Channel Interference Measurement and Optimization Based on Link Conflict Graph Embedding

doi:10.3969/j.issn.1671-1122.2022.09.009

Abstract

Abstract:

In order to solve the problem that the existing channel measurement and optimization methods ignore the link state, which leads to the failure to prioritize the reduction of the actual link interference, a dynamic channel based on link conflict graph embedding was proposed for large-scale wireless network environments such as campus networks. Interference measurement and optimization methods. In the measurement phase, link status and adjustment coefficients were introduced to classify the interference between different links, and a link conflict graph based on the channel interference classification was constructed. At the same time, the graph embeding algorithm was used to embed the link conflict graph, and the overall interference situation of the wireless network was described by the embedded vector, which improved the accuracy of the modeling. The partial sampling method based on sliding window was adopted to accurately reflect the interference of the link at a low sampling rate. In the network optimization stage, the optimization algorithm based on the maximum cut problem was used to calculate the optimal wireless network channel configuration strategy, which effectively reduced the interference on the actual link.

Key words: conflict graph, graph embedding, channel measurement, channel optimization

CLC Number:

TP309

LIANG Yan, LI Dong, ZHAO Yizhu, YU Junqing. Channel Interference Measurement and Optimization Based on Link Conflict Graph Embedding[J]. Netinfo Security, 2022, 22(9): 76-85.

Figures/Tables 18

References 16

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	S₂→A₂	S₄→A₂	A₂→S₂	A₂→S₄
S₂→A₂	—	1	—	1
S₄→A₂	1	—	1	—
A₂→S₂	—	1	—	1
A₂→S₄	1	—	1	—

	S₁→A₁	S₂→A₂	A₁→S₁	A₂→S₂
S₁→A₁	—	1	—	0
S₂→A₂	1	—	1	—
A₁→S₁	—	1	—	0
A₂→S₂	0	—	0	—

	S₂→A₂	S₃→A₃	A₂→S₂	A₃→S₃
S₂→A₂	—	0	—	0
S₃→A₃	0	—	1	—
A₂→S₂	—	1	—	0
A₃→S₃	0	—	0	—

项目	描述
操作系统	Windows 10
CPU	Intel(R) Core(TM) i7-9700 CPU @3.00 GHz
内存	16 GB

项目	描述
镜像配置	Jupyter-cuda
CPU	Intel Xeon Gold 6230R
GPU	NVIDIA Tesla V100S [32 GB]
内存	16 GB