基于图谱域移位的带限图信号重构算法

杨杰; 赵磊; 郭文彬

doi:10.16383/j.aas.c200802

基于图谱域移位的带限图信号重构算法

doi: 10.16383/j.aas.c200802

杨杰^1,,
赵磊^1,,
郭文彬^{1, 2,}

1.
北京邮电大学北京 100786
2.
通信网信息传输与分发技术重点实验室石家庄 050000

基金项目: 国家自然科学基金(61271181) 资助

详细信息

作者简介:
杨杰：北京邮电大学信息与通信工程学院博士研究生. 主要研究方向为图信号处理. E-mail: yjie934@bupt.edu.cn

赵磊：北京邮电大学信息与通信工程学院博士研究生. 主要研究方向为无线信号处理. E-mail: leizhao@bupt.edu.cn

郭文彬：北京邮电大学信息与通信工程学院教授. 主要研究方向为无线信号处理. 本文通信作者. E-mail: gwb@bupt.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2020-09-28
- 录用日期: 2021-01-26
- 网络出版日期: 2021-04-28
- 刊出日期: 2021-10-13

Graph Band-limited Signals Reconstruction Method Based Graph Spectral Domain Shifting

YANG Jie^1
,,
ZHAO Lei^1
,,
GUO Wen-Bin^{1, 2
,}

1.
Beijing University of Posts and Telecommunications, Beijing 100786
2.
Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory, Shijiazhuang 050000

Funds: Supported by National Natural Science Foundation of China (61271181)

More Information

Author Bio:
YANG Jie　Ph.D. candidate at the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications. His main research interest is graph signal processing

ZHAO Lei　Ph.D. candidate at the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications. His main research interest is wireless signal processing

GUO Wen-Bin　Professor at the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications. His main research interest is wireless signal processing. Corresponding author of this paper

摘要

摘要: 针对带限图信号的重构问题, 本文提出了基于图谱域移位的带限图信号重构模型, 该模型将图带限分量的恒等不变特性建模为最小二乘问题. 基于所提出的重构模型, 本文设计了基于谱移位的重构算法和基于残差谱移位的重构算法. 相比于其他重构算法, 两种新算法提升了迭代效率和重构精度. 此外, 本文算法还适用于分段带限图信号的重构问题, 并且具有良好的迭代效率和重构精度.通过实验仿真表明, 相比于目前其他的带限图信号重构算法, 新算法的迭代效率提升约70%和重构精度提升约60%.
- 图信号处理 /
- 信号重构 /
- 图谱理论 /
- 位移算子
Abstract: Aiming at the problem of graph band-limited signals reconstruction, a novel reconstruction model based shift strategy in graph spectral domain is proposed in this paper, and it models the identity invariance of graph spectral band-limited components as a least-square problem. For solving the established reconstruction model, two novel reconstruction methods are proposed based on spectral shift operator and residual spectral shift operator. Compared with other methods, the novel methods improve iteration efficiency and reconstruction accuracy. Besides, the novel methods are suitable for the problem of separate band-limited graph signals reconstruction and have good performances. The simulation shows that compared with other reconstruction methods of band-limited graph signals, the novel methods improve about seventy percent in iterative efficiency and sixty percent of reconstruction accuracy.
- Graph signal processing /
- signal reconstruction /
- graph spectral theory /
- shift operator

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图 1 带限图信号

Fig. 1 Graph band-limited signals

下载: 全尺寸图片幻灯片

图 2 分段带限图信号

Fig. 2 Graph sperate band-limited signals

下载: 全尺寸图片幻灯片

图 3 图信号采样

Fig. 3 Graph signals sampling

下载: 全尺寸图片幻灯片

图 4 无噪环境下带限图信号重构性能对比

Fig. 4 Comparison of graph band-limited signals reconstruction performances in noiseless environment

下载: 全尺寸图片幻灯片

图 5 含噪环境下带限图信号重构性能对比

Fig. 5 Comparison of graph band-limited signals reconstruction performances in noisy environment

下载: 全尺寸图片幻灯片

图 6 分段带限图信号重构性能对比

Fig. 6 Comparison of graph separate band-limited signals reconstruction performances

下载: 全尺寸图片幻灯片

表 1 无噪情况下基于随机采样的${G_1}$重构效率

Table 1 ${G_1}$ reconstruction efficiency of random sampling in noiseless

算法	迭代次数	运行时间 (s)
ILSR	220	139.99
OPGIR	114	108.78
IPR	96	61.87
IGDR	33	20.47
BGSR-GFS	27	5.73
BGSR-GFS-R	8	8.97

下载: 导出CSV

表 2 无噪情况下基于随机采样的${G_2}$重构效率

Table 2 ${G_2}$ reconstruction efficiency of random sampling in noiseless

算法	迭代次数	运行时间 (s)
ILSR	269	0.1509
OPGIR	139	0.1291
IPR	64	0.0405
IGDR	34	0.0271
BGSR-GFS	7	0.0065
BGSR-GFS-R	5	0.0146

下载: 导出CSV

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