Transformer-Based Rate Prediction for Multi-Band Cellular Handsets
This work addresses the challenge of reliable multi-band channel tracking for cellular wireless systems, which is incremental as it applies a known transformer method to a specific domain problem.
The paper tackles the problem of predicting achievable rates across multiple antenna arrays and bands with sparse historical measurements in multi-band cellular handsets, proposing a transformer-based neural architecture that demonstrates superior performance over baseline predictors in ray-traced simulations.
Cellular wireless systems are witnessing the proliferation of frequency bands over a wide spectrum, particularly with the expansion of new bands in 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.