Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Fuzzy Logic Adaptive Min-Max Model (Flamm) for Pathloss Prediction in Mobile Communications Network

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Olaniyi Ademola S., Babalola Gbemisola O.

Olaniyi Ademola S. and Babalola Gbemisola O.. Fuzzy Logic Adaptive Min-Max Model (Flamm) for Pathloss Prediction in Mobile Communications Network. International Journal of Computer Applications 155(1):22-30, December 2016. BibTeX

	author = {Olaniyi Ademola S. and Babalola Gbemisola O.},
	title = {Fuzzy Logic Adaptive Min-Max Model (Flamm) for Pathloss Prediction in Mobile Communications Network},
	journal = {International Journal of Computer Applications},
	issue_date = {December 2016},
	volume = {155},
	number = {1},
	month = {Dec},
	year = {2016},
	issn = {0975-8887},
	pages = {22-30},
	numpages = {9},
	url = {},
	doi = {10.5120/ijca2016911918},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Poor GSM coverage area and dead spots are problems facing GSM engineers and users. This issue should be met during system design when path loss calculations are carried out. It is most informative that a model is required to improve signal strength, help in planning better mobile wireless network and to address the poor quality of mobile network services in metropolitan areas caused by propagation pathloss. In this study, a Fuzzy Logic Adaptive Min-Max Model (FLAMM) for pathloss prediction is developed. Experimental pathloss measurement was carried out in a Non urban region in Lagos Nigeria. The Received Signal Strength level obtained from the FLAMM model was subjected to adequacy check in order to ascertain the viability of the model. The results show that the fuzzy model is close in value to the original measured value. This suggests that the proposed fuzzy model produces an acceptable approximate value for pathloss measurement. The fuzzy-based method of this research is more efficient, faster and accurate than the physical and empirical methods. The evaluation of the pathloss criteria shows that the system can effectively model pathloss mobile network and the different measured input values and their respective output shows that the model is robust enough for the evaluation of probable degree of variation of pathloss conditions in mobile communication.


  1. Akpado, K.A, Oguejiofor O.S, Abe, A, Femijemilohun O.J (2013): “Outdoor Propagation Prediction in Wireless Local Area Network (WLAN)” International Journal of Engineering Science and Innovation Technology (IJESIT) Vol.2, Issue 2
  2. Akpado, K.A, Oguejiofor O.S, Abe, A, and Ejiofor, A.C. (2013): “Pathloss Prediction for a Typical Mobile Communication System in Nigeria Using Empirical Models” International Journal of Computer networks and Wireless Communications (IJCNWC). Vol 3, No 2, Pg 207-211
  3. Emagbetere J.O, Aigbodioh F.A and Edeko F.O. (2009): “Radio Network Planning for GSM 900 in a Rural Environment. Journal of Mobile Communication 3(1): 8-11.
  4. Emagbetere J.O. and Edeko F.O (2010): An evaluation of outgoing calls of GSM network services on Oghara Delta state. Res J. Appl. Sci. 2: 106-1018
  5. Shalangwa, D.A, and Malgwi, D.I (2010): An investigation of inter and intra connectivity between the three major Global system for Mobile Communication GSM operators in Mubi Local Government Area of Adamawa state Nigeria. Journal of communication 4(3) 64-67.
  6. Shoewu, O and Edeko, F. O. (2011) “Analysis of radio wave propagation in Lagos Environs, American Journal of Scientif and Industrial Research, Vol. 2, No 3, pg 438 – 455.
  7. Emagbetere; J.O and Edeko, F.O,.(2009): measurement validation of Hata like models for Radio Propagation path loss in Rural Environment at 1.89Hz journal of mobile communication 3(2)pg 17-21.
  8. Ayeni, A; Faruk,N, Lukman, O, Muhammed, M.Y and Gumel M.I (2012) “ Comparative Assessments of Some Selected Existing radio Propagation Models. A Study Of Kano City, Nigeria” European Journal of Scientific Research. Vol 70, No 1, Pg 120-127
  9. Shalangwa, D.A. and Jerome, G, (2010): Path Loss Propagation Model for Gombi-Town, IJCSNS International Journal of Computer Science and Network Security.
  10. Ademola Olaniyi and O.A. Okunade (2011): A Fuzzy Time-Series Approach to Enrolment forecasting. Afr J. of Comp & ICT. Vol 4, No. 2. Issue 2. P41-46


Path Loss, Fuzzy Inference System, Min-Max, Mobile Network.