Call for Paper - July 2022 Edition
IJCA solicits original research papers for the July 2022 Edition. Last date of manuscript submission is June 20, 2022. Read More

Interval Type-2 Fuzzy Logic System for Dynamic Spectrum Access in Cognitive Radio

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2015
Mahesh V. Lakhekar, Shirish L. Kotgire

Mahesh V Lakhekar and Shirish L Kotgire. Article: Interval Type-2 Fuzzy Logic System for Dynamic Spectrum Access in Cognitive Radio. International Journal of Computer Applications 131(8):34-40, December 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Mahesh V. Lakhekar and Shirish L. Kotgire},
	title = {Article: Interval Type-2 Fuzzy Logic System for Dynamic Spectrum Access in Cognitive Radio},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {131},
	number = {8},
	pages = {34-40},
	month = {December},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


The current scenario has shown that, with the conventional spectrum access approach, the radio spectrum allocated to primary (licensed) users is hugely underutilized. While many spectrum methods have been proposed to utilize spectrum efficient manner, the spectrum access opportunistic way is happen to the most practical approach to attain near-optimal spectrum utilization by permitting secondary (unlicensed) users to sense and access available spectrum opportunistically. In this paper, we present decision making scheme in cognitive radio based on Interval type-2 fuzzy logic system. Here, classical type-1 and Interval type-2 fuzzy logic system has been compared in terms of possibility of spectrum access by the secondary user with effective and seamless communication between cognitive radio and primary user. The proposed fuzzy inference system has three input parameters such as spectrum utilization efficiency, degree of mobility and distance to primary user of cognitive radio, along with output parameter as the possibility of accessing the spectrum for secondary user based on linguistic knowledge of 27 rules. This paper mainly deals with design of decision making scheme using Interval type-2 fuzzy logic for minimizing the effect of uncertainty produced by the measurement and environmental noise. Simulation result shows significant enhancement in dynamic spectrum allocation for secondary user with higher probability conditions.


  1. J. Mitola and G.Q. Maguire, 1999 “Cognitive radio: making software radios more personal”, IEEE Personal Communications, Vol. 6, pp.13-18.
  2. Simon Haykin, 2005 “Cognitive Radio: Brain-Empowered Wireless Communications”, IEEE Journal on Selected Areas in Communications, Vol. 23, No. 2, pp 201-220.
  3. Ming-Ying Hsiao, Tzuu-Hseng S. Li , J. Z. Lee, C. H. Chao and S. H. Tsai, 2008 “Design of interval type-2 fuzzy sliding-mode controller”, Information Sciences, Vol. 178, pp 1696–1716.
  4. Sumit Ghosh, Qutaiba Razouqi, H. Jerry Schumacher, and Aivars Celmins, 1998. “A Survey of Recent Advances in Fuzzy Logic in Telecommunications Networks and New Challenges”, IEEE Transactions on Fuzzy Systems, Vol. 6, No. 3, pp 443-447.
  5. Nicola Baldo and Michele Zorzi, 2009 “Cognitive network access using fuzzy decision making”, IEEE Transactions on Wireless Communications, Vol. 8, No. 7, pp-3523-3535.
  6. Marja Matinmikko, Tapio Rauma, Miia Mustonen, Ilkka Harjula, Heli Sarvanko and Aarne Mammela 2009 “Application of fuzzy logic to cognitive radio systems”, IEICE Trans. Communication, Vol. E-92-B, No. 12, pp3572-3580.
  7. Prabhjot Kaur, Moin Uddin, and Arun Khosla. 2010 “Fuzzy Based Adaptive Bandwidth Allocation Scheme in Cognitive Radio Networks”, International Conference on ICT and Knowledge Engineering, pp 41-45.
  8. Hong-Sam T. Le and Hung D. Ly. 2011, “Opportunistic Spectrum Access Using Fuzzy Logic for Cognitive Radio Networks”, International Journal of Wireless Information Networks, Vol. 18, No. 3, pp 171-178.
  9. F. Koroupi, H. Salehinejad, and S. Talebi, 2013. “Spectrum Assignment In Cognitive Radio Networks Using Fuzzy Logic Empowered Ants”, Iranian Journal of Fuzzy Systems, Vol. 10, No. 6, pp. 1-19.
  10. Mansi Subhedar, Gajanan Birajdar, 2013. “Comparison of mamdani and sugeno inference systems for dynamic spectrum allocation in cognitive radio networks”, Wireless Personal Communication, Vol. 71, pp. 805-819.


Cognitive Radio, Type-1 fuzzy logic, Interval Type-2 Fuzzy logic System, Spectrum Access.