Classification of Spectrum Scheduling Using Conditional Probability and Decision Tree Supervised Learning Approach

Imeh Umoren; Esther Polycarp; Godwin Ansa

doi:doi:10.11648/j.ajai.20210502.11

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Classification of Spectrum Scheduling Using Conditional Probability and Decision Tree Supervised Learning Approach

Imeh Umoren, Esther Polycarp, Godwin Ansa

Published in American Journal of Artificial Intelligence (Volume 5, Issue 2)

Received: 8 June 2021 Accepted: 21 July 2021 Published: 27 August 2021

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Abstract

Spectrum Scheduling is an efficient scheme of improving spectrum utilization for faster communications, higher definition media (HDM) and data transmission. Radio spectrum is very limited in supply resulting in enormous problems related to scarcity. It owes the physical support for wireless communication, both fixed applications and mobile broadband. Basically, effective use of the spectrum depends on the channel settings, sensing performance, detection of spectrum prospect as well as effective transmission of both Primary Users (PUs) and Secondary Users (SUs) packets at a specific time slot. In order to improve spectrum utilization this paper adopted quantitative method which employs Probability Theorem to identify the probabilities of both primary Users (PUs) and secondary users (SUs) in the spectrum datasets allocation and further used conditional probability to compare two Frequency Bands i.e., High Frequency (HF) and Very High Frequency (VHF). The result indicates available spectrum holes (SH) left unutilized in the Secondary User (SU) resulting in the need for spectrum scheduling for the SU. The procedure makes the secondary users occupy a probability of 0.002mhz compared to the primary users on 0.00004mhz utilization. This further indicates that some spectrum holes were left unutilized by the license users (Primary Users). However, spectrum allocation is one of the major issues of improving spectrum efficiency and has become a considerable tool in cognitive wireless networks (CWN). Consequently, the goal of spectrum allocation is to assign leisure spectrum resources efficiently to achieve the optimal Quality of Service (QOS and cognitive user requirements of wireless network. Again, classification of spectrum allocation was carried out through difference methods. Firstly, we employ a probability theorem to identify the probability of both Primary Users (PUs) and Secondary Users (SUs) in the allocated spectrum data sets. Secondly, conditional probability was used to compare two frequency band based on primary and secondary allocation policies designed to identify the specific allocation of each band. Thirdly, Machine Learning (ML) Algorithm based on Decision Tree - Supervised Learning (DTSL) approach was adopted to classified our data sets. The result yielded 68% which correctly classified instances based on the total records of sixty-nine (69) data sets. Research findings demonstrate a highly optimized spectrum scheduling for efficient networks service provisions.

Published in	American Journal of Artificial Intelligence (Volume 5, Issue 2)
DOI	10.11648/j.ajai.20210502.11
Page(s)	46-55
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

High Frequency (HF), Very High Frequency (VHF), Primary Users (PUs), Secondary Users (SUs), Decision Tree Supervised Learning (DTSL), Algorithm and Cognitive Wireless Networks (CWN)

