Abstract
In a typical outdoor environment, the propagation of radio waves is usually random in nature, to the extent that the characterization of the wireless channel often becomes very difficult. Several models have been developed to predict the average Received Signal Strength (RSS) for specified distance ranges. However, the use of deterministic models requires high computational efficiency while the prediction results of empirical models may not be as accurate as required. On machine learning approach, the performances of multi-layered feed-forward network models are limited by slow convergence and local minimum, such that a global optimal solution is not guaranteed. In this paper, Extreme Learning Machine (ELM) algorithm is considered in the development of an optimal path loss prediction model for outdoor propagation scenario. Single Hidden Layer Feed-forward Neural Networks (SHLFNNs) are trained and tested with the path loss data that were computed based on the RSS data of a commercial 1800 MHz base station located along Lagos-Badagry highway in Nigeria. The training speed, learning effectiveness, and the generalization ability of Artificial Neural Network Back-Propagation (ANN-BP) and ELM algorithms are analysed. Experimental results show that ELM models are 140 times faster to train than the ANN-BP models. On prediction accuracy, the outputs of ELM, ANN-BP, Okumura–Hata, and COST-231 models have Root Mean Squared Error (RMSE) values of 2.896, 2.449, 7.456, and 6.116 dB respectively; and regression coefficient (R) values of 0.959, 0.973, 0.935, and 0.935 respectively, when compared to the target variable of the training dataset. When the models were tested with new input data that were excluded from the training process, RMSE values of 4.250, 6.622, 8.732, and 7.087 respectively; and R values of 0.893, 0.876, 0.904, and 0.904 respectively are obtained. In conclusion, the findings of this study confirm that ELM algorithm guarantees an optimal path loss model with fast training convergence, high prediction accuracy, and good generalization ability for radio network planning and optimization in outdoor environments.
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Sanou, B. (2016). ICT facts & figures the world in 2015. ICT data and statistics division telecommunication development Bureau International Telecommunication Union. 2015.
El-Sayed, M., Mukhopadhyay, A., Urrutia-Valdés, C., & Zhao, Z. J. (2011). Mobile data explosion: Monetizing the opportunity through dynamic policies and QoS pipes. Bell Labs Technical Journal, 16, 79–99.
Ranganathan, P. (2011). From microprocessors to nanostores: Rethinking data-centric systems. Computer, 44, 39–48.
Han, B., Hui, P., Kumar, V. A., Marathe, M. V., Shao, J., & Srinivasan, A. (2012). Mobile data offloading through opportunistic communications and social participation. IEEE Transactions on Mobile Computing, 11, 821–834.
Mcqueen, D. (2009). The momentum behind LTE adoption [sGPP LTE]. IEEE Communications Magazine, 47, 44–45.
Cha, I., Shah, Y., Schmidt, A. U., Leicher, A., & Meyerstein, M. V. (2009). Trust in M2M communication. IEEE Vehicular Technology Magazine, 4, 69–75.
Chen, Y., & Wang, W. (2010). Machine-to-machine communication in LTE-A. In Vehicular Technology Conference Fall (VTC 2010-Fall), 2010 IEEE 72nd, pp. 1–4.
Fadlullah, Z. M., Fouda, M. M., Kato, N., Takeuchi, A., Iwasaki, N., & Nozaki, Y. (2011). Toward intelligent machine-to-machine communications in smart grid. IEEE Communications Magazine, 49, 60–65.
Niyato, D., Xiao, L., & Wang, P. (2011). Machine-to-machine communications for home energy management system in smart grid. IEEE Communications Magazine, 49, 53–59.
Lien, S.-Y., Chen, K.-C., & Lin, Y. (2011). Toward ubiquitous massive accesses in 3GPP machine-to-machine communications. IEEE Communications Magazine, 49, 475.
Starsinic, M. (2010). System architecture challenges in the home M2M network. In Applications and Technology Conference (LISAT), 2010 Long Island Systems, pp. 1–7.
Lu, R., Li, X., Liang, X., Shen, X., & Lin, X. (2011). GRS: The green, reliability, and security of emerging machine to machine communications. IEEE Communications Magazine, 49, 53–59.
Zhang, Y., Yu, R., Xie, S., Yao, W., Xiao, Y., & Guizani, M. (2011). Home M2M networks: Architectures, standards, and QoS improvement. IEEE Communications Magazine, 49, 36–43.
Lien, S.-Y., & Chen, K.-C. (2011). Massive access management for QoS guarantees in 3GPP machine-to-machine communications. IEEE Communications Letters, 15, 311–313.
Jian, W.-S., Hsu, M.-H., Sukati, H., Syed-Abdul, S., Scholl, J., Dube, N., et al. (2012). LabPush: A pilot study of providing remote clinics with laboratory results via short message service (SMS) in Swaziland, Africa. PLoS ONE, 7, e44462.
Liyanagunawardena, T. R., Adams, A. A., & Williams, S. A. (2013). MOOCs: A systematic study of the published literature 2008–2012. The International Review of Research in Open and Distributed Learning, 14, 202–227.
Liyanagunawardena, T. R., Williams, S., & Adams, A. A. (2014). The impact and reach of MOOCs: A developing countries’ perspective. eLearning Papers, 38–46.
Liu, Y., Han, W., Zhang, Y., Li, L., Wang, J., & Zheng, L. (2016). An Internet-of-Things solution for food safety and quality control: A pilot project in China. Journal of Industrial Information Integration, 3, 1–7.
Wang, R., Hu, H., & Yang, X. (2014). Potentials and challenges of C-RAN supporting multi-RATs toward 5G mobile networks. IEEE Access, 2, 1187–1195.
Osseiran, A., Boccardi, F., Braun, V., Kusume, K., Marsch, P., Maternia, M., et al. (2014). Scenarios for 5G mobile and wireless communications: The vision of the METIS project. IEEE Communications Magazine, 52, 26–35.
Fettweis, G. P. (2014). The tactile internet: Applications and challenges. IEEE Vehicular Technology Magazine, 9, 64–70.
Oseni, O. F., Popoola, S. I., Enumah, H., & Gordian, A. (2014). Radio frequency optimization of mobile networks in Abeokuta, Nigeria for improved quality of service. International Journal of Research in Engineering and Technology, 3, 174–180.
Bhushan, N., Li, J., Malladi, D., Gilmore, R., Brenner, D., Damnjanovic, