, we propose a cross-system neural precoder (XNP), where each layer implements AO-inspired update equations, which define the layer-wise input-output mappings. By reusing common update structures and learning only lightweight nonlinear mappings, the XNP enables efficient adaptation across systems only with several thousand trainable parameters. Simulation results show that pre-trained XNPs achieve fast adaptation to new configurations with significantly fewer training samples and epochs than a graph neural network-based baseline. This demonstrates that cross-system adaptability can be achieved by exploiting shared computational structure, rather than relying on large models.
