International Journal of Mathematical, Engineering and Management Sciences

ISSN: 2455-7749

Opportunistic Channel Allocation Model in Collocated Primary Cognitive Network

Mangala Prasad Mishra
School of Computer and Information Science, Indira Gandhi National Open University, New Delhi, India.

Sunil Kumar Singh
Department of Computer Science & Information Technology, Mahatma Gandhi Central University, Bihar, India.

Deo Prakash Vidyarthi
School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India.

DOI https://doi.org/10.33889/IJMEMS.2020.5.5.076

Received on February 22, 2020
  ;
Accepted on May 12, 2020

Abstract

The growing demand of radio spectrum to facilitate the primary/secondary users in a cellular network is a challenging task. Many channel allocation models, applying cognition, have been proposed to increase the radio spectrum utilization. The proposed model peruses three types of users: primary users (PUs), opportunistic primary users (OPUs), and secondary users (SUs) that use the radio resources in collocated primary base stations. Out of these users, the opportunistic primary users and secondary users may request for handover as per their requirements. The objective of the model is to enhance the radio spectrum utilization by the opportunistic utilization of radio resources by OPUs and by enabling cognitive radio base stations to collect free channel information dynamically. The cognitive radio base station maintains the centralized free channel at collocated primary base stations to facilitate the SUs opportunistically. The proposed channel allocation technique maintains the Quality of Experience (QoE) of the users as well. The performance analysis of the model is done by simulation which diversifies the importance of the proposed model in the view of minimum blocked services.

Keywords- Cognitive radio, Channel allocation, Quality of experience (QoE), Secondary base station (SBS), Spectrum handover, Call admission.

Citation

Mishra, M. P., Singh, S. K., & Vidyarthi, D. P. (2020). Opportunistic Channel Allocation Model in Collocated Primary Cognitive Network. International Journal of Mathematical, Engineering and Management Sciences, 5(5), 995-1012. https://doi.org/10.33889/IJMEMS.2020.5.5.076.

Conflict of Interest

The authors confirm that there is no conflict of interest to declare for this publication.

Acknowledgements

Authors would like to acknowledge the editors and the anonymous reviewers for their useful suggestions resulting in quality improvement of this paper. Also to acknowledge Mahatma Gandhi Central University, Bihar, India, and Indira Gandhi National Open University, New Delhi, India for the support and cooperation.

References

Ali, A., Abbas, L., Shafiq, M., Bashir, A.K., Afzal, M.K., Liaqat, H.B., Siddiqi, M.H., & Kwak, K.S. (2019). Hybrid fuzzy logic scheme for efficient channel utilization in cognitive radio networks. IEEE Access, 7, 24463-24476.

Ali, A., Feng, L., Bashir, A.K., El-Sappagh, S.H.A., Ahmed, S.H., Iqbal, M., & Raja, G. (2020). Quality of service provisioning for heterogeneous services in cognitive radio-enabled internet of things. IEEE Transactions on Network Science and Engineering, 7(1), 328-342.

Anandakumar, H., & Umamaheswari, K. (2017). Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers. Cluster Computing, 20(2), 1505-1515.

Bharathi, G., & Jeyanthi, K.M.A. (2018). An optimization algorithm-based resource allocation for cooperative cognitive radio networks. The Journal of Supercomputing, 76, 1180-1200.

Bhattacharya, A., Ghosh, R., Sinha, K., Datta, D., & Sinha, B.P. (2015). Noncontiguous channel allocation for multimedia communication in cognitive radio networks. IEEE Transactions on Cognitive Communications and Networking, 1(4), 420-434.

Cisco, T. (2013). Cisco visual networking index: global mobile data traffic forecast update, 2012–2017. Cisco Public Information, 26, 27.

Han, R., Gao, Y., Wu, C., & Lu, D. (2018). An effective multi-objective optimization algorithm for spectrum allocations in the cognitive-radio-based Internet of Things. IEEE Access, 6, 12858-12867.

Hasan, N.U., Ejaz, W., Ejaz, N., Kim, H.S., Anpalagan, A., & Jo, M. (2016). Network selection and channel allocation for spectrum sharing in 5G heterogeneous networks. IEEE Access, 4, 980-992.

Hu, F., Chen, B., & Zhu, K. (2018). Full spectrum sharing in cognitive radio networks toward 5G: a survey. IEEE Access, 6, 15754-15776.

Piran, M.J., Tran, N.H., Suh, D.Y., Song, J.B., Hong, C.S., & Han, Z. (2016). QoE-driven channel allocation and handoff management for seamless multimedia in cognitive 5G cellular networks. IEEE Transactions on Vehicular Technology, 66(7), 6569-6585.

Radhakrishnan, I., Souay, R., Palattellaz, M.R., & Engel, T. (2017, June). An efficient service channel allocation scheme in sdn-enabled vanets. In 2017 16th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net) (pp. 1-7). IEEE. Budva, Montenegro.

Rajaratnam, M., & Takawira, F. (2001). Handoff traffic characterization in cellular networks under nonclassical arrivals and service time distributions. IEEE Transactions on Vehicular Technology, 50(4), 954-970.

Seyedebrahimi, M., Bouhafs, F., Raschella, A., Mackay, M., & Shi, Q. (2016, June). SDN-based channel assignment algorithm for interference management in dense wi-fi networks. In 2016 European Conference on Networks and Communications (EuCNC) (pp. 128-132). IEEE. Athens, Greece.

Shafigh, A.S., Mertikopoulos, P., Glisic, S., & Michael, Y. (2017). Semi-cognitive radio networks: a novel dynamic spectrum sharing mechanism. IEEE Transactions on Cognitive Communications and Networking, 3(1), 97-111.

Singh, S.K., Kaushik, A., & Vidyarthi, D.P. (2016, February). A model for cognitive channel allocation using GA. In 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT) (pp. 528-532). IEEE. Ghaziabad, India.

Singh, S.K., Kaushik, A., & Vidyarthi, D.P. (2017). A cognitive channel allocation model in cellular network using genetic algorithm. Wireless Personal Communications, 96(4), 6085-6110.

Singh, S.K., & Vidyarthi, D.P. (2015). Independent tasks scheduling using parallel PSO in multiprocessor systems. International Journal of Grid and High-Performance Computing, 7(2), 1-17.

Singh, S.K., & Vidyarthi, D.P. (2019a). A heuristic channel allocation model with multi lending in mobile computing network. International Journal of Wireless and Mobile Computing, 16(4), 322-339.

Singh, S.K., & Vidyarthi, D.P. (2019b). A pricing model for effective radio spectrum utilization. International Journal of Mobile Computing and Multimedia Communications, 10(4), 41-65.

Tragos, E.Z., Zeadally, S., Fragkiadakis, A.G., & Siris, V.A. (2013). Spectrum assignment in cognitive radio networks: a comprehensive survey. IEEE Communications Surveys & Tutorials, 15(3), 1108-1135.

Vidyarthi, D.P., & Singh, S.K. (2015). A heuristic channel allocation model using cognitive radio. Wireless Personal Communications, 85(3), 1043-1059.

Wei, Z.H., & Hu, B.J. (2018). A fair multi-channel assignment algorithm with practical implementation in distributed cognitive radio networks. IEEE Access, 6, 14255-14267.

Yawada, P.S., & Dong, M.T. (2019). Intelligent process of spectrum handoff/mobility in cognitive radio networks. Journal of Electrical and Computer Engineering. 2019, Article ID 7692630 https://doi.org/10.1155/2019/7692630.

Privacy Policy| Terms & Conditions