电磁频谱地图构建:稀疏高斯过程回归方法

Electromagnetic Spectrum Map Construction:a Sparse Gaussian Process Regression Approach

摘要

针对大面积场景电磁频谱地图构建面临的大数据量、大计算开销的挑战,提出一种基于变分推断的稀疏高斯过程回归方法,实现电磁频谱地图构建.首先,将电磁频谱地图构建问题建模为一个回归问题,利用已采集的数据拟合地理位置与对应接收信号强度之间的映射关系.其次,将该映射关系建模为一个高斯随机过程,利用高斯过程回归的非参数化特性构建预测模型.最后,仅选取部分已采集数据作为模型的数据输入,通过最大化变分分布与后验分布之间的相似性训练得到一个稀疏的高斯过程回归预测模型.仿真实验和基于实采数据的实验表明,该方法能够在显著降低计算复杂度、加快运算速度的基础上,构建得到高精度的电磁频谱地图.

Abstract

To address the challenges of massive data volume and substantial computational overhead in large area electromagnetic spectrum maps construction,a sparse Gaussian process regression method based on variational inference is proposed.First,the electromagnetic spectrum map construction problem is modeled as a regression problem,and mapping relationship between geographic location and corresponding received signal strength is fitted using collected data.Second,the mapping relationship is modeled as a Gaussian stochastic process,and the non-parametric property of Gaussian process regression is used to construct a prediction model.Finally,only part of the collected data is selected as input of the model,and a sparse Gaussian process regression prediction model is obtained by maximizing the similarity between variational distribution and posterior distribution.Simulation experiments and real data experiments show that this method can significantly reduce computational complexity and speed up operation process while obtaining high-precision electromagnetic spectrum maps.

关键词

稀疏高斯过程回归 / 频谱感知 / 电磁频谱地图构建

Key words

sparse Gaussian process regression / spectrum awareness / electromagnetic spectrum map construction

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基金资助

国家自然科学基金项目(61971473,62372456)资助.