# Transformer-Based Rate Prediction for Multi-Band Cellular Handsets

arXiv: https://arxiv.org/abs/2509.25722 · HTML: https://mlsanity.com/paper/2509.25722/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 30 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 14 of 14)

- [Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels](https://mlsanity.com/paper/1910.02900/) (2019)
- [Computer Vision Aided mmWave Beam Alignment in V2X Communications](https://mlsanity.com/paper/2207.11409/) (2022)
- [Digital Twin-Enhanced Wireless Indoor Navigation: Achieving Efficient Environment Sensing with Zero-Shot Reinforcement Learning](https://mlsanity.com/paper/2306.06766/) (2023)
- [Environment Semantics Aided Wireless Communications: A Case Study of mmWave Beam Prediction and Blockage Prediction](https://mlsanity.com/paper/2301.05837/) (2023)
- [Cellular Wireless Networks in the Upper Mid-Band](https://mlsanity.com/paper/2309.03038/) (2023)
- [Reinforcement Learning with Physics-Informed Symbolic Program Priors for Zero-Shot Wireless Indoor Navigation](https://mlsanity.com/paper/2506.22365/) (2025)
- [Site-Specific Location Calibration and Validation of Ray-Tracing Simulator NYURay at Upper Mid-Band Frequencies](https://mlsanity.com/paper/2507.22027/) (2025)
- [Parametrization and Estimation of High-Rank Line-of-Sight MIMO Channels with Reflected Paths](https://mlsanity.com/paper/2205.05640/) (2022)
- [Sharing Spectrum and Services in the 7-24 GHz Upper Midband](https://mlsanity.com/paper/2402.08649/) (2024)
- [Propagation measurements and channel models in Indoor Environment at 6.75 GHz FR1(C) and 16.95 GHz FR3 Upper-mid band Spectrum for 5G and 6G](https://mlsanity.com/paper/2405.01358/) (2024)
- [Angular Spread Statistics for 6.75 GHz FR1(C) and 16.95 GHz FR3 Mid-Band Frequencies in an Indoor Hotspot Environment](https://mlsanity.com/paper/2409.03013/) (2024)
- [Urban Outdoor Propagation Measurements and Channel Models at 6.75 GHz FR1(C) and 16.95 GHz FR3 Upper Mid-Band Spectrum for 5G and 6G](https://mlsanity.com/paper/2410.17539/) (2024)
- [A Millimeter-Wave Channel Simulator NYUSIM with Spatial Consistency and Human Blockage](https://mlsanity.com/paper/1908.09762/) (2019)
- [Hand and Body Blockage Measurements with Form-Factor User Equipment at 28 GHz](https://mlsanity.com/paper/1912.03717/) (2019)

## Built on by (most cited first, 1 of 1)

- [LOCUS-DT: Localization via Observation-Conditioned Uncertainty Scoring with Digital Twins](https://mlsanity.com/paper/2608.00406/) (2026)

## Abstract (arXiv)

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.