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.

翻译：本文首次将影响函数应用于基于深度学习的无线接收机。针对全卷积接收机DeepRx，影响分析揭示了哪些训练样本驱动比特预测，从而实现对性能欠佳案例的定向微调。实验表明：采用类容量二元交叉熵损失函数的损失相对影响方法，结合对有益样本的一阶更新，能够最稳定地将误码率提升至接近理想辅助性能，在单目标场景中优于随机微调。多目标适应策略效果欠佳，凸显了该领域尚存的挑战。除实验验证外，本研究将影响函数与自影响校正理论关联，并提出二阶影响对齐更新策略。研究结果证实，影响函数既可作为接收机的可解释性分析工具，也能为高效接收机适应提供理论基础。