用影响函数精准优化深度学习无线接收机，提升误码率表现。
Targeted Fine-Tuning of DNN-Based Receivers via Influence Functions
- 通过影响函数识别关键训练样本，实现针对性微调。
- 一阶更新有益样本可使误码率逼近理想性能，优于随机微调。
- 提出二阶对齐更新策略，兼顾解释性与高效适应性。
我们首次将影响函数应用于基于深度学习的无线接收机。以全卷积接收机DeepRx为例，影响分析揭示了驱动比特预测的关键训练样本，从而实现对性能较差情况的精准微调。实验表明，采用具有容量特性的相对损失函数，并对有益样本进行一阶更新，能最稳定地降低误码率，接近理想基准性能，优于单目标场景下的随机微调。多目标适应效果较差，凸显仍存挑战。此外，我们建立了影响函数与自影响修正的联系，提出一种二阶、影响对齐的更新策略。结果证明，影响函数既是可解释工具，也为高效接收机适配提供了基础。
原文摘要 · Abstract (English)
We present the first use of influence functions for deep learning-based wireless receivers. Applied to DeepRx, a fully convolutional receiver, influence analysis reveals which training samples drive bit predictions, enabling targeted fine-tuning of poorly performing cases. We show that loss-relative influence with capacity-like binary cross-entropy loss and first-order updates on beneficial samples most consistently improves bit error rate toward genie-aided performance, outperforming random fine-tuning in single-target scenarios. Multi-target adaptation proved less effective, underscoring open challenges. Beyond experiments, we connect influence to self-influence corrections and propose a second-order, influence-aligned update strategy. Our results establish influence functions as both an interpretability tool and a basis for efficient receiver adaptation.
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