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arXiv · 2509.25722

Transformer-Based Rate Prediction for Multi-Band Cellular Handsets

Ruibin Chen·Haozhe Lei·Hao Guo·Marco Mezzavilla·Hitesh Poddar·Tomoki Yoshimura·Sundeep Rangan
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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.

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Ruibin Chen, Haozhe Lei, Hao Guo, Marco Mezzavilla, Hitesh Poddar, Tomoki Yoshimura, Sundeep Rangan. 2025-09-30. Transformer-Based Rate Prediction for Multi-Band Cellular Handsets. https://arxiv.org/abs/2509.25722

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