An Improved Proactive Handoff Scheme (IPHS) for Target Channel Selection in Cognitive Radio Network
DOI:
https://doi.org/10.35806/ijoced.v5i1.293Keywords:
Channel selection, Cognitive radio network, Primary users (PUs), Secondary users (PUs), Spectrum handoffAbstract
In recent years, one of the challenges of spectrum allocation and utilization was fixed spectrum allocation which lead to spectrum scarcity and underutilization. In an attempt to address this challenge, Cognitive Radio Network (CRN) which uses Dynamic Spectrum Access (DSA) was proposed. It allows licensed users’ or Primary Users’ (PUs) spectrum to be shared with unlicensed users or Secondary Users (SUs). DSA could be achieved by developing an effective channel selection scheme for SUs spectrum handoff. Selecting an appropriate channel for the SUs to continue their interrupted transmission is a challenging task. Several researchers have used different techniques such as Novel Proactive Handoff Scheme (NPHS) to enhance accurate channel selection for spectrum handoff by considering only channel occupancy. These techniques still suffer some set back like high number of spectrum handoff and delay in channel selection. This paper presents an Improved Proactive Spectrum Handoff Scheme (IPHS) for accurate target channel selection in CRN. The improvement is achieved by considering channel signal quality in addition to channel occupancy as a criteria for the selection of a backup channel for spectrum handoff. Simulation results showed that the IPHS reduced the number of spectrum handoff by 15% and 26% as compared to NPHS and IEEE 802.11 scheme respectively. The average delay was also reduced by 13% and 35% as compared to NPHS and IEEE 802.11 scheme respectively.
References
Alias, D. M. (2016). Cognitive radio networks: A survey. In 2016 International conference on wireless communications, signal processing and networking (WiSPNET) (pp. 1981-1986). IEEE. https://doi.org/10.1109/WiSPNET.2016.7566489
Bharathy, G. T., Rajendran, V., Meena, M., & Tamilselvi, T. (2021). Research and development in the networks of cognitive radio: a survey. In Sustainable Communication Networks and Application (pp. 475-494). Springer, Singapore. https://doi.org/10.1007/978-981-15-8677-4_39
Buttar, A. S. (2019). Fundamental operations of cognitive radio: A survey. In 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT) (pp. 1-5). IEEE. https://doi.org/10.1109/ICECCT.2019.8869190
Gouda, A. E., Rabia, S. I., Zakariya, A. Y., & Omar, M. A. (2018). Reactive spectrum handoff combined with random target channel selection in cognitive radio networks with prioritized secondary users. Alexandria engineering journal, 57(4), 3219-3225. https://doi.org/10.1016/j.aej.2017.11.011
Grover, A., Bali, V., & Singh, S. (2018). Channel Selection and Switching in Cognitive Radio Networks: Challenges and Approaches. 2018 8th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 14-15.
Hindia, M. H. D., Qamar, F., Ojukwu, H., Dimyati, K., Al-Samman, A. M., & Amiri, I. S. (2020). On platform to enable the cognitive radio over 5G networks. Wireless Personal Communications, 113(2), 1241-1262. https://doi.org/10.1007/s11277-020-07277-3
Kumar, A., & Kumar, K. (2020). Multiple access schemes for Cognitive Radio networks: A survey. Physical Communication, 38, 100953. https://doi.org/10.1016/j.phycom.2019.100953
Manesh, M. R., Quadri, A., Subramaniam, S., & Kaabouch, N. (2017). An Optimized SNR Estimation Technique using Particle Swarm Optimization Algorithm. IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), 1-6. https://doi.org/10.1109/CCWC.2017.7868387
Mishra, M. P., & Vidyarthi, D. P. (2019). Spectrum Handoff in Cognitive Radio Cellular Network: A Review. In 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART) (pp. 210-215). IEEE. https://doi.org/10.1109/SMART46866.2019.9117491
Quadri, A. (2018). A Channel Ranking and Selection Scheme Based on Channel Occupancy and SNR for Cognitive Radio Systems. The University of North Dakota.
Rajpoot, V., & Tripathi, V. S. (2019). A Novel Proactive Handoff Scheme with CR Receiver Based Target Channel Selection for Cognitive Radio Network. Physical Communication, 36, 100810. https://doi.org/10.1016/j.phycom.2019.100810
Rodrigues, J. J. P. C. (2020). Deep reinforcement learning based optimal channel selection for cognitive radio vehicular ad-hoc network. IET Communications, 14(19), 3464-3471(7). https://digital-library.theiet.org/content/journals/10.1049/iet-com.2020.0451
Thomas, J., & Menon, P. P. (2017). A survey on spectrum handoff in cognitive radio networks. In 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) (pp. 1-4). IEEE. https://doi.org/10.1109/ICIIECS.2017.8275896
Tlouyamma, J., & Velempini, M. (2021). Channel selection algorithm optimized for improved performance in cognitive radio networks. Wireless Personal Communications, 119(4), 3161-3178. https://doi.org/10.1007/s11277-021-08392-5
