Skip to main content

Advertisement

Log in

Multi-layer optimization with backpressure and genetic algorithms for multi-hop wireless networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

This paper presents an efficient scheme to optimize multiple layers in multi-hop wireless networks with throughput objectives. Considering channel sensing and power control at the physical layer, a non-convex throughput optimization problem is formulated for resource allocation and a genetic algorithm is designed to allow distributed implementation. To address link and network layers, a localized back-pressure algorithm is designed to make routing, scheduling, and frequency band assignments along with physical-layer considerations. Our multi-layer scheme is extended to cognitive radio networks with different user classes and evaluate our analytical solution via simulations. Hardware-in-the-loop emulation test results obtained with real radio transmissions over emulated channels are presented to verify the performance of our distributed multi-layer optimization solution for multi-hop wireless networks. Finally, a security system is considered, where links have their security levels and data flows require certain security levels on each of its links. This problem is addressed by formulating additional constraints to the optimization problem.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. The value of m should be set appropriately. If m is too small, sensing results may not be accurate enough; but if m is too large, the transmission stage is too short to achieve a large throughput. An appropriate value can be determined off-line by comparing the performance with different m values.

References

  1. Alicherry, M., Bhatia, R., & Li, L. (2005). Joint channel assignment and routing for throughput optimization in multi-radio wireless mesh networks. In Proceedins of ACM MobiCom, Cologne, Germany, Augest 28–September 2.

  2. Andrews, M., & Dinitz, M. (2009). Maximizing capacity in arbitrary wireless networks in the SINR model: Complexity and game theory. In Proceedings of IEEE INFOCOM, Rio de Janeiro, Brazil, April 19–25, pp. 1332–1340.

  3. Bhatia, R., & Li, L. (2007). Throughput optimization of wireless mesh networks with MIMO links. In Proceedings of IEEE INFOCOM, Anchorage, AK, pp. 2326–2330.

  4. Gao, C., Shi, Y., Hou, Y. T., & Kompella, S. (2011). On the throughput of MIMO-empowered multi-hop cognitive radio networks. IEEE Transactions on Mobile Computing, 10(11), 1505–1519.

    Article  Google Scholar 

  5. Shi, Y., Hou, Y. T., Kompella, S., & Sherali, H. D. (2011). Maximizing capacity in multi-hop cognitive radio networks under the SINR model. IEEE Transactions on Mobile Computing, 10(7), 954–967.

    Article  Google Scholar 

  6. Yuan, Y., Bahl, P., Chandra, R., Moscibroda, T., & Wu, Y. (2007). Allocating dynamic time-spectrum blocks in cognitive radio networks. In Proceedins of ACM MobiHoc, Montreal, Quebec, Canada, September 9–14.

  7. Zhao, J., Zheng, H., & Yang, G. (2005). Distributed coordination in dynamic spectrum allocation networks. In Proceedings of IEEE DySPAN, Baltimore, MD, November 8–11, pp. 259–268.

  8. Chen, C.C., & Lee, D.S. (2006). A joint design of distributed QoS scheduling and power control for wireless networks. In Proceedings of IEEE INFOCOM, Barcelona, Catalunya, Spain, April 23–29.

  9. Dreo, J., Petrowski, A., Siarry, P., & Taillard, E. (2006). Metaheuristics for hard optimization: Methods and case studies. Berlin: Springer.

    Google Scholar 

  10. Tassiulas, L. (1995). Adaptive back-pressure congestion control based on local information. IEEE Transactions on Automatic Control, 40(2), 236–250.

    Article  MATH  MathSciNet  Google Scholar 

  11. Yackoski, J., Azimi-Sadjadi, B., Namazi, A., Li, J. H., Sagduyu, Y., & Levy, R. (2011). RF-NEST: Radio frequency network emulator simulator tool. In Proceedings of IEEE MILCOM, Baltimore, MD, November.

  12. Cox, D. R., & Hinkley, D. V. (1974). Theoretical statistics. London: Chapman and Hall.

    Book  MATH  Google Scholar 

  13. Wyglinski, A. M., Nekovee, M., & Hou, Y. T. (eds) (2010). Cognitive radio communications and networks: Principles and practices. Amsterdam: Elsevier.

    Google Scholar 

  14. Back, T., Fogel, D., & Michalewicz, Z. (eds) (1997). Handbook of evolutionary computation. New York, NY: Oxford University Press.

    Google Scholar 

Download references

Acknowledgments

This material is based upon work supported by the Air Force Office of Scientific Research under STTR Contracts FA9550-12-C-0037, FA9550-10-C-0026 and FA9550-11-C-0006.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yi Shi.

Additional information

Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Air Force Office of Scientific Research.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shi, Y., Sagduyu, Y.E. & Li, J.H. Multi-layer optimization with backpressure and genetic algorithms for multi-hop wireless networks. Wireless Netw 20, 1265–1273 (2014). https://doi.org/10.1007/s11276-013-0676-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-013-0676-8

Keywords

Navigation