Journal of Optimization Theory and Applications

, Volume 183, Issue 3, pp 1153–1176 | Cite as

An Improved Distributed Gradient-Push Algorithm for Bandwidth Resource Allocation over Wireless Local Area Network

  • Zhengqing Shi
  • Chuan ZhouEmail author


Bandwidth allocation problems over wireless local area network have attracted extensive research recently due to the rapid growth in the number of users and bandwidth-intensive applications. In this paper, a bandwidth allocation problem over wireless local area network with directed topologies is investigated and the global objective function of the problem consists of local downloading and uploading cost with both constraints of feasible allocation region and network resources. An improved high-efficiency gradient-push algorithm is proposed for the bandwidth allocation problem which not only guarantees successful data transmission but also minimizes the global objective function. Compared with the existing distributed algorithms, firstly, we use weighted running average bandwidth to replace the current state variables which can ensure the solution converge to the optimal value asymptotically with probability one. Next, noisy gradient samples are used in the proposed algorithm instead of accurate gradient information which enhances the robustness and expands the scope of application. Theoretical analysis shows the convergence rate of the time-averaged value to the optimal solution. Finally, numerical examples are presented to validate the proposed algorithm.


Bandwidth-intensive applications Bandwidth allocation Distributed optimization Directed communication topology Noisy gradient sample Push-sum protocol 

Mathematics Subject Classification

90C06 90C25 90C47 



This research is supported by National Natural Science Foundation of China under Grant nos. 61673219 and 61673214, 13th Five-Year Plan for Equipment Pre-research on Common Technology under Grant no. 41412040101, Tianjin Major Projects of Science and Technology under Grant no. 15ZXZNGX00250.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of AutomationNanjing University of Science and TechnologyNanjingPeople’s Republic of China

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