A Novel Dynamically Sensing and Resource Allocation in a Cognitive Radio Network Using Inspiration from Hemoglobin Binding Algorithm
 |
International Journal of Recent Engineering Science (IJRES) |  |
| © 2022 by IJRES Journal | ||
| Volume-9 Issue-1 |
||
| Year of Publication : 2022 | ||
| Authors : Gbayan Bundega Martins, Mom Joseph M., Igwue Gabriel A., Enokela Adakole Jonathan |
||
 10.14445/23497157/IJRES-V9I1P104 |
How to Cite?
Gbayan Bundega Martins, Mom Joseph M., Igwue Gabriel A., Enokela Adakole Jonathan, "A Novel Dynamically Sensing and Resource Allocation in a Cognitive Radio Network Using Inspiration from Hemoglobin Binding Algorithm," International Journal of Recent Engineering Science, vol. 9, no. 1, pp. 22-30, 2022. Crossref, https://doi.org/10.14445/23497157/IJRES-V9I1P104
Abstract
The eminent challenge on today's radio spectrum is caused by the mode of spectrum allocation that is currently being adapted by spectrum regulators. It is, therefore very, essential that this allocation policy be broken to allow radios equipped with good cognition of the spectrum space to take advantage of the spectrum space and allocate their spectrum under the right condition. This paper proposes a dynamic allocation system based on oxygen-haemoglobin detection and allocation system where preference for allocation is given to spectrum holes with higher allocation over those with lower allocation. The primary motivation for the algorithm is to reward channel(s) spectrum users who are most willing to lease their channels, thus motivating more primary users to open up their spectrum for secondary users. In the work, a mathematical framework for computing spectrum holes has been presented, data has been used to determine this, and the allocation process is performed based on the proposed method.
Keywords
Spectrum allocation, Oxygen-haemoglobin detection, Cognition, Spectrum holes, Primary user, secondary user.
Reference
[1] Fuhui Zhou et al., “Robust Max–Min Fairness Resource Allocation in Sensing-Based Wideband Cognitive Radio with SWIPT: Imperfect Channel Sensing,” IEEE Systems, vol. 12, no. 3, pp. 2361-2372, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Kishor Patil, Ramjee Prasad, and Knud Skouby, “A Survey of Worldwide Spectrum Occupancy Measurement Campaigns for Cognitive Radio,” IEEE Xplore, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Sachin Chaudhari et al., “Measurement Campaign for Collaborative Sensing Using Cyclostationary Based Mobile Sensors,” IEEE Xplore, pp. 283-290, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[4] J. Mitola, “The Software Radio Architecture,” IEEE Communications Magazine, vol. 33, no. 5, pp. 26-38, 1995.
[CrossRef] [Google Scholar] [Publisher Link]
[5] John E. Hall, and Michael E. Hall, Guyton and Hall Textbook of Medical Physiology E-Book, Thirteenth, Elsevier Health Sciences, 2016.
[Google Scholar] [Publisher Link]
[6] Sandeep Kumar Jain, and Baljeet Kaur, “Hybrid Sharing And Power Allocation Using Water Filling Algorithm for MIMO-OFDM Based Cognitive Radio Network,” ICTACT Journal on Communication Technology, vol. 12, no. 2, pp. 2402-2406, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Tim Blackwell, and James Kennedy, “Impact of Communication Topology in Particle Swarm Optimization,” IEEE Transactions on Evolutionary Computation, vol. 23, no. 4, pp. 689-702, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Andrew Portelli, and Adrian Muscat, “A Swarm Intelligence Based Routing Protocol for Decentralised Cognitive Mobile Radio Networks,”.
[Google Scholar] [Publisher Link]
[9] H. Anandakumar, and K. Umamaheswari, “A Bio-Inspired Swarm Intelligence Technique for Social Aware Cognitive Radio Handovers,” Computers & Electrical Engineering, vol. 71, pp. 925-937, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Youness Arjoune, and Naima Kaabouch, “A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions,” Sensors, vol. 19, no. 1, p. 126, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Tevfik Yucek, and Huseyin Arslan, “A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications,” IEEE Communications Surveys & Tutorials, vol. 11, no. 1, pp. 116-130, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Saman Atapattu, Chintha Tellambura, and Hai Jiang, Energy Detection for Spectrum Sensing in Cognitive Radio, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Garima Mahendru, Anil Shukla, and P. Banerjee, “A Novel Mathematical Model for Energy Detection Based Spectrum Sensing in Cognitive Radio Networks,” Wireless Personal Communications, vol. 110, no. 3, pp. 1237-1249, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Chilakala Sudhamani, Ashutosh Saxena, and Vunnava Aswini, “Improved Detection Performance of Energy Detection Based Spectrum Sensing in Cognitive Radio Networks,” International Journal of Sensors Wireless Communications and Control, vol. 11, no. 9, pp. 957- 962, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] M. Ajay Kumar, and N Rajesha, “Effective Analysis on Matched Filter Technique in Cognitive Radio,” International Journal of Applied Engineering Research, vol. 14, no. 3, pp. 840-844, 2019.
[Google Scholar] [Publisher Link]
[16] Suresh Dannana, Babji Prasad Chapa, and Gottapu Sasibhushana Rao, “Spectrum Sensing Using Matched Filter Detection,” Advances in Intelligent Systems and Computing, pp. 497-503, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[17] M. Khadeeja Sherbin, and V. Sindhu, “Cyclostationary Feature Detection for SpectrumSensing in Cognitive Radio Network,” International Conference on Intelligent Computing and Control Systems, pp. 1250-1254, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Kyouwoong Kim et al., “Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio,” IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, pp. 212-215, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Cipriano Santos, Xiaoyun Zhu, and Harlan Crowder, “A Mathematical Optimization Approach for Resource Allocation in Largescale Data Centers,” 2002.
[Google Scholar] [Publisher Link]
[20] Saptarshi Debroy, and Mainak Chatterjee, “Radio Environment Maps and Its Utility in Resource Management for Dynamic Spectrum Access Networks,” Resource Allocation in Next-Generation Broadband Wireless Access Networks, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Andreas Könsgen et al., “A Mathematical Model for Efficient and Fair Resource Assignment in Multipath Transport,” Future Internet, vol. 11, no. 2, p. 39, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Behrouz A. Forouzan, Data Communications and Networking, 4th Edition, McGraw-Hill Publishing, 2007.
[Google Scholar] [Publisher Link]
[23] Deanna Hlavacek, and J. Morris Chang, “A Layered Approach to Cognitive Radio Network Security: A Survey,” Computer Networks, vol. 75, pp. 414-436, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Yinusa. A. Adediran et al., “TV White Space in Nigeria in UHF Band: Geo-Spatial Approach,” IEEE 6th International Conference on Adaptive Science & Technology, pp. 1-6, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[25] T. A. AbdulRahman, A. A. Ayeni, and N. Faruk, “TV Spectrum Usage profile and White Space for Nigeria,” International Journal of Information Processing and Communication, vol. 2, no. 1&2, pp. 129-141, 2014.
[Google Scholar] [Publisher Link]
[26] Matthias Wellens, Jin Wu, and Petri Mahonen, “Evaluation of Spectrum Occupancy in Indoor and Outdoor Scenario in the Context of Cognitive Radio,” 2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, pp. 420-427, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[27] S. D. Barnes, P. A. Jansen van Vuuren, and B. T. Maharaj, “Spectrum Occupancy Investigation: Measurements in South Africa,” Measurement, vol. 46, no. 9, pp. 3098-3112, 2013.
[CrossRef] [Google Scholar] [Publisher Link]