Skip to main content
Log in

Channel Allocation with MIMO in Cognitive Radio Network

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The bulk volume and diverse data generated by Smart grid applications require use of Cognitive Radio (CR) technology for efficient handling. The CR technology proved out to be the most efficient technology which improves spectral utilization of any wireless network. The work in this paper is on the assignment of spectrum to the secondary users when primary user are not using. The work uses hybrid of CR and MIMO technology with clustering of gate ways, the channel allocation is done by taking real time environment in consideration. In this paper, three assignment schemes are proposed fair (F-MIMO) scheme, priority (P-MIMO) scheme and small clusters (CC-MIMO). The F-MIMO is used in low traffic condition, when all the HGWs sends the periodic data. The P-MIMO instead of reserving channels for priority user’s places non priority users in buffer in conditions when no vacant channel is available for priority users, this scheme is used in moderate traffic conditions. For the high traffic load, CC-MIMO provides priority and also borrows channels from nearby gate ways. The simulations are performed in three clusters. The simulations are done under moderate traffic using P-MIMO to compare utility of gateway with sensing bandwidth using different sensing costs. The proposed three schemes are compared in fairness and user rewards in both scenarios when borrowing is allowed and not allowed. The CC-MIMO performed optimal as compared to F-MIMO and P-MIMO which are comparable to other schemes in literature. The number of channel allocations and maximum sum reward are simulated with respect to the number of users in presence of priority users.

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
Fig. 7

Similar content being viewed by others

References

  1. Khan, A. A., Rehmani, M. H., & Reisslein, M. (2015). Cognitive radio for smart grids : Survey of architectures, spectrum sensing mechanisms, and networking protocols. IEEE Communications Surveys & Tutorials, 18(1), 860–898.

    Article  Google Scholar 

  2. Alam, S., Sohail, M. F., Ghauri, S. A., Qureshi, I. M., & Aqdas, N. (2017). Cognitive radio based smart grid communication network. Renewable and Sustainable Energy Reviews, 72, 535–548.

    Article  Google Scholar 

  3. Faheem, M., & Cagri-Gungor, V. (2017). Capacity and spectrum-aware communication framework for wireless sensor network-based smart grid applications. Computer Standards and Interfaces, 53, 48–58.

    Article  Google Scholar 

  4. Faheem, M., & Gungor, V. C. (2018). Energy efficient and QoS-aware routing protocol for wireless sensor network-based smart grid applications in the context of industry 4.0. Applied Soft Computing Journal, 68, 910–922.

    Article  Google Scholar 

  5. Faheem, M., & Gungor, V. C. (2018). MQRP: Mobile sinks-based QoS-aware data gathering protocol for wireless sensor networks-based smart grid applications in the context of industry 4.0-based on internet of things. Future Generation Computer Systems, 82, 354–378.

    Article  Google Scholar 

  6. Wang, H., Qian, Y., & Sharif, H. (2013). Multimedia communications over cognitive radio networks for smart grid applications. IEEE Wireless Communications, 20, 125–132.

    Article  Google Scholar 

  7. Xu, L., Qian, F., Li, Y., Li, Q., Yang, Y. W., & Xu, J. (2016). Resource allocation based on quantum particle swarm optimization and RBF neural network for overlay cognitive OFDM system. Neurocomputing, 173, 1250–1256.

    Article  Google Scholar 

  8. Farhang-Boroujeny, B., & Kempter, R. (2008). Multicarrier communication techniques for spectrum sensing and communication in cognitive radios. IEEE Communications Magazine, 46, 80–85.

    Article  Google Scholar 

  9. Laverty, D. M., Morrow, D. J., Best, R., & Crossley, P. A. (2010). Telecommunications for smart grid: Backhaul solutions for the distribution network. In: IEEE PES General Meeting, PES 2010.

  10. Yan, Y., Qian, Y., Sharif, H., & Tipper, D. (2013). A survey on smart grid communication infrastructures: Motivations, requirements and challenges. IEEE Communications Surveys and Tutorials, 15, 5–20.

    Article  Google Scholar 

  11. Bouhafs, F., Mackay, M., & Merabti, M. (2012). Links to the future: Communication requirements and challenges in the smart grid. IEEE Power and Energy Magazine, 10, 24–32.

    Article  Google Scholar 

  12. Wang, Q., He, T., Chen, K. C., Wang, J., Ko, B., Lin, Y., et al. (2012). Dynamic spectrum allocation under cognitive cell network for M2M applications. In Conference RecordAsilomar Conference on Signals, Systems and Computers.

  13. Jiang, C., Chen, Y., Gao, Y., & Liu, K. J. R. (2013). Evolutionary game for joint spectrum sensing and access in cognitive radio networks. In GLOBECOMIEEE Global Telecommunications Conference.

  14. Askari, M., Kavian, Y. S., Kaabi, H., & Rashvand, H. F. (2012). A channel assignment algorithm for cognitive radio wireless sensor networks. In IET Conference on Wireless Sensor Systems (WSS 2012).

  15. Fadel, E., Faheem, M., Gungor, V. C., Nassef, L., Akkari, N., Malik, M. G. A., et al. (2017). Spectrum-aware bio-inspired routing in cognitive radio sensor networks for smart grid applications. Computer Communications, 101, 106–120.

    Article  Google Scholar 

  16. Yau, K. L. A., Ramli, N., Hashim, W., & Mohamad, H. (2014). Clustering algorithms for Cognitive Radio networks: A survey. Journal of Network and Computer Applications, 45, 79–95.

