QoS Improvement Using Intelligent Algorithm under Dynamic Tropical Weather for Earth-Space Satellite Applications
In this paper, the intelligent algorithm (IA) that is capable of adapting to dynamical tropical weather conditions is proposed based on fuzzy logic techniques. The IA effectively interacts with the quality of service (QoS) criteria irrespective of the dynamic tropical weather to achieve improvement in the satellite links. To achieve this, an adaptive network-based fuzzy inference system (ANFIS) has been adopted. The algorithm is capable of interacting with the weather fluctuation to generate appropriate improvement to the satellite QoS for efficient services to the customers. 5-year (2012-2016) rainfall rate of one-minute integration time series data has been used to derive fading based on ITU-R P. 618-12 propagation models. The data are obtained from the measurement undertaken by the Communication Research Group (CRG), Physics Department, Federal University of Technology, Akure, Nigeria. The rain attenuation and signal-to-noise ratio (SNR) were derived for frequency between Ku and V-band and propagation angle with respect to different transmitting power. The simulated results show a substantial reduction in SNR especially for application in the area of digital video broadcast-second generation coding modulation satellite networks.
[1] A.W. Dissanayake, J.E Allnutt, and F Haidara, “A prediction model that combines rain attenuation and other propagation impairments along earth-satellite paths," IEEE Trans on Antennas & Propagation, vol. 45, no. 10, pp. 1546-1558, 1997
[2] S. Katiyar, A textbook on Satellite communication, 3rd ed., published by S. K. Kataria and Sons, New Delhi, 2013.
[3] R. K, Crane, “Prediction of the effects of rain on satellite communication systems” in Proc. of the IEEE, 1977, vol. 65, no. 3, pp. 456-474.
[4] C. Capsoni, F. Fedi, C. Magistroni, A. Pawlina, and A. Paraboni A. “Data and theory for a new model of the horizontal structure of rain cells for propagation applications, Radio Science, vol. 22, no. 33, pp. 395-404, 1987.
[5] L. J Ippolito. "Propagation effects and system performance considerations for satellite communications above 10 GHz," Proc. of the IEEE, Global Telecommunications Conference, 1990, and Exhibition. 'Communications: Connecting the Future', GLOBECOM '90, 2-5 December, 1990.
[6] X. Boulanger, G., Benjamin, L. Casadebaig, and C. Laurent. “Four Years of Total Attenuation Statistics of Earth-Space Propagation Experiments at Ka-Band in Toulouse”, IEEE Transactions on Antennas and Propagation, vol. 63 no. 5, pp. 2203-2214, 2015
[7] S Shrestha and D. Choi Rain attenuation statistics over millimeter wave bands in South Korea, Journal of Atmospheric and Solar- Terrestrial Physics, vol. 152–153, Pp 1-10, 2017
[8] J. S, Ojo, “Geo-spatial distribution of cloud cover and influence of cloud induced attenuation and noise temperature on satellite signal propagation over Nigeria” Advances in Space Research, vol 59, no. 10, pp. 2611-2622, 2017
[9] .S.C. Lu, “Quality of service for personal satellite multimedia”, IEEE Seminar on Personal Broadband Satellite (Ref. No. 2002/059), Jan. 2002, London U.K, 10.1049/ic:20020021. 2002.
[10] L. Ki-Dong, “Variable-target admission control for nonstationary handover traffic in LEO satellite networks”, IEEE Transactions on Vehicular Technology, vol: 54, no 1, pp. 127 – 135, 2005.
[11] M Saraireh, R. Saatchi, S. Al-Khayatt and R Strachan, “Assessment and improvement of quality of service in wireless networks using fuzzy and hybrid genetic-fuzzy approaches,” Artificial Intelligent Review, 27, pp. 95–111, 2008.
[12] K Harb, Omair Butt, Samir Abdul-Jauwad, Abdul-Aziz M. Al-Yami, “Systems Adaptation for Satellite Signal under Dust, Sand and Gaseous Attenuations”, Journal of Wireless Networking and Communications, vol.3, no.3, pp. 39-49, 2013.
