基于生成神经网络的空间一致空地信道建模
Spatially Consistent Air-to-Ground Channel Modeling via Generative Neural Networks
AI总结：
本文提出生成神经网络架构，利用无人机轨迹和基站条件数据，实现空间一致的RSS序列生成，以支持性能评估与切换等移动性管理任务。
摘要：
本文提出了一种用于空间一致空地信道建模的生成神经网络架构。该方法考虑了无人机沿典型城市路径的轨迹，捕获来自多个蜂窝基站（gNB）的接收信号强度（RSS）序列中的空间依赖性。通过引入条件数据，模型能够准确区分不同gNB，并将真实序列与生成序列之间的相关矩阵距离驱动至最小值。这使得能够使用空间（并由此时间）一致的RSS值来评估性能和移动性管理指标，而非独立的快照。对于某些基于这些指标的任务，例如切换，一致性至关重要。
英文摘要：
This article proposes a generative neural network architecture for spatially consistent air-to-ground channel modeling. The approach considers the trajectories of uncrewed aerial vehicles along typical urban paths, capturing spatial dependencies within received signal strength (RSS) sequences from multiple cellular base stations (gNBs). Through the incorporation of conditioning data, the model accurately discriminates between gNBs and drives the correlation matrix distance between real and generated sequences to minimal values. This enables evaluating performance and mobility management metrics with spatially (and by extension temporally) consistent RSS values, rather than independent snapshots. For some tasks underpinned by these metrics, say handovers, consistency is essential.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
