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.

翻译：蜂窝无线系统正面临频谱宽带上频段数量激增的挑战，特别是在向FR3频段扩展的背景下。用户设备(UE)手机必须在受限尺寸内通过多天线支持这些频段。由运动与手部遮挡导致的跨频段信道质量快速变化、天线有限视场以及硬件与功耗受限的测量稀疏性，给可靠的多频段信道追踪带来了重大挑战。本文提出了基于稀疏历史测量数据预测多天线阵列及频段可达速率的问题。我们设计了一种基于Transformer的神经网络架构，该架构将异步速率历史作为输入，输出各阵列的速率预测。通过密集城市微蜂窝场景下FR1与FR3阵列的射线追踪仿真评估，我们的方法在基准预测器中展现出优越性能，能够在实际移动性与硬件约束条件下实现更优的频段选择决策。