Dynamic Distributed Algorithm for AP Association under User Random Arrivals and Departures

In this paper, we study the novel problem of optimizing AP association by maximizing the network throughput, subject to the degree bound of AP. The formulated problem is a combinatorial optimization. We resort to the Markov Chain approximation technique to design a distributed algorithm. We first ap...

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Vydané v:IOP conference series. Earth and environmental science Ročník 428; číslo 1; s. 12066 - 12073
Hlavní autori: Chen, Zhenwei, Zhang, Wenjie, Yang, Jingmin, Chen, Shengyu, Yang, Liwei, Chai Kiat, Yeo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: IOP Publishing 01.01.2020
ISSN:1755-1307, 1755-1315
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Abstract In this paper, we study the novel problem of optimizing AP association by maximizing the network throughput, subject to the degree bound of AP. The formulated problem is a combinatorial optimization. We resort to the Markov Chain approximation technique to design a distributed algorithm. We first approximate our optimal objective via Log-Sum-Exp function. Thereafter, we construct a special class of Markov Chain with steady-state distribution specify to our problem to yield a distributed solution. Furthermore, we extend the static problem setting to a dynamic environment where the users can randomly leave or join the system. Our proposed algorithm has provable performance, achieving an approximation gap of 1 η log | F | . It is simple and can be implemented in a distributed manner. Our extensive simulation results show that the proposed algorithm can converge very fast, and achieve a close-to-optimal performance with a guaranteed loss bound.
AbstractList In this paper, we study the novel problem of optimizing AP association by maximizing the network throughput, subject to the degree bound of AP. The formulated problem is a combinatorial optimization. We resort to the Markov Chain approximation technique to design a distributed algorithm. We first approximate our optimal objective via Log-Sum-Exp function. Thereafter, we construct a special class of Markov Chain with steady-state distribution specify to our problem to yield a distributed solution. Furthermore, we extend the static problem setting to a dynamic environment where the users can randomly leave or join the system. Our proposed algorithm has provable performance, achieving an approximation gap of 1 η log | F | . It is simple and can be implemented in a distributed manner. Our extensive simulation results show that the proposed algorithm can converge very fast, and achieve a close-to-optimal performance with a guaranteed loss bound.
In this paper, we study the novel problem of optimizing AP association by maximizing the network throughput, subject to the degree bound of AP. The formulated problem is a combinatorial optimization. We resort to the Markov Chain approximation technique to design a distributed algorithm. We first approximate our optimal objective via Log-Sum-Exp function. Thereafter, we construct a special class of Markov Chain with steady-state distribution specify to our problem to yield a distributed solution. Furthermore, we extend the static problem setting to a dynamic environment where the users can randomly leave or join the system. Our proposed algorithm has provable performance, achieving an approximation gap of 1 η log | F | . It is simple and can be implemented in a distributed manner. Our extensive simulation results show that the proposed algorithm can converge very fast, and achieve a close-to-optimal performance with a guaranteed loss bound.
Author Zhang, Wenjie
Chen, Zhenwei
Yang, Jingmin
Chen, Shengyu
Yang, Liwei
Chai Kiat, Yeo
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Cites_doi 10.1109/TIT.2013.2268923
10.1109/TWC.2018.2797182
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