Resilient Channel Charting Under Varying Radio Link Availability
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866908813307674624 |
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| author | Pirkl, Jonas Ott, Jonathan Stahlke, Maximilian Yammine, George Feigl, Tobias Mutschler, Christopher |
| author_facet | Pirkl, Jonas Ott, Jonathan Stahlke, Maximilian Yammine, George Feigl, Tobias Mutschler, Christopher |
| contents | Channel charting (CC) has become a key technology for RF-based localization, enabling unsupervised radio fingerprinting, even in non line of sight scenarios, with a minimum of reference position labels. However, most CC models assume fixed-size inputs, such as a constant number of antennas or channel measurements. In practical systems, antennas may fail, signals may be blocked, or antenna sets may change during handovers, making fixed-input architectures fragile. Existing radio-fingerprinting approaches address this by training separate models for each antenna configuration, but the resulting training effort scales prohibitively with the array size. We propose Adaptive Positioning (AdaPos), a CC architecture that natively handles variable numbers of channel measurements. AdaPos combines convolutional feature extraction with a transformer-based encoder using learnable antenna identifiers and self-attention to fuse arbitrary subsets of CSI inputs. Experiments on two public real-world datasets (SISO and MIMO) show that AdaPos maintains state-of-the-art accuracy under missing-antenna conditions and replaces roughly 57 configuration-specific models with a single unified model. With AdaPos and our novel training strategies, we provide resilience to both individual antenna failures and full-array outages. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_04704 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Resilient Channel Charting Under Varying Radio Link Availability Pirkl, Jonas Ott, Jonathan Stahlke, Maximilian Yammine, George Feigl, Tobias Mutschler, Christopher Signal Processing Channel charting (CC) has become a key technology for RF-based localization, enabling unsupervised radio fingerprinting, even in non line of sight scenarios, with a minimum of reference position labels. However, most CC models assume fixed-size inputs, such as a constant number of antennas or channel measurements. In practical systems, antennas may fail, signals may be blocked, or antenna sets may change during handovers, making fixed-input architectures fragile. Existing radio-fingerprinting approaches address this by training separate models for each antenna configuration, but the resulting training effort scales prohibitively with the array size. We propose Adaptive Positioning (AdaPos), a CC architecture that natively handles variable numbers of channel measurements. AdaPos combines convolutional feature extraction with a transformer-based encoder using learnable antenna identifiers and self-attention to fuse arbitrary subsets of CSI inputs. Experiments on two public real-world datasets (SISO and MIMO) show that AdaPos maintains state-of-the-art accuracy under missing-antenna conditions and replaces roughly 57 configuration-specific models with a single unified model. With AdaPos and our novel training strategies, we provide resilience to both individual antenna failures and full-array outages. |
| title | Resilient Channel Charting Under Varying Radio Link Availability |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2602.04704 |