References

[1]	Anita G., Partha P., Bhattacharya (2011). Dynamic Spectrum Access in Cognitive Radio: a brief review, International Journal of Computer Application in Engineering Sciences, Special Issue on Computer Networks & Security, pp 149-153.
[2]	Akyildiz, Won-Yeol L, Vuran M., Mohanty S. (2006). Next generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Elsevier B. V.
[3]	Cisco (2018). Cisco Predicts More IP Traffic in the Next Five Years Than in the History of the Internet, Nov. 2018.
[4]	Fcc, (2020), Federal communication commission “FCC online table of frequency allocations,” Tech. Rep., 2020.
[5]	GSMA P. (2017). Introducing spectrum management (Spectrum primer series). Floor 2, The Walbrook Building, 25 Walbrook, London EC4N 8AF 020 7356 0600.
[6]	Habibzadeh A. (2017). Improvement of handover in femtocell-based cellular cognitive radio network [Ph.D. Dissertation]. Iran: Shahid Rajaee Teacher Training University; (In Farsi).
[7]	Habibzadeh A, Shirvani Moghaddam S (2015). Noise calibrated GLRT-based spectrum sensing method for cognitive radio applications. In: 15th IEEE International Symposium on Signal Processing and Information Technology; Abu Dhabi, UAE; 7-10 Dec. 2015. pp. 174-179.
[8]	Imeh J. Umoren and Saviour J. Inyang (2021). Methodical Performance Modelling of Mobile Broadband Networks with Soft Computing Model. International Journal of Computer Applications 174 (25): 7-21, NY, USA.
[9]	Imeh J. Umoren and Samuel B. Okon, (2021). A Multidimensional Fuzzy Knowledge-based System for Optimizing Wireless Local Area Networks Performance, International Journal of Computer Applications (0975–8887), 183 (1): 8 19, New York, USA.
[10]	Li Zhang, Kai Z, Prasant M. (2010). Opportunistic Spectrum Scheduling for Mobile Cognitive Radio Networks in White Space. Computer Science Department University of California, Davis, CA, USA.
[11]	Linda E. (2009). “Essentials of Cognitive Radio”, New York: Cambridge University Press.
[12]	Mitola J, Maguire G. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, vol 6 (4): pp 13-18.
[13]	Mansi S. and Gajanan B. (2011), “Spectrum Sensing Techniques in Cognitive Radio Networks: A Survey”, International Journal of Next Generation Networks, Vol. 3, No. 2.
[14]	Opendata, (2016). https://www.ofcom.org.uk/research-and data/data/opendata.
[15]	Rosston G (2014). Increasing the efficiency of spectrum allocation, Springer’s Review of Industrial Organization, vol. 45, no. 3, pp. 221–243.
[16]	Raouia M., E. V. Belmega, Inbar F. (2016). EURASIP Journal on Wireless Communications and Networking Efficient spectrum scheduling and power management for opportunistic users. Springer Nature.
[17]	Salim A. Hanna (2011), Spectrum metrics for 2.4GHz ISM Band Cognitive Radio Applications, IEEE 22nd International Symposium on Personal, Indoor, and Mobile Radio Communications.
[18]	Tilghman. P (2019). Will rule the airwaves: A darpa grand challenge seeks autonomous radios to manage the wireless spectrum, IEEE Spectrum, vol. 56, no. 6, pp. 28–33.

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APA Style

Imeh Umoren, Esther Polycarp, Godwin Ansa. (2021). Classification of Spectrum Scheduling Using Conditional Probability and Decision Tree Supervised Learning Approach. American Journal of Artificial Intelligence, 5(2), 46-55. https://doi.org/10.11648/j.ajai.20210502.11

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ACS Style

Imeh Umoren; Esther Polycarp; Godwin Ansa. Classification of Spectrum Scheduling Using Conditional Probability and Decision Tree Supervised Learning Approach. Am. J. Artif. Intell. 2021, 5(2), 46-55. doi: 10.11648/j.ajai.20210502.11

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AMA Style

Imeh Umoren, Esther Polycarp, Godwin Ansa. Classification of Spectrum Scheduling Using Conditional Probability and Decision Tree Supervised Learning Approach. Am J Artif Intell. 2021;5(2):46-55. doi: 10.11648/j.ajai.20210502.11

