Targeted Fine-Tuning of DNN-Based Receivers via Influence Functions
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908778071326720 |
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| author | Tuononen, Marko Penttinen, Heikki Hautamäki, Ville |
| author_facet | Tuononen, Marko Penttinen, Heikki Hautamäki, Ville |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15950 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Targeted Fine-Tuning of DNN-Based Receivers via Influence Functions Tuononen, Marko Penttinen, Heikki Hautamäki, Ville Machine Learning Signal Processing 68T05 (Primary), 94A05 (Secondary) I.2.6; C.2.1; I.5.2 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. |
| title | Targeted Fine-Tuning of DNN-Based Receivers via Influence Functions |
| topic | Machine Learning Signal Processing 68T05 (Primary), 94A05 (Secondary) I.2.6; C.2.1; I.5.2 |
| url | https://arxiv.org/abs/2509.15950 |