Transformer-Based Rate Prediction for Multi-Band Cellular Handsets
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918396588720128 |
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| author | Chen, Ruibin Lei, Haozhe Guo, Hao Mezzavilla, Marco Poddar, Hitesh Yoshimura, Tomoki Rangan, Sundeep |
| author_facet | Chen, Ruibin Lei, Haozhe Guo, Hao Mezzavilla, Marco Poddar, Hitesh Yoshimura, Tomoki Rangan, Sundeep |
| contents | Cellular wireless systems are facing a proliferation of frequency bands over a wide spectrum, particularly with the expansion into FR3. These bands must be supported in user equipment (UE) handsets with multiple antennas in a constrained form factor. Rapid variations in channel quality across the bands from motion and hand blockage, limited field-of-view of antennas, and hardware and power-constrained measurement sparsity pose significant challenges to reliable multi-band channel tracking. This paper formulates the problem of predicting achievable rates across multiple antenna arrays and bands with sparse historical measurements. We propose a transformer-based neural architecture that takes asynchronous rate histories as input and outputs per-array rate predictions. Evaluated on ray-traced simulations in a dense urban micro-cellular setting with FR1 and FR3 arrays, our method demonstrates superior performance over baseline predictors, enabling more informed band selection under realistic mobility and hardware constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25722 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transformer-Based Rate Prediction for Multi-Band Cellular Handsets Chen, Ruibin Lei, Haozhe Guo, Hao Mezzavilla, Marco Poddar, Hitesh Yoshimura, Tomoki Rangan, Sundeep Signal Processing Information Theory Machine Learning Cellular wireless systems are facing a proliferation of frequency bands over a wide spectrum, particularly with the expansion into FR3. These bands must be supported in user equipment (UE) handsets with multiple antennas in a constrained form factor. Rapid variations in channel quality across the bands from motion and hand blockage, limited field-of-view of antennas, and hardware and power-constrained measurement sparsity pose significant challenges to reliable multi-band channel tracking. This paper formulates the problem of predicting achievable rates across multiple antenna arrays and bands with sparse historical measurements. We propose a transformer-based neural architecture that takes asynchronous rate histories as input and outputs per-array rate predictions. Evaluated on ray-traced simulations in a dense urban micro-cellular setting with FR1 and FR3 arrays, our method demonstrates superior performance over baseline predictors, enabling more informed band selection under realistic mobility and hardware constraints. |
| title | Transformer-Based Rate Prediction for Multi-Band Cellular Handsets |
| topic | Signal Processing Information Theory Machine Learning |
| url | https://arxiv.org/abs/2509.25722 |