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@article{10.11648/j.ajai.20210502.11,
  author = {Imeh Umoren and Esther Polycarp and Godwin Ansa},
  title = {Classification of Spectrum Scheduling Using Conditional Probability and Decision Tree Supervised Learning Approach},
  journal = {American Journal of Artificial Intelligence},
  volume = {5},
  number = {2},
  pages = {46-55},
  doi = {10.11648/j.ajai.20210502.11},
  url = {https://doi.org/10.11648/j.ajai.20210502.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20210502.11},
  abstract = {Spectrum Scheduling is an efficient scheme of improving spectrum utilization for faster communications, higher definition media (HDM) and data transmission. Radio spectrum is very limited in supply resulting in enormous problems related to scarcity. It owes the physical support for wireless communication, both fixed applications and mobile broadband. Basically, effective use of the spectrum depends on the channel settings, sensing performance, detection of spectrum prospect as well as effective transmission of both Primary Users (PUs) and Secondary Users (SUs) packets at a specific time slot. In order to improve spectrum utilization this paper adopted quantitative method which employs Probability Theorem to identify the probabilities of both primary Users (PUs) and secondary users (SUs) in the spectrum datasets allocation and further used conditional probability to compare two Frequency Bands i.e., High Frequency (HF) and Very High Frequency (VHF). The result indicates available spectrum holes (SH) left unutilized in the Secondary User (SU) resulting in the need for spectrum scheduling for the SU. The procedure makes the secondary users occupy a probability of 0.002mhz compared to the primary users on 0.00004mhz utilization. This further indicates that some spectrum holes were left unutilized by the license users (Primary Users). However, spectrum allocation is one of the major issues of improving spectrum efficiency and has become a considerable tool in cognitive wireless networks (CWN). Consequently, the goal of spectrum allocation is to assign leisure spectrum resources efficiently to achieve the optimal Quality of Service (QOS and cognitive user requirements of wireless network. Again, classification of spectrum allocation was carried out through difference methods. Firstly, we employ a probability theorem to identify the probability of both Primary Users (PUs) and Secondary Users (SUs) in the allocated spectrum data sets. Secondly, conditional probability was used to compare two frequency band based on primary and secondary allocation policies designed to identify the specific allocation of each band. Thirdly, Machine Learning (ML) Algorithm based on Decision Tree - Supervised Learning (DTSL) approach was adopted to classified our data sets. The result yielded 68% which correctly classified instances based on the total records of sixty-nine (69) data sets. Research findings demonstrate a highly optimized spectrum scheduling for efficient networks service provisions.},
 year = {2021}
}

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TY - JOUR
T1 - Classification of Spectrum Scheduling Using Conditional Probability and Decision Tree Supervised Learning Approach
AU - Imeh Umoren
AU - Esther Polycarp
AU - Godwin Ansa
Y1 - 2021/08/27
PY - 2021
N1 - https://doi.org/10.11648/j.ajai.20210502.11
DO - 10.11648/j.ajai.20210502.11
T2 - American Journal of Artificial Intelligence
JF - American Journal of Artificial Intelligence
JO - American Journal of Artificial Intelligence
SP - 46
EP - 55
PB - Science Publishing Group
SN - 2639-9733
UR - https://doi.org/10.11648/j.ajai.20210502.11
AB - Spectrum Scheduling is an efficient scheme of improving spectrum utilization for faster communications, higher definition media (HDM) and data transmission. Radio spectrum is very limited in supply resulting in enormous problems related to scarcity. It owes the physical support for wireless communication, both fixed applications and mobile broadband. Basically, effective use of the spectrum depends on the channel settings, sensing performance, detection of spectrum prospect as well as effective transmission of both Primary Users (PUs) and Secondary Users (SUs) packets at a specific time slot. In order to improve spectrum utilization this paper adopted quantitative method which employs Probability Theorem to identify the probabilities of both primary Users (PUs) and secondary users (SUs) in the spectrum datasets allocation and further used conditional probability to compare two Frequency Bands i.e., High Frequency (HF) and Very High Frequency (VHF). The result indicates available spectrum holes (SH) left unutilized in the Secondary User (SU) resulting in the need for spectrum scheduling for the SU. The procedure makes the secondary users occupy a probability of 0.002mhz compared to the primary users on 0.00004mhz utilization. This further indicates that some spectrum holes were left unutilized by the license users (Primary Users). However, spectrum allocation is one of the major issues of improving spectrum efficiency and has become a considerable tool in cognitive wireless networks (CWN). Consequently, the goal of spectrum allocation is to assign leisure spectrum resources efficiently to achieve the optimal Quality of Service (QOS and cognitive user requirements of wireless network. Again, classification of spectrum allocation was carried out through difference methods. Firstly, we employ a probability theorem to identify the probability of both Primary Users (PUs) and Secondary Users (SUs) in the allocated spectrum data sets. Secondly, conditional probability was used to compare two frequency band based on primary and secondary allocation policies designed to identify the specific allocation of each band. Thirdly, Machine Learning (ML) Algorithm based on Decision Tree - Supervised Learning (DTSL) approach was adopted to classified our data sets. The result yielded 68% which correctly classified instances based on the total records of sixty-nine (69) data sets. Research findings demonstrate a highly optimized spectrum scheduling for efficient networks service provisions.
VL - 5
IS - 2
ER -

