Targeted Fine-Tuning of DNN-Based Receivers via Influence Functions

• URL: http://arxiv.org/abs/2509.15950v1
• Date: Fri, 19 Sep 2025 13:01:30 GMT
• Title: Targeted Fine-Tuning of DNN-Based Receivers via Influence Functions
• Authors: Marko Tuononen, Heikki Penttinen, Ville Hautamäki,
• Abstract summary: We present the first use of influence functions for deep learning-based wireless receivers.<n>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.
• Score: 7.18961038438762
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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