用Transformer预测多频段手机速率，提升带宽选择效率。
Transformer-Based Rate Prediction for Multi-Band Cellular Handsets
- 基于异步速率历史数据，用Transformer建模多天线多频段速率
- 在密集城区仿真中，相比基线方法性能更优，支持实时决策
- 适合移动通信设备设计与无线资源管理场景
蜂窝无线系统正面临从FR1到FR3的广泛频段扩展，这些频段需在空间受限的用户设备（UE）中通过多天线实现支持。由于运动和手部遮挡导致的频段间信道质量快速变化、天线视场有限以及测量稀疏性带来的硬件与功耗约束，可靠地跟踪多频段信道成为挑战。本文提出一种基于Transformer的神经架构，利用稀疏的历史速率数据，预测多个天线阵列在各频段上的可实现速率。在包含FR1与FR3阵列的密集城市微小区射线追踪仿真中，该方法显著优于基线预测器，可在真实移动性和硬件约束下实现更优的频段选择决策。
原文摘要 · Abstract (English)
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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