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Wireless Personal Communications

, Volume 107, Issue 1, pp 325–340 | Cite as

Throughput Optimization for Interference Aware Underlay CRN

  • Aaman Shamsul Hussain
  • Sanjib K. DekaEmail author
  • Prakash Chauhan
  • Arindam Karmakar
Article
  • 37 Downloads

Abstract

Minimization of interference to primary user (PU), maximizing the throughput and connectivity of secondary users (SUs) while providing the quality of service are the major challenges for success of an underlay mode cognitive radio network (CRN). In this work we propose a model for addressing channel sharing problem for an ad-hoc underlay CRN. The objective is to maximize the throughput of the SUs while keeping the level of interference caused to the PU by the SUs under a given PU threshold. During the underlay communication, each SU receives certain signal strength based on their distances from the PU. A head node is chosen amongst the SUs using a technique based on received signal strength by the SUs. The head collects the information from all the other SUs in terms of data rate requirement, interference produced to PU and the SNR level of the SUs through a common control channel. Using the information received a dynamic programming based model is proposed to decide which SUs can be selected to access a channel for underlay mode communication. A channel is allowed to be accessed such that the throughput of the network is maximized while the overall interference to the PU receiver is kept within the specified tolerable limit. The head node dynamically assigns the channel among the selected SUs. The efficacy of the proposed model has been evaluated with numerical evaluation and simulation study. A greedy algorithm has been formulated which selects the SUs based on their data rate to interference ratio for simulation and comparative study with the proposed model. The results show that the proposed model outperforms the greedy algorithm while increasing the overall connectivity.

Keywords

Cognitive radio network Received signal strength Primary user Secondary user 

Notes

References

  1. 1.
    FCC. (2002). Et Docket no 02-135, spectrum policy task force (sptf) report.Google Scholar
  2. 2.
    Mitola, J., & Maguire, G. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6(4), 13–18.CrossRefGoogle Scholar
  3. 3.
    Bhattacharjee, S., Konar, A., & Bhattacharjee, S. (2011). Throughput maximization problem in a cognitive radio network. International Journal of Machine Learning and Computing, 1(4), 332.CrossRefGoogle Scholar
  4. 4.
    Verma, G., & Sahu, O. P. (2017). Throughput maximization of cognitive radio under the optimization of sensing duration. Wireless Personal Communications.  https://doi.org/10.1007/s11277-017-4564-x.Google Scholar
  5. 5.
    Zhao, Y., Wu, J., & Lu, S. (2011) Throughput maximization in cognitive radio based wireless mesh networks. In IEEE military communications conference (MILCOM) (pp. 260–265).Google Scholar
  6. 6.
    Xie, S., & Shen, L. (2017). Maximum transmission capacity in cognitive radio networks. Wireless Personal Communications.  https://doi.org/10.1007/s11277-017-4348-3.Google Scholar
  7. 7.
    Li, S., Zheng, Z., Ekici, E., & Shroff, N. (2011). Maximizing system throughput by cooperative sensing in cognitive radio networks. arXiv: 11113041v1 [csNI].
  8. 8.
    Mazloumi, L., Shahtalebi, K., & Sabahi, M. F. (2015). A simple method for throughput maximization of ofdma based CR networks. Wireless Personal Communications, 85(4), 1869–1882.  https://doi.org/10.1007/s11277-015-2875-3.CrossRefGoogle Scholar
  9. 9.
    Tan, L. T., & Le, L. B. (2014) Channel assignment for throughput maximization in cognitive radio networks. arXiv:14041995v1 [csNI].
  10. 10.
    Benaya, A. M., Rosas, A. A., & Shokair, M. (2017). Proposed scheme for maximization of minimal throughput in mimo underlay cognitive radio networks. Wireless Personal Communications.  https://doi.org/10.1007/s11277-017-4456-0.Google Scholar
  11. 11.
    Moravek, P., Komosny, D., Simek, M., Jelinek, M., Girbau, D., & Lazaro, A. (2010) Signal propagation and distance estimation in wireless sensor networks. In 33rd International conference on telecommunication and signal processing, TSP 2010 (pp. 35–40).Google Scholar
  12. 12.
    Vu, M., Devroye, N., & Tarokh, V. (2009). On the primary exclusive region of cognitive networks. IEEE Transactions on Wireless Communications, 8(9), 3380–3385.CrossRefGoogle Scholar
  13. 13.
    Haenggi, M., & Ganti, R. K. (2009). Interference in large wireless networks. Foundations and Trends in Networking, NOW, 3(2), 127–248.CrossRefzbMATHGoogle Scholar
  14. 14.
    Martello, S., & Toth, P. (1990). Knapsack problems: Algorithms and computer implementations. New York, NY: Wiley.zbMATHGoogle Scholar
  15. 15.
    Deka, S. K., & Sarma, N. (2017). Opportunity prediction at MAC-layer sensing for ad-hoc cognitive radio networks. Journal of Network and Computer Applications, 82, 140–151.CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Aaman Shamsul Hussain
    • 1
  • Sanjib K. Deka
    • 1
    Email author
  • Prakash Chauhan
    • 1
  • Arindam Karmakar
    • 1
  1. 1.Department of Computer Science Science and EngineeringTezpur UniversityTezpurIndia

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