Telecommunication Systems

, Volume 66, Issue 4, pp 639–655 | Cite as

Cross-layer optimization using two-level dual decomposition in multi-flow ad-hoc networks



The conventional protocol architectures based on rigid and inflexible layering principles are not suitable to meet the key challenges posed by ad-hoc networks. To overcome the performance limitations caused by lack of coordination between layers, the communication protocols crossing different layers need to be optimized jointly. In this paper, we explore a novel cross-layer framework for joint optimization of congestion control, routing, contention control, and power control in multi-flow ad-hoc networks. Accordingly, a generalized mathematical model is developed for cross-layer optimization formulation that can seamlessly address these issues with the aid of network utility maximization. Convexity is essential to attain a global optimum solution for the optimization problem in its feasible region. Therefore, the original non-convex optimization problem is convexified through suitable logarithmic transformations. Then, we employ a two-level dual decomposition technique for relaxing the interlayer coupling constraints in the network. In the first level, the problem is vertically decomposed into four disjoint subproblems that are solved independently across transport, network, MAC and physical layers. In the second level, a horizontal decomposition is applied within the physical layer. Each of the four subproblems is jointly correlated through the set of Lagrangian dual variables that control the interlayer coupling. Subsequently, the subgradient projection procedure is used to solve the associated dual problem. For this, a distributed iterative algorithm is proposed to implement the cross-layer approach with static channels and multiple source–destination pairs. Simulation results and analyses depict the global convergence of the convex optimization problem to a unique optimal solution. We also present the convergence process of the dual variables that act as bridges connecting and coordinating the four subproblems. Finally, we compare our proposed cross-layer algorithm with two previous algorithms employing cross-layer deign of two and three layers. Numerical results show that our algorithm offers considerable performance enhancement over the previous work in terms of throughput, persistence probability, and power consumption.


Ad-hoc network Cross-layer optimization Network utility maximization Two-level dual decomposition 



Ridhima Mehta is thankful to the University Grants Commission (UGC), New Delhi for providing necessary fellowship. We are also thankful to Department of Science and Technology (DST) for support through PURSE Grant.


