分区域学习提升6G信道地图预测精度
Channel Gain Map Construction based on Subregional Learning and Prediction
- 按数据驱动聚类划分区域，每区独立建模
- 不均匀采样+边界数据复用，提升预测准确率
- 适合复杂环境下的6G无线通信系统设计
信道增益图（CGM）的构建是实现6G环境下感知环境无线通信的关键，其核心挑战在于如何利用有限测量值有效预测未知位置的信道增益。由于单一预测模型在复杂传播环境中表现不佳，本文提出基于子区域学习的CGM构建方案：通过数据驱动聚类将整个地图划分为多个子区域，为每个子区域分别构建并训练独立模型，从而在有限训练数据下更有效地提取各区域特有的传播特征。此外，通过不均匀子区域采样以及在子区域边界处重用训练数据，进一步提升了预测精度。仿真结果验证了该方案在CGM构建中的有效性。
原文摘要 · Abstract (English)
The construction of channel gain map (CGM) is essential for realizing environment-aware wireless communications expected in 6G, for which a fundamental problem is how to predict the channel gains at unknown locations effectively by a finite number of measurements. As using a single prediction model is not effective in complex propagation environments, we propose a subregional learning-based CGM construction scheme, with which the entire map is divided into subregions via data-driven clustering, then individual models are constructed and trained for every subregion. In this way, specific propagation feature in each subregion can be better extracted with finite training data. Moreover, we propose to further improve prediction accuracy by uneven subregion sampling, as well as training data reuse around the subregion boundaries. Simulation results validate the effectiveness of the proposed scheme in CGM construction.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。