[13] R. Z Dhafer, S. Thulfiqar Aldeen A. AL Wahab (2013), “Simplified the QoS Factor for the Adhoc Network sing Fuzzy Technique” Int. J. Communications, Network and System Sciences, vol. 6, pp. 381-387, 2013.
[14] J.S Ojo and P.A Owolawi, “Prediction of Time-series Rain Attenuation based on Rain Rate using Synthetic Storm Techniques over a Subtropical Region. Southern Africa Telecommunication Networks and Applications (SATNAC-2014) conference proceedings, at Boardwalk, Port Elizabeth, Eastern Cape, South Africa, 31 August - 3 September 2014, 67 – 71.
[15] I. M Atta-ur-Rahman. Qureshib, A. N Malikc and M. T. Naseema. “QoS and rate enhancement in DVB-S2 using fuzzy rule based system”, Journal of Intelligent & Fuzzy Systems, vol. 30, no. 2, pp. 801–810, 2016.
[16] I. Adegbidin, P.A Owolawi, and M.O Odhiambo, “Intelligent Weather Awareness Technique for Mitigating Propagation Impairment at SHF and EHF Satellite Network System in a Tropical Climate”, Research Journal of the South African Institute of Electrical Engineers), vol. 107, no.3, pp. 136 – 145, 2016.
[17] H. S. Asad, K.K Richard and H. Gongsheng, “Hybrid Adaptive Modeling to Enhance Robustness of Real-Time Optimization”, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, vol:11, no 4, pp. 348-352, 2017.
[18] O. O. Obiyemi, J. S. Ojo, and T. S. Ibiyemi “Performance Analysis of Rain Rate Models for Microwave Propagation Designs over Tropical Climate,” Progress in Electromagnetics Research M, vol. 39, pp. 115–122, 2014.
[19] O.M, Durodola, J. S Ojo, and M. O Ajewole, (2017) “Performance of Ku-Band Satellite Signals Received during Rainy Condition in two low Latitude Tropical locations of Nigeria”, Adamawa State University Journal of Scientific Research, vol. 1, no.5, pp. 1-17, 2017.
[20] ITU-Rec. P 618-12,"Propagation data and prediction method required for the design of earth-space telecommunication systems, "Radio wave propagation, International Telecommunication Union Recommendation ITU-R P.6I8-12, 2012.
[21] ITU-Rec. P 838-3. Specific attenuation model for rain for use in prediction methods, International Telecommunication Union Recommendation ITU-R P.838-5, 2005.
[22] E., Lutz, M Werner, and A Jahn.: Satellite systems for personal and broadband communications. Springer, New York, 2000.
[23] K Harb., F. R Yu., P. Dakhal, and A. Srinivasan, "An intelligent QoS control system for satellite networks based on Markovian weather," Proc. of IEEE 72nd Conference on Vehicular Technology (VTC)'201O Fall, 6-9 September, 2010, Ottawa, ON, Canada.
[24] A. M. Al-Saegh, A Sali, J. S Mandeep, A. Ismail, Abdulmajeed HJ Al-Jumaily, and C. Gomes: Atmospheric Propagation Model for Satellite Communications, MATLAB Applications for the Practical Engineer, publisher, Intech, 249-275, 2014.
[25] C.C Lee, "Fuzzy Logic in Control Systems: Fuzzy Logic Controller - I, 11," IEEE Transactions on Systems. Man. and Cybernetics, vol. 20, no. 2, pp. 404-435 1990.
[26] L. A., Zadeh, "Fuzzy Sets, Usuality and Commonsense Reasoning," EECS Technical Report, University of California, Berkeley.1985.
[27] B Khoshnevisan, R. Shahin , O. Mahmoud and M. Hossein (2014): Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs, Information Processing in Agriculture 1, pp. 14–22, 2014.
[28] ITU-R P. 311-15 ‘Acquisition, presentation and analysis of data in studies of tropospheric propagation. Recommendations, 2013.
