Applying Distributed Constraint Optimization Approach to the User Association Problem in Heterogeneous Networks

Applying Distributed Constraint Optimization Approach to the User Association Problem in Heterogeneous Networks
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将分布式约束优化方法应用于异构网络中的用户关联问题

DOI:
10.1109/tcyb.2017.2713387
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发表时间:
2018-06
影响因子:
11.8
通讯作者:
Bin Zhang
Bin Zhang
中科院分区:
计算机科学1区
文献类型:
--
作者:
Peibo Duan;Changsheng Zhang;Guoqiang Mao;Bin Zhang

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用户关联已成为异质网络(HETNETS)中的分布式资源分配问题。尽管可以使用组合优化和基于游戏理论的方案等方法获得近似解决方案,但是这些TEC
User association has emerged as a distributed resource allocation problem in the heterogeneous networks (HetNets). Although an approximate solution is obtainable using the approaches like combinatorial optimization and game theory-based schemes, these techniques can be easily trapped in local optima. Furthermore, the lack of exploring the relation between the quality of the solution and the parameters in the HetNet [e.g., the number of users and base stations (BSs)], at what levels, impairs the practicability of deploying these approaches in a real world environment. To address these issues, this paper investigates how to model the problem as a distributed constraint optimization problem (DCOP) from the point of the view of the multiagent system. More specifically, we develop two models named each connection as variable (ECAV) and each BS and user as variable (EBUAV). Hereinafter, we propose a DCOP solver which not only sets up the model in a distributed way but also enables us to efficiently obtain the solution by means of a complete DCOP algorithm based on distributed message-passing. Naturally, both theoretical analysis and simulation show that different qualitative solutions can be obtained in terms of an introduced parameter ${\eta }$ which has a close relation with the parameters in the HetNet. It is also apparent that there is 6% improvement on the throughput by the DCOP solver comparing with other counterparts when ${\eta =3}$ . Particularly, it demonstrates up to 18% increase in the ability to make BSs service more users when the number of users is above 200 while the available resource blocks (RBs) are limited. In addition, it appears that the distribution of RBs allocated to users by BSs is better with the variation of the volume of RBs at the macro BS.
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