  1. 1.
    Wang, X., & Kar, K. (2005). Cross-layer rate control for end-to-end proportional fairness in wireless networks with random access. In Proceedings of the of ACM MOBICOM ’05.Google Scholar
  2. 2.
    Lee, J. W., Chiang, M., & Calderbank, R. A. (2006). Jointly optimal congestion and contention control in wireless ad hoc networks. IEEE Communications Letters, 10(3), 216–218.CrossRefGoogle Scholar
  3. 3.
    Chiang, M. (2005). Balancing transport and physical layers in wireless multihop networks: Jointly optimal congestion control and power control. IEEE Journal on Selected Areas in Communications, 23(1), 104–116. (Special Issue on wireless ad-hoc networks) .CrossRefGoogle Scholar
  4. 4.
    Ramesh, B., et al. (2012). Optimizing congestion in wireless ad-hoc networks. International Journal of Advanced Research in Computer Science and Software Engineering, 2(9), 356–361.Google Scholar
  5. 5.
    Tang, Z., He, J., Zhang, Y., & Fan, Z. (2015). Achievable performance gain of IEEE 802.11 multi-rate link adaptation algorithm with cross-layer design. International Journal of Autonomous and Adaptive Communications Systems, 8(1), 42–59.CrossRefGoogle Scholar
  6. 6.
    Shabdanov, S., Mitran, P., & Rosenberg, C. (2012). Cross-layer optimization using advanced physical layer techniques in wireless mesh networks. IEEE Transactions on Wireless Communications, 11(4), 1622–1631.CrossRefGoogle Scholar
  7. 7.
    Amerimehr, M. H., Khalaj, B. H., & Crespo, P. M. A. (2008). Distributed cross-layer optimization method for multicast in interference-limited multihop wireless networks. EURASIP Journal on Wireless Communications and Networking, 2008, 702036.CrossRefGoogle Scholar
  8. 8.
    Sadek, R., Youssif, A., & Elaraby, A. (2015). MPEG-4 video transmission over IEEE 802.11e wireless mesh networks using dynamic-cross-layer approach. National Academy Science Letters, 38, 113–119.CrossRefGoogle Scholar
  9. 9.
    Zareei, M., Muzahidul Islam, A. K. M., Mansoor, N., Baharun, S., Mohamed, E. M., & Sampei, S. (2016). CMCS: A cross-layer mobility aware MAC protocol for cognitive radio sensor networks. EURASIP Journal on Wireless Communications and Networking, 2016, 160.CrossRefGoogle Scholar
  10. 10.
    Le, T. A., Nguyen, H., & Nguyen, M. C. (2015). Application-network cross layer multi-variable cost function for application layer multicast of multimedia delivery over convergent networks. Wireless Networks. doi: 10.1007/s11276-015-0940-1.
  11. 11.
    Ghasemi, A., & Faez, K. (2007). Jointly rate and power control in contention based multihop wireless networks. Computer Communications, 30(9), 2021.CrossRefGoogle Scholar
  12. 12.
    Zheng, V. W. C., Zhang, X. M., Liu, D. K., & Sung, D. K. (2007). A joint power control, link scheduling and rate control algorithm for wireless ad hoc networks. In Proceedings of IEEE wireless communications and networking conference (WCNC), Hong Kong.Google Scholar
  13. 13.
    Chen, L., Low, S. H., Chiang, M., & Doyle, J. C. (2006). Cross-layer congestion control, routing and scheduling design in ad hoc wireless networks. In Proceedings of 25th IEEE international conference on computer communications (pp. 1–13).Google Scholar
  14. 14.
    Leinonen, M., Karjalainen, J., & Juntti, M. (2011). Distributed power and routing optimization in single-sink data gathering wireless sensor networks. In Proceedings of 19th European signal processing conference (EUSIPCO 2011) (pp. 407–411). Barcelona, Spain. August 29, September 2.Google Scholar
  15. 15.
    Zhou, L., Zheng, B., Geller, B., Wei, A., Xu, S., & Li, Y. (2008). Cross-layer rate control, medium access control and routing design in cooperative VANET. Computer Communications, 31(12), 2870–2882.CrossRefGoogle Scholar
  16. 16.
    Wang, X., & Garcia-Luna-Aceves, J. J. (2011). Collaborative routing, scheduling and frequency assignment for wireless ad hoc networks using spectrum-agile radios. Wireless Networks, 17(1), 167–181.Google Scholar
  17. 17.
    Zhou, L., Geller, B., Zheng, B., Wei, A., & Cui, J. (2009). System scheduling for multi-description video streaming over wireless multi-hop networks. IEEE Transactions on Broadcasting, 55(4), 731–741.CrossRefGoogle Scholar
  18. 18.
    Liu, J. S., Lin, C. H. R., & Tung, K. Y. (2010). Cross-layer design for end-to-end throughput maximization and fairness in MIMO multihop wireless networks. EURASIP Journal on Wireless Communications and Networking, 2010, 515609.Google Scholar
  19. 19.
    Debbarma, J., Roy, S., & Pal, R. K. (2012). Cross-Layer design approach with power consciousness for mobile ad-hoc networks. International Journal of Wireless & Mobile Networks (IJWMN), 4(3), 51.CrossRefGoogle Scholar
  20. 20.
    Dobslaw, F., Zhang, T., & Gidlund, M. (2016). QoS-aware cross-layer configuration for industrial wireless sensor networks. IEEE Transactions on Industrial Informatics, 12(5), 1679–1691.CrossRefGoogle Scholar
  21. 21.
    Liu, J. S., & Richard Lin, C. H. (2014). Cross-layer optimization for performance trade-off in network code-based wireless multi-hop networks. Computer Communications, 52, 89–101.CrossRefGoogle Scholar
  22. 22.
    Goldsmith, A. J. (2005). Wireless communications. New York: Cambridge University Press.CrossRefGoogle Scholar
  23. 23.
    Grant, M., & Boyd, S. (2011). CVX: Matlab software for disciplined convex programming, version 1.21, build 808.
  24. 24.
    Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  25. 25.
    Bazaraa, M. S., Sherali, H. D., & Shetty, C. M. (2006). Nonlinear programming: Theory and algorithms (3rd ed.). New York: Wiley.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Computer and Systems SciencesJawaharlal Nehru UniversityNew DelhiIndia

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