{"ID":2862473,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.25722","arxiv_id":"2509.25722","title":"Transformer-Based Rate Prediction for Multi-Band Cellular Handsets","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2509.25722","url_pdf":"https://arxiv.org/pdf/2509.25722v2","authors":"[\"Ruibin Chen\",\"Haozhe Lei\",\"Hao Guo\",\"Marco Mezzavilla\",\"Hitesh Poddar\",\"Tomoki Yoshimura\",\"Sundeep Rangan\"]","published":"2025-09-30T03:29:42Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.IT\",\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false}