Sixth-generation (6G) networks are envisioned to achieve full-band cognition by jointly utilizing spectrum resources from Frequency Range~1 (FR1) to Frequency Range~3 (FR3, 7--24\,GHz). Realizing this vision faces two challenges. First, physics-based ray tracing (RT), the standard tool for network planning and coverage modeling, becomes computationally prohibitive for multi-band and multi-directional analysis over large areas. Second, current 5G systems rely on inter-frequency measurement gaps for carrier aggregation and beam management, which reduce throughput, increase latency, and scale poorly as bands and beams proliferate. These limitations motivate a data-driven approach to infer high-frequency characteristics from low-frequency observations. This work proposes CommUNext, a unified deep learning framework for cross-band, multi-directional signal strength (SS) prediction. The framework leverages low-frequency coverage data and crowd-aided partial measurements at the target band to generate high-fidelity FR3 predictions. Two complementary architectures are introduced: Full CommUNext, which substitutes costly RT simulations for large-scale offline modeling, and Partial CommUNext, which reconstructs incomplete low-frequency maps to mitigate measurement gaps in real-time operation. Experimental results show that CommUNext delivers accurate and robust high-frequency SS prediction even with sparse supervision, substantially reducing both simulation and measurement overhead.

翻译：第六代（6G）网络旨在通过联合利用从频段范围1（FR1）到频段范围3（FR3，7–24 GHz）的频谱资源，实现全频段认知。实现这一愿景面临两大挑战。首先，基于物理的射线追踪（RT）作为网络规划和覆盖建模的标准工具，在大范围多频段多方向分析中计算成本过高。其次，当前5G系统依赖频间测量间隔进行载波聚合和波束管理，这会降低吞吐量、增加延迟，并随着频段和波束数量增加而难以扩展。这些局限性促使我们采用数据驱动方法，从低频观测中推断高频特性。本研究提出CommUNext，一个用于跨频段多方向信号强度（SS）预测的统一深度学习框架。该框架利用低频覆盖数据和目标频段的众包部分测量，生成高保真的FR3预测。我们引入了两种互补架构：完整版CommUNext，用于替代大规模离线建模中昂贵的RT仿真；部分版CommUNext，通过重构不完整的低频地图来缓解实时操作中的测量间隔问题。实验结果表明，即使在稀疏监督下，CommUNext也能提供准确且稳健的高频SS预测，显著降低了仿真和测量开销。