    Article  Google Scholar 

  17. Brettschneider, D., Hölker, D., Roer, P., & Tönjes, R. (2016). Cluster-based distributed algorithm for energy management in smart grids. Computer Science-Research and Development, 31, 17–23.

    Article  Google Scholar 

  18. Al-Jarrah, O. Y., Al-Hammadi, Y., Yoo, P. D., & Muhaidat, S. (2017). Multi-layered clustering for power consumption profiling in smart grids. IEEE Access, 5, 18459–18468.

    Article  Google Scholar 

  19. Xishuang, D., Lijun, Q., & Lei, H. (2017). Short-term load forecasting in smart grid: A combined CNN and K-means clustering approach. In 2017 IEEE international conference on Big Data and Smart Computing, BigComp 2017.

  20. Vrbský, L., Da Silva, M. S., Cardoso, D. L., & Francês, C. R. L. (2017). Clustering techniques for data network planning in Smart Grids. In Proceedings of the 2017 IEEE 14th international conference on networking, sensing and control, ICNSC 2017.

  21. Sreesha, A. A., Somal, S., & Lu, I. T. (2011). Cognitive Radio Based Wireless Sensor Network architecture for smart grid utility. In 2011 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2011.

  22. Huynh, C. K., & Lee, W. C. (2016). An efficient channel selection and power allocation scheme for TVWS based on interference analysis in smart metering infrastructure. Journal of Communications and Networks, 18(1), 50–64.

    Article  Google Scholar 

  23. He, W., Li, K., Zhou, Q., & Li, S. (2014). A CR spectrum allocation algorithm in smart grid wireless sensor network. Algorithms, 7, 510–522.

    Article  Google Scholar 

  24. Yang, S., Wang, J., Han, Y., & Zhao, Q. (2016). Dynamic spectrum allocation algorithm based on fairness for smart grid communication networks. In 2016 35th Chinese Control Conference, pp. 6873–6877.

  25. Boustani, A., Jadliwala, M., Kwon, H. M., & Alamatsaz, N. (2015). Optimal resource allocation in Cognitive Smart Grid Networks. In 2015 12th annual IEEE Consumer Communications and Networking Conference, CCNC 2015.

  26. Miao, H., Chen, G., & Dong, Z. (2016). Enhanced evolutionary heuristic approaches for remote metering smart grid networks. IET Networks, 5(6), 153–161.

    Article  Google Scholar 

  27. Alam, S., Sarfraz, M., Usman, M. B., Ahmad, M. A., & Iftikhar, S. (2017). Dynamic resource allocation for cognitive radio based smart grid communication networks. International Journal of Advanced and Applied Sciences, 4(10), 76–83.

    Article  Google Scholar 

  28. Ji, B., Li, Y., Cao, D., & Li, C. (2020). Secrecy performance analysis of UAV assisted relay transmission for cognitive network with energy harvesting. IEEE Transactions on Vehicular Technology, 9545, 1–12.

    Article  Google Scholar 

  29. Zhang, H., Jiang, D., Li, F., & Liu, K. (2017). Cluster-based resource allocation for spectrum-sharing femtocell networks. IEEE Access, 4, 8643–8656.

    Article  Google Scholar 

  30. Huixin, W., Duo, M., & He, L. (2014). Analysis and simulation of the dynamic spectrum allocation based on parallel immune optimization in cognitive wireless networks. The Scientific World Journal, 2014, 623670. https://doi.org/10.1155/2014/623670.

    Article  Google Scholar 

  31. Peng, C., Zheng, H., & Zhao, B. Y. (2006). Utilization and fairness in spectrum assignment for opportunistic spectrum access*. Mobile Networks and Applications, 11(4), 555–576.

    Article  Google Scholar 

  32. Dai, J., Wang, S., & Member, S. (2017). Clustering-based spectrum sharing strategy for cognitive radio networks. IEEE Journal on Selected Areas in Communications, 35(1), 228–237.

    Google Scholar 

  33. El, Tanab M., Member, S., Hamouda, W., & Member, S. (2017). Resource allocation for underlay cognitive radio networks: A survey. IEEE Communications Surveys and Tutorials, 19(2), 1249–1276.

    Article  Google Scholar 

  34. Tabakovic, Z. (2016). Cognitive radio frequency assignment with interference weighting and categorization. EURASIP Journal on Wireless Communications and Networking, 16, 1–24.

    Google Scholar 

  35. Hawa, M., Abu-al-nadi, D. I., Alsmadi, O. M. K., & Jafar, I. F. (2016). On using spectrum history to manage opportunistic access in cognitive radio networks. IEEE Access, 4, 5293–5308.

    Article  Google Scholar 

  36. Ranjan, R., Agrawal, N., & Joshi, S. (2020). Interference mitigation and capacity enhancement of cognitive radio networks using modified greedy algorithm/channel assignment and power allocation techniques. IET Communications, 14(9), 1502–1509.

    Article  Google Scholar 

  37. Ghosh, S., De, D., & Deb, P. (2019). Energy and spectrum optimization for 5G massive MIMO cognitive femtocell based mobile network using auction game theory. Wireless Personal Communications, 106(2), 555–576.

    Article  Google Scholar 

  38. Groenewald, B., Balyan, V., & Kahn, M. T. E. (2018). Fast channel load algorithm for downlink of multi-rate MC-DS-CDMA and smart grid communication. Journal of Applied Engineering Research, 13(20), 14607–14613.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vipin Balyan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Balyan, V. Channel Allocation with MIMO in Cognitive Radio Network. Wireless Pers Commun 116, 45–60 (2021). https://doi.org/10.1007/s11277-020-07704-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07704-5

Keywords

Navigation