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Author Information

Imeh Umoren

Department of Computer Science, Faculty of Physical Sciences, Akwa Ibom State University, Mkpat Enin, Nigeria
Esther Polycarp

Department of Computer Science, Faculty of Physical Sciences, Akwa Ibom State University, Mkpat Enin, Nigeria
Godwin Ansa

Department of Computer Science, Faculty of Physical Sciences, Akwa Ibom State University, Mkpat Enin, Nigeria

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APA Style

Imeh Umoren, Esther Polycarp, Godwin Ansa. (2021). Classification of Spectrum Scheduling Using Conditional Probability and Decision Tree Supervised Learning Approach. American Journal of Artificial Intelligence, 5(2), 46-55. https://doi.org/10.11648/j.ajai.20210502.11

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ACS Style

Imeh Umoren; Esther Polycarp; Godwin Ansa. Classification of Spectrum Scheduling Using Conditional Probability and Decision Tree Supervised Learning Approach. Am. J. Artif. Intell. 2021, 5(2), 46-55. doi: 10.11648/j.ajai.20210502.11

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AMA Style

Imeh Umoren, Esther Polycarp, Godwin Ansa. Classification of Spectrum Scheduling Using Conditional Probability and Decision Tree Supervised Learning Approach. Am J Artif Intell. 2021;5(2):46-55. doi: 10.11648/j.ajai.20210502.11

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@article{10.11648/j.ajai.20210502.11,
  author = {Imeh Umoren and Esther Polycarp and Godwin Ansa},
  title = {Classification of Spectrum Scheduling Using Conditional Probability and Decision Tree Supervised Learning Approach},
  journal = {American Journal of Artificial Intelligence},
  volume = {5},
  number = {2},
  pages = {46-55},
  doi = {10.11648/j.ajai.20210502.11},
  url = {https://doi.org/10.11648/j.ajai.20210502.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20210502.11},
  abstract = {Spectrum Scheduling is an efficient scheme of improving spectrum utilization for faster communications, higher definition media (HDM) and data transmission. Radio spectrum is very limited in supply resulting in enormous problems related to scarcity. It owes the physical support for wireless communication, both fixed applications and mobile broadband. Basically, effective use of the spectrum depends on the channel settings, sensing performance, detection of spectrum prospect as well as effective transmission of both Primary Users (PUs) and Secondary Users (SUs) packets at a specific time slot. In order to improve spectrum utilization this paper adopted quantitative method which employs Probability Theorem to identify the probabilities of both primary Users (PUs) and secondary users (SUs) in the spectrum datasets allocation and further used conditional probability to compare two Frequency Bands i.e., High Frequency (HF) and Very High Frequency (VHF). The result indicates available spectrum holes (SH) left unutilized in the Secondary User (SU) resulting in the need for spectrum scheduling for the SU. The procedure makes the secondary users occupy a probability of 0.002mhz compared to the primary users on 0.00004mhz utilization. This further indicates that some spectrum holes were left unutilized by the license users (Primary Users). However, spectrum allocation is one of the major issues of improving spectrum efficiency and has become a considerable tool in cognitive wireless networks (CWN). Consequently, the goal of spectrum allocation is to assign leisure spectrum resources efficiently to achieve the optimal Quality of Service (QOS and cognitive user requirements of wireless network. Again, classification of spectrum allocation was carried out through difference methods. Firstly, we employ a probability theorem to identify the probability of both Primary Users (PUs) and Secondary Users (SUs) in the allocated spectrum data sets. Secondly, conditional probability was used to compare two frequency band based on primary and secondary allocation policies designed to identify the specific allocation of each band. Thirdly, Machine Learning (ML) Algorithm based on Decision Tree - Supervised Learning (DTSL) approach was adopted to classified our data sets. The result yielded 68% which correctly classified instances based on the total records of sixty-nine (69) data sets. Research findings demonstrate a highly optimized spectrum scheduling for efficient networks service provisions.},
 year = {2021}
}

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