[1] A.W. Dissanayake, J.E Allnutt, and F Haidara, “A prediction model that combines rain attenuation and other propagation impairments along earth-satellite paths," IEEE Trans on Antennas & Propagation, vol. 45, no. 10, pp. 1546-1558, 1997
[2] S. Katiyar, A textbook on Satellite communication, 3rd ed., published by S. K. Kataria and Sons, New Delhi, 2013.
[3] R. K, Crane, “Prediction of the effects of rain on satellite communication systems” in Proc. of the IEEE, 1977, vol. 65, no. 3, pp. 456-474.
[4] C. Capsoni, F. Fedi, C. Magistroni, A. Pawlina, and A. Paraboni A. “Data and theory for a new model of the horizontal structure of rain cells for propagation applications, Radio Science, vol. 22, no. 33, pp. 395-404, 1987.
[5] L. J Ippolito. "Propagation effects and system performance considerations for satellite communications above 10 GHz," Proc. of the IEEE, Global Telecommunications Conference, 1990, and Exhibition. 'Communications: Connecting the Future', GLOBECOM '90, 2-5 December, 1990.
[6] X. Boulanger, G., Benjamin, L. Casadebaig, and C. Laurent. “Four Years of Total Attenuation Statistics of Earth-Space Propagation Experiments at Ka-Band in Toulouse”, IEEE Transactions on Antennas and Propagation, vol. 63 no. 5, pp. 2203-2214, 2015
[7] S Shrestha and D. Choi Rain attenuation statistics over millimeter wave bands in South Korea, Journal of Atmospheric and Solar- Terrestrial Physics, vol. 152–153, Pp 1-10, 2017
[8] J. S, Ojo, “Geo-spatial distribution of cloud cover and influence of cloud induced attenuation and noise temperature on satellite signal propagation over Nigeria” Advances in Space Research, vol 59, no. 10, pp. 2611-2622, 2017
[9] .S.C. Lu, “Quality of service for personal satellite multimedia”, IEEE Seminar on Personal Broadband Satellite (Ref. No. 2002/059), Jan. 2002, London U.K, 10.1049/ic:20020021. 2002.
[10] L. Ki-Dong, “Variable-target admission control for nonstationary handover traffic in LEO satellite networks”, IEEE Transactions on Vehicular Technology, vol: 54, no 1, pp. 127 – 135, 2005.
[11] M Saraireh, R. Saatchi, S. Al-Khayatt and R Strachan, “Assessment and improvement of quality of service in wireless networks using fuzzy and hybrid genetic-fuzzy approaches,” Artificial Intelligent Review, 27, pp. 95–111, 2008.
[12] K Harb, Omair Butt, Samir Abdul-Jauwad, Abdul-Aziz M. Al-Yami, “Systems Adaptation for Satellite Signal under Dust, Sand and Gaseous Attenuations”, Journal of Wireless Networking and Communications, vol.3, no.3, pp. 39-49, 2013.
[13] R. Z Dhafer, S. Thulfiqar Aldeen A. AL Wahab (2013), “Simplified the QoS Factor for the Adhoc Network sing Fuzzy Technique” Int. J. Communications, Network and System Sciences, vol. 6, pp. 381-387, 2013.
[14] J.S Ojo and P.A Owolawi, “Prediction of Time-series Rain Attenuation based on Rain Rate using Synthetic Storm Techniques over a Subtropical Region. Southern Africa Telecommunication Networks and Applications (SATNAC-2014) conference proceedings, at Boardwalk, Port Elizabeth, Eastern Cape, South Africa, 31 August - 3 September 2014, 67 – 71.
[15] I. M Atta-ur-Rahman. Qureshib, A. N Malikc and M. T. Naseema. “QoS and rate enhancement in DVB-S2 using fuzzy rule based system”, Journal of Intelligent & Fuzzy Systems, vol. 30, no. 2, pp. 801–810, 2016.
[16] I. Adegbidin, P.A Owolawi, and M.O Odhiambo, “Intelligent Weather Awareness Technique for Mitigating Propagation Impairment at SHF and EHF Satellite Network System in a Tropical Climate”, Research Journal of the South African Institute of Electrical Engineers), vol. 107, no.3, pp. 136 – 145, 2016.
