Research Article
Graph-Based Terminal Ranking for Sparsification of Interference Coordination Parameters in Ultra-Dense Networks
@INPROCEEDINGS{10.1007/978-3-030-64002-6_12, author={Junxiang Gou and Lusheng Wang and Hai Lin and Min Peng}, title={Graph-Based Terminal Ranking for Sparsification of Interference Coordination Parameters in Ultra-Dense Networks}, proceedings={Mobile Networks and Management. 10th EAI International Conference, MONAMI 2020, Chiba, Japan, November 10--12, 2020, Proceedings}, proceedings_a={MONAMI}, year={2020}, month={12}, keywords={Ultra-dense networks Inter-cell interference coordination Sparsification Hamiltonian path Approximation algorithm}, doi={10.1007/978-3-030-64002-6_12} }- Junxiang Gou
Lusheng Wang
Hai Lin
Min Peng
Year: 2020
Graph-Based Terminal Ranking for Sparsification of Interference Coordination Parameters in Ultra-Dense Networks
MONAMI
Springer
DOI: 10.1007/978-3-030-64002-6_12
Abstract
In the future mobile communication system, inter-cell interference becomes a serious problem due to the intensive deployment of cells and terminals. Traditional interference coordination schemes take long time for optimization in ultra-dense networks. Meanwhile, due to the increase of factors affecting communication and in order to better meet the communication needs of each terminal, an interference coordination scheme needs to fully consider multiple characteristic parameters of the terminal, which will further increase the scheme’s computational time. Therefore, we should compress all the data through sparsification of parameters before optimization. There are many terminal parameters, and the essence of sparsification of parameters is to rank terminals. In this paper, a graph-based terminal ranking scheme is designed. First, each terminal can be represented by its multiple parameters. Then, all terminals are used as the vertexes in the graph to form a complete weighted graph, and edge weights represent the degree of dissimilarity between terminals. A ranking of terminals is obtained by finding a minimum Hamiltonian path in the graph. Finally, the ranking of all parameter sequences is obtained according to terminals ranking, which makes the sparsity of all parameter sequences better. Simulation results show that the proposed scheme can accomplish sparsification of parameter sequences effectively, especially when the number of sequences increases. In addition, compared with the optimal coordination of traditional scheme, this scheme improves the fairness of the system while ensuring high system capacity, and dramatically reduces the computational time of interference coordination.
