A multi-agent approach to optimal channel assignment in WLANs

Monteiro, Tania L.; Pujolle, Guy; Pellenz, Marcelo Eduardo; Penna, Manoel Camillo; Souza, Richard Demo

doi:10.1109/wcnc.2012.6214246

Cited by 16 publications

(9 citation statements)

References 11 publications

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“…In a cooperative channel assignment problem (Hollos, Karl, & Wolisz, 2004;Monteiro, Pellenz, Penna, Enembreck, Souza, & Pujolle, 2012a;Monteiro, Pujolle, Pellenz, Penna, Enembreck, & Demo Souza, 2012b), APs need to be configured in order to reduce the overall interference between simultaneous transmissions on neighboring channels. Monteiro et al (2012aMonteiro et al ( , 2012b proposed a DCOP-based approach for cooperative channel assignment in WLANs where APs may belong to different administration entities. In the proposed model, each AP is represented by a DCOP agent, which controls a decision variable modeling a choice for the AP's channels.…”

Section: Radio Frequency Allocation Problemsmentioning

confidence: 99%

Distributed Constraint Optimization Problems and Applications: A Survey

Fioretto¹,

Pontelli²,

Yeoh³

2018

jair

144

119

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The field of multi-agent system (MAS) is an active area of research within artificial intelligence, with an increasingly important impact in industrial and other real-world applications. In a MAS, autonomous agents interact to pursue personal interests and/or to achieve common objectives. Distributed Constraint Optimization Problems (DCOPs) have emerged as a prominent agent model to govern the agents' autonomous behavior, where both algorithms and communication models are driven by the structure of the specific problem. During the last decade, several extensions to the DCOP model have been proposed to enable support of MAS in complex, real-time, and uncertain environments.This survey provides an overview of the DCOP model, offering a classification of its multiple extensions and addressing both resolution methods and applications that find a natural mapping within each class of DCOPs. The proposed classification suggests several future perspectives for DCOP extensions, and identifies challenges in the design of efficient resolution algorithms, possibly through the adaptation of strategies from different areas.

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Section: Radio Frequency Allocation Problemsmentioning

confidence: 99%

Distributed Constraint Optimization Problems and Applications: A Survey

Fioretto¹,

Pontelli²,

Yeoh³

2018

jair

144

119

View full text Add to dashboard Cite

show abstract

“…As a fundamental formalism in multi-agent systems, Distributed Constraint Optimization Problems (DCOPs) (Modi et al 2005) capture the essentials of cooperative distributed problem solving and have been successfully applied to model the problems in many real-world domains like radio channel allocation (Monteiro et al 2012), vessel navigation (Hirayama et al 2019), and smart grid (Fioretto et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Pretrained Cost Model for Distributed Constraint Optimization Problems

Deng

Kong

2022

AAAI

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Distributed Constraint Optimization Problems (DCOPs) are an important subclass of combinatorial optimization problems, where information and controls are distributed among multiple autonomous agents. Previously, Machine Learning (ML) has been largely applied to solve combinatorial optimization problems by learning effective heuristics. However, existing ML-based heuristic methods are often not generalizable to different search algorithms. Most importantly, these methods usually require full knowledge about the problems to be solved, which are not suitable for distributed settings where centralization is not realistic due to geographical limitations or privacy concerns. To address the generality issue, we propose a novel directed acyclic graph representation schema for DCOPs and leverage the Graph Attention Networks (GATs) to embed graph representations. Our model, GAT-PCM, is then pretrained with optimally labelled data in an offline manner, so as to construct effective heuristics to boost a broad range of DCOP algorithms where evaluating the quality of a partial assignment is critical, such as local search or backtracking search. Furthermore, to enable decentralized model inference, we propose a distributed embedding schema of GAT-PCM where each agent exchanges only embedded vectors, and show its soundness and complexity. Finally, we demonstrate the effectiveness of our model by combining it with a local search or a backtracking search algorithm. Extensive empirical evaluations indicate that the GAT-PCM-boosted algorithms significantly outperform the state-of-the-art methods in various benchmarks.

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“…there is no research of modeling user association problem as MAS. However, some similar works have been done focusing on solving the resource management problem in the field of wireless networks or cognitive radio network [23], [24], [25] by DCOP framework. These methods can not be directly applied to user association problem mainly due to the scale of the models for these practical applications is relatively small.…”

Section: Introductionmentioning

confidence: 99%

“…These methods can not be directly applied to user association problem mainly due to the scale of the models for these practical applications is relatively small. For instance, Monteiro [24] formalized the channel allocation in a wireless network as a DCOP with no more than 10 agents considered in the simulation parts. However, the amount of users and resource included in a HetNet is always hundreds and thousands.…”

Section: Introductionmentioning

confidence: 99%

Applying DCOP to User Association Problem in Heterogeneous Networks with Markov Chain Based Algorithm

Duan¹,

Mao²,

Zhang³

2017

Preprint

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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-η (Each Connection As Variable) model in which a parameter η with an adequate assignment (η = 3 in this paper) is able to control the scale of the model. After that, a Markov chain (MC) based algorithm is proposed on the basis of log-sum-exp function. Experimental results show that the solution obtained by DCOP framework is better than the one obtained by the Max-SINR algorithm. Comparing with the Lagrange dual decomposition based method (LDD), the solution performance has been improved since there is no need to transform original problem into a satisfied one. In addition, it is also apparent that the DCOP based method has better robustness than LDD when the number of users increases but the available resource at base stations are limited.

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A multi-agent approach to optimal channel assignment in WLANs

Cited by 16 publications

References 11 publications

Distributed Constraint Optimization Problems and Applications: A Survey

Distributed Constraint Optimization Problems and Applications: A Survey

Pretrained Cost Model for Distributed Constraint Optimization Problems

Applying DCOP to User Association Problem in Heterogeneous Networks with Markov Chain Based Algorithm

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