Applying DCOP to User Association Problem in Heterogeneous Networks with Markov Chain Based Algorithm
Peibo Duan, Guoqiang Mao, Changsheng Zhang, Bin Zhang

TL;DR
This paper introduces a DCOP-based framework with a Markov chain algorithm for user association in HetNets, improving solution quality and robustness over traditional methods.
Contribution
It proposes an ECAV-$ ext{ exteta}$ model for scalable DCOP application and a Markov chain algorithm, enhancing performance and robustness in user association tasks.
Findings
DCOP framework outperforms Max-SINR algorithm.
Proposed method improves over Lagrange dual decomposition (LDD).
Better robustness with increasing users.
Abstract
Multi-agent systems (MAS) is able to characterize the behavior of individual agent and the interaction between agents. Thus, it motivates us to leverage the distributed constraint optimization problem (DCOP), a framework of modeling MAS, to solve the user association problem in heterogeneous networks (HetNets). Two issues we have to consider when we take DCOP into the application of HetNet including: (i) How to set up an effective model by DCOP taking account of the negtive impact of the increment of users on the modeling process (ii) Which kind of algorithms is more suitable to balance the time consumption and the quality of soltuion. Aiming to overcome these issues, we firstly come up with an ECAV- (Each Connection As Variable) model in which a parameter with an adequate assignment ( in this paper) is able to control the scale of the model. After that, a Markov…
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Taxonomy
TopicsConstraint Satisfaction and Optimization · Scheduling and Timetabling Solutions · Data Management and Algorithms
