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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
Article
  • 66 Downloads

Abstract

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.

Keywords

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

Mathematics Subject Classification

90C06 90C25 90C47 

Notes

Acknowledgements

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.

References

  1. 1.
    Zhao, Y., Jiang, H., Zhou, K., et al.: Dream-(l) g: a distributed grouping-based algorithm for resource assignment for bandwidth-intensive applications in the cloud. IEEE Trans. Parallel Distrib. 27(12), 3469–3484 (2016)CrossRefGoogle Scholar
  2. 2.
    Dai, X., Wang, X., Liu, N.: Optimal scheduling of data-intensive applications in cloud-based video distribution services. IEEE Trans. Circuits Syst. Video Technol. 27(1), 73–83 (2017)CrossRefGoogle Scholar
  3. 3.
    Zhang, H., Liu, H., Cheng, J., et al.: Downlink energy efficiency of power allocation and wireless backhaul bandwidth allocation in heterogeneous small cell networks. IEEE Trans. Commun. 66(4), 1705–1716 (2018)CrossRefGoogle Scholar
  4. 4.
    López-Pérez, D., Chu, X., Vasilakos, A.V., et al.: Power minimization based resource allocation for interference mitigation in OFDMA femtocell networks. IEEE J. Sel. Area Commun. 32(2), 333–344 (2014)CrossRefGoogle Scholar
  5. 5.
    Jiang, C., Zhang, H., Ren, Y., et al.: Energy-efficient non-cooperative cognitive radio networks: micro, meso, and macro views. IEEE Commun. Mag. 52(7), 14–20 (2014)CrossRefGoogle Scholar
  6. 6.
    Xu, C., Sheng, M., Yang, C., et al.: Pricing-based multiresource allocation in OFDMA cognitive radio networks: an energy efficiency perspective. IEEE Trans. Veh. Technol. 63(5), 2336–2348 (2014)CrossRefGoogle Scholar
  7. 7.
    Yang, K., Ou, S., Guild, K., Chen, H.: Convergence of ethernet PON and IEEE 802.16 broadband access networks and its QoS-aware dynamic bandwidth allocation scheme. IEEE J. Sel Area Commun. 27(2), 101–116 (2009)CrossRefGoogle Scholar
  8. 8.
    Delicado, F.M., Cuenca, P., Orozco-BarbosaL, L.: QoS mechanisms for multimedia communications over TDMA/TDD WLANs. Comput. Commun. 29(13–14), 2721–2735 (2006)CrossRefGoogle Scholar
  9. 9.
    Treust, M., Lasaulce, S.: A repeated game formulation of energy-efficient decentralized power control. IEEE Trans. Commun. 9(9), 2860–2869 (2010)Google Scholar
  10. 10.
    Buzzi, S., Saturnino, D.: A game-theoretic approach to energy-efficient power control and receiver design in cognitive CDMA wireless networks. IEEE J. STSP 5(1), 137–150 (2011)Google Scholar
  11. 11.
    Yuan, W., Wang, P., Liu, W., et al.: Variable-width channel allocation for access points: a game-theoretic perspective. IEEE Trans. Mobile Comput. 12(7), 1428–1442 (2013)CrossRefGoogle Scholar
  12. 12.
    Song, T., Kim, T.Y., Kim, W., et al.: Adaptive and distributed radio resource allocation in densely deployed wireless LANs: a game-theoretic approach. IEEE Trans Veh. Technol. 67(5), 4466–4475 (2018)CrossRefGoogle Scholar
  13. 13.
    Lei, J., Chen, H.F., Fang, H.T.: Primal-dual algorithm for distributed constrained optimization. Syst. Control Lett. 96, 110–117 (2016)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Xi, C., Khan, U.A.: Distributed subgradient projection algorithm over directed graphs. IEEE Trans. Automat. Contr. 62(8), 3986–3992 (2017)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Cai, K., Ishii, H.: Average consensus on general strongly connected digraphs. Automatica 48(11), 2750–2761 (2012)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Xi, C., Mai, V.S., Xin, R., et al.: Linear convergence in optimization over directed graphs with row-stochastic matrices. IEEE Trans. Automat. Contr. 63(10), 3558–3565 (2018)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Nedić, A., OlshevskyA, A.: Distributed optimization over time-varying directed graphs. IEEE Trans. Automat. Contr. 60(3), 601–615 (2015)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Deming, Y., Shengyuan, X., Junwei, L.: Gradient-free method for distributed multi-agent optimization via push-sum algorithms. Int. J. Robust Nonlin. 25, 1569–1580 (2015)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Nedić, A., Olshevsky, A.: Stochastic gradient-push for strongly convex functions on time-varying directed graphs. IEEE Trans. Automat. Contr. 61(12), 3936–3947 (2016)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Xiuxian, L., Xinlei, Y., Lihua, X.: Distributed online optimization for multi-agent networks with coupled inequality constraints. arXiv preprint arXiv.1805.05573 (2018)
  21. 21.
    Nedić, A., Ozdaglar, A., Parrilo, P.A.: Constrained consensus and optimization in multi-user networks. IEEE Trans. Automat. Contr. 55(4), 922–938 (2010)CrossRefGoogle Scholar
  22. 22.
    Khan, S.U., Ahmad, I.: Comparison and analysis of ten static heuristics-based Internet data replication techniques. J. Parallel Distrib. Comput. 68(2), 113–136 (2008)CrossRefGoogle Scholar

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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