Channel Gain Cartography for Cognitive Radios Leveraging Low Rank and Sparsity
Channel Gain Cartography for Cognitive Radios Leveraging Low Rank and Sparsity
复制标题
DOI：
10.1109/twc.2017.2717822
复制
发表时间：
2017-09-01
影响因子：
10.4
通讯作者：
Giannakis, Georgios B.
中科院分区：
文献类型：
--
作者：
Lee, Donghoon;Kim, Seung-Jun;Giannakis, Georgios B.
信道增益制图的目的是根据部署在该区域的一组无线电收集的增益测量值（样本）推断空间中任意两点之间的信道增益。信道增益图对于认知无线电网络的操作所必需的各种感测和资源分配任务是有用的。在本文中，信道增益被建模为一个潜在的空间损失场（SLF），它捕获的信号强度的衰减，由于在传播路径中的障碍物的层析积累。为了估计地图准确地与相对较少的测量数量，SLF被假定为具有低秩结构可能与稀疏偏差。高效的批处理和在线算法推导出的地图重建问题。综合测试与合成和真实的数据集证实，该算法可以准确地揭示传播介质的结构，并产生所需的信道增益图。
Channel gain cartography aims at inferring the channel gains between two arbitrary points in space based on the measurements (samples) of the gains collected by a set of radios deployed in the area. Channel gain maps are useful for various sensing and resource allocation tasks essential for the operation of cognitive radio networks. In this paper, the channel gains are modeled as the tomographic accumulations of an underlying spatial loss field (SLF), which captures the attenuation in the signal strength due to the obstacles in the propagation path. In order to estimate the map accurately with a relatively small number of measurements, the SLF is postulated to have a low-rank structure possibly with sparse deviations. Efficient batch and online algorithms are derived for the resulting map reconstruction problem. Comprehensive tests with both synthetic and real data sets corroborate that the algorithms can accurately reveal the structure of the propagation medium, and produce the desired channel gain maps.