基于内存的在线高斯过程从流频谱测量中更新无线电地图
Radio Map Updating from Streaming Spectrum Measurements via Memory-Based Online Gaussian Processes
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中文总结 AI 辅助
研究如何从流频谱测量中更新无线电地图，提出基于内存的在线稀疏变分高斯过程（M-OSVGP）方法，并用网格辅助在线诱导点选择（GOIPS）算法扩展，模拟表明该方法在多方面优于现有方法。
中文摘要 AI 辅助
无线电地图对于频谱管理和网络规划等应用至关重要，它通过频谱测量估计空间射频特性。随着频谱测量不断到来，传统的批量处理方法在更新无线电地图时计算量过大。为此，我们提出基于内存的在线稀疏变分高斯过程（M-OSVGP）方法，通过最小化混合目标来在线更新后验。为进一步改进，我们用网格辅助在线诱导点选择（GOIPS）算法扩展M-OSVGP。大量模拟表明该方法在重建精度、计算效率和不确定性量化方面优于现有方法。
英文摘要
Radio maps, which estimate spatial radio-frequency characteristics from spectrum measurements, are essential for applications such as spectrum management and network planning. With the continuous arrival of spectrum measurements, conventional batch processing methods for radio map reconstruction become computationally prohibitive, as they require reprocessing all accumulated measurements for each radio map update. To address this, we propose a memory-based online sparse variational Gaussian process (M-OSVGP) method that efficiently updates radio maps from streaming spectrum measurements. Our method employs sparse variational inference and updates the posterior online by minimizing a hybrid objective that integrates newly received measurements and a memory subset of previous ones to mitigate catastrophic forgetting. To further improve posterior approximation as measurements accumulate over spatially diverse regions, we extend M-OSVGP with a grid-assisted online inducing point selection (GOIPS) algorithm. GOIPS dynamically adapts the number and locations of inducing points based on measurement density and spatial correlation, providing a more informative inducing set while maintaining computational efficiency. Extensive simulations demonstrate the effectiveness of our proposed methods in reconstruction accuracy, computational efficiency, and uncertainty quantification, compared to existing batch and online baselines across various scenarios.
