Distributed Multi-View Vision-Only RSSI Estimation
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866913072968368128 |
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| author | Kim, Jung-Beom Lee, Woongsup |
| author_facet | Kim, Jung-Beom Lee, Woongsup |
| contents | Received Signal Strength Indicator (RSSI) estimation is essential for wireless link management, yet conventional feedback-based approaches incur uplink overhead, suffer from measurement instability, and are subject to inherent feedback loop latency, rendering proactive adaptation infeasible. Although vision-based approaches have been explored, existing methods remain limited by hardware dependency or auxiliary inputs, and lack the spatial diversity needed to resolve camera-side NLoS conditions. To address these limitations, we propose MulViT-TF, a vision-only RSSI estimation framework that exploits distributed multi-view observations through Transformer-based fusion, achieving complementary spatial coverage without any auxiliary sensing inputs. Experimental results across two distinct indoor scenes demonstrate that MulViT-TF achieves RMSE reductions of up to 26.3% and improves the 3dB error coverage by up to 13.8 percentage points over the best-performing single-view baseline, while using fewer FLOPs and parameters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_26738 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Distributed Multi-View Vision-Only RSSI Estimation Kim, Jung-Beom Lee, Woongsup Information Theory 94-05 Received Signal Strength Indicator (RSSI) estimation is essential for wireless link management, yet conventional feedback-based approaches incur uplink overhead, suffer from measurement instability, and are subject to inherent feedback loop latency, rendering proactive adaptation infeasible. Although vision-based approaches have been explored, existing methods remain limited by hardware dependency or auxiliary inputs, and lack the spatial diversity needed to resolve camera-side NLoS conditions. To address these limitations, we propose MulViT-TF, a vision-only RSSI estimation framework that exploits distributed multi-view observations through Transformer-based fusion, achieving complementary spatial coverage without any auxiliary sensing inputs. Experimental results across two distinct indoor scenes demonstrate that MulViT-TF achieves RMSE reductions of up to 26.3% and improves the 3dB error coverage by up to 13.8 percentage points over the best-performing single-view baseline, while using fewer FLOPs and parameters. |
| title | Distributed Multi-View Vision-Only RSSI Estimation |
| topic | Information Theory 94-05 |
| url | https://arxiv.org/abs/2604.26738 |