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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2603.02061 |
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| _version_ | 1866918385975033856 |
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| author | Maggi, Lorenzo Bonev, Boris Wiesmayr, Reinhard Cammerer, Sebastian Keller, Alexander |
| author_facet | Maggi, Lorenzo Bonev, Boris Wiesmayr, Reinhard Cammerer, Sebastian Keller, Alexander |
| contents | We introduce a novel online convex optimization (OCO) framework to estimate the user's signal-to-interference-plus-noise ratio (SINR) from ACK/NACK feedback, channel quality indicator (CQI) reports, and previously selected modulation and coding scheme (MCS) values. Specifically, the proposed approach minimizes a regularized binary cross-entropy loss using mirror descent enhanced with Nesterov momentum for accelerated SINR tracking. Its parameters are tuned online via an expert-advice algorithm, endowing the estimator with continual learning capabilities. Numerical experiments in ray-traced scenarios show that the proposed method outperforms state-of-the-art schemes in estimation accuracy and adapts robustly to time-varying SINR regimes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02061 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SINR Estimation under Limited Feedback via Online Convex Optimization Maggi, Lorenzo Bonev, Boris Wiesmayr, Reinhard Cammerer, Sebastian Keller, Alexander Information Theory We introduce a novel online convex optimization (OCO) framework to estimate the user's signal-to-interference-plus-noise ratio (SINR) from ACK/NACK feedback, channel quality indicator (CQI) reports, and previously selected modulation and coding scheme (MCS) values. Specifically, the proposed approach minimizes a regularized binary cross-entropy loss using mirror descent enhanced with Nesterov momentum for accelerated SINR tracking. Its parameters are tuned online via an expert-advice algorithm, endowing the estimator with continual learning capabilities. Numerical experiments in ray-traced scenarios show that the proposed method outperforms state-of-the-art schemes in estimation accuracy and adapts robustly to time-varying SINR regimes. |
| title | SINR Estimation under Limited Feedback via Online Convex Optimization |
| topic | Information Theory |
| url | https://arxiv.org/abs/2603.02061 |