[17] H. S. Asad, K.K Richard and H. Gongsheng, “Hybrid Adaptive Modeling to Enhance Robustness of Real-Time Optimization”, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, vol:11, no 4, pp. 348-352, 2017.
[18] O. O. Obiyemi, J. S. Ojo, and T. S. Ibiyemi “Performance Analysis of Rain Rate Models for Microwave Propagation Designs over Tropical Climate,” Progress in Electromagnetics Research M, vol. 39, pp. 115–122, 2014.
[19] O.M, Durodola, J. S Ojo, and M. O Ajewole, (2017) “Performance of Ku-Band Satellite Signals Received during Rainy Condition in two low Latitude Tropical locations of Nigeria”, Adamawa State University Journal of Scientific Research, vol. 1, no.5, pp. 1-17, 2017.
[20] ITU-Rec. P 618-12,"Propagation data and prediction method required for the design of earth-space telecommunication systems, "Radio wave propagation, International Telecommunication Union Recommendation ITU-R P.6I8-12, 2012.
[21] ITU-Rec. P 838-3. Specific attenuation model for rain for use in prediction methods, International Telecommunication Union Recommendation ITU-R P.838-5, 2005.
[22] E., Lutz, M Werner, and A Jahn.: Satellite systems for personal and broadband communications. Springer, New York, 2000.
[23] K Harb., F. R Yu., P. Dakhal, and A. Srinivasan, "An intelligent QoS control system for satellite networks based on Markovian weather," Proc. of IEEE 72nd Conference on Vehicular Technology (VTC)'201O Fall, 6-9 September, 2010, Ottawa, ON, Canada.
[24] A. M. Al-Saegh, A Sali, J. S Mandeep, A. Ismail, Abdulmajeed HJ Al-Jumaily, and C. Gomes: Atmospheric Propagation Model for Satellite Communications, MATLAB Applications for the Practical Engineer, publisher, Intech, 249-275, 2014.
[25] C.C Lee, "Fuzzy Logic in Control Systems: Fuzzy Logic Controller - I, 11," IEEE Transactions on Systems. Man. and Cybernetics, vol. 20, no. 2, pp. 404-435 1990.
[26] L. A., Zadeh, "Fuzzy Sets, Usuality and Commonsense Reasoning," EECS Technical Report, University of California, Berkeley.1985.
[27] B Khoshnevisan, R. Shahin , O. Mahmoud and M. Hossein (2014): Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs, Information Processing in Agriculture 1, pp. 14–22, 2014.
[28] ITU-R P. 311-15 ‘Acquisition, presentation and analysis of data in studies of tropospheric propagation. Recommendations, 2013.
@article{"International Journal of Information, Control and Computer Sciences:77217", author = "Joseph S. Ojo and Vincent A. Akpan and Oladayo G. Ajileye and Olalekan L and Ojo", title = "QoS Improvement Using Intelligent Algorithm under Dynamic Tropical Weather for Earth-Space Satellite Applications", abstract = "In this paper, the intelligent algorithm (IA) that is capable of adapting to dynamical tropical weather conditions is proposed based on fuzzy logic techniques. The IA effectively interacts with the quality of service (QoS) criteria irrespective of the dynamic tropical weather to achieve improvement in the satellite links. To achieve this, an adaptive network-based fuzzy inference system (ANFIS) has been adopted. The algorithm is capable of interacting with the weather fluctuation to generate appropriate improvement to the satellite QoS for efficient services to the customers. 5-year (2012-2016) rainfall rate of one-minute integration time series data has been used to derive fading based on ITU-R P. 618-12 propagation models. The data are obtained from the measurement undertaken by the Communication Research Group (CRG), Physics Department, Federal University of Technology, Akure, Nigeria. The rain attenuation and signal-to-noise ratio (SNR) were derived for frequency between Ku and V-band and propagation angle with respect to different transmitting power. The simulated results show a substantial reduction in SNR especially for application in the area of digital video broadcast-second generation coding modulation satellite networks.
", keywords = "Fuzzy logic, intelligent algorithm, Nigeria, QoS, satellite applications, tropical weather.", volume = "11", number = "9", pages = "1106-7", }
