Distributed algorithm for AP association with random arrivals and departures of users

• Published in: IET communications Vol. 14; no. 5; pp. 846 - 856
• Main Authors: Chen, Zhenwei, Zhang, Wenjie, Zheng, Yifeng, Yang, Liwei, Yeo, ChaiKiat
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 17.03.2020
• Subjects: access point association; AP association; approximation theory; combinatorial mathematics; combinatorial optimisation; distributed algorithm; distributed algorithms; Log‐Sum‐Exp function; Markov Chain approximation technique; Markov processes; optimisation; random arrivals; Research Article; static problem; steady‐state distribution; user departures; wireless LAN; wireless local area networks; WLAN; approximation theory; combinatorial mathematics; wireless local area networks; user departures; WLAN; combinatorial optimisation; AP association; distributed algorithm; Log-Sum-Exp function; optimisation; access point association; static problem; distributed algorithms; Markov Chain approximation technique; Markov processes; random arrivals; wireless LAN; steady-state distribution
• ISSN: 1751-8628; 1751-8636; 1751-8636
• DOI: 10.1049/iet-com.2019.0817

Here, the authors study the novel problem of optimising access point (AP) association by maximising the network throughput, subject to the degree bound of AP. The formulated problem is a combinatorial optimisation. They resort to the Markov Chain approximation technique to design a distributed algorithm. They first approximate their optimal objective via Log-Sum-Exp function. Thereafter, they construct a special class of Markov Chain with steady-state distribution specify to their problem to yield a distributed solution. Furthermore, they extend the static problem setting to a dynamic environment where the users can randomly leave or join the system. Their proposed algorithm has provable performance, achieving an approximation gap of $({1}/{\eta})\log |\mathscr {F}|$(1/η)log⁡|F|. It is simple and can be implemented in a distributed manner. Their extensive simulation results show that the proposed algorithm can converge very fast, and achieve a close-to-optimal performance with a guaranteed loss bound.