ST-CSFNet: Spatial-Temporal Cross Scale FNet for Radio Environment Map Forecasting

IF 3.7 3区计算机科学 Q2 TELECOMMUNICATIONS

IEEE Communications Letters Pub Date : 2025-01-16 DOI:10.1109/LCOMM.2025.3529898

Song Zha;Haiyang Xia;Jijun Huang;Peiguo Liu

引用次数: 0

Abstract

Radio environment maps (REMs) forecasting is critical for wireless communication, yet existing methods have notable gaps, hindering efficient, reliable, and secure spectrum utilization. This letter introduces an innovative network, ST-CSFNet, that addresses these limitations, particularly their dependence on radiation source information and their failure to capture spatiotemporal dynamics. ST-CSFNet enhances REM forecasting accuracy through multiscale temporal analysis, capturing sequence dynamics across intervals, and by assessing spatial correlations, it accurately extracts spatial characteristics at various granularities. Specifically, it reduces MAE by 37.6%, RMSE by 29.5%, and increases

$\mathrm {R}^{2}$

by 6.3%, showcasing significant advancements in REM forecasting accuracy.

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ST-CSFNet：面向无线电环境图预报的时空交叉比例尺FNet

无线电环境图（REMs）预测对无线通信至关重要，但现有方法存在明显差距，阻碍了高效、可靠和安全的频谱利用。这封信介绍了一个创新的网络ST-CSFNet，它解决了这些限制，特别是它们对辐射源信息的依赖以及它们无法捕捉时空动态。ST-CSFNet通过多尺度时间分析、捕捉序列动态、评估空间相关性，准确提取不同粒度的空间特征，提高REM预测精度。具体来说，它将MAE降低了37.6%，RMSE降低了29.5%，将$\ mathm {R}^{2}$提高了6.3%，显示了REM预测精度的显著提高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Communications Letters 工程技术-电信学

CiteScore

8.10

自引率

7.30%

发文量

590

审稿时长

2.8 months

期刊介绍： The IEEE Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of communication over different media and channels including wire, underground, waveguide, optical fiber, and storage channels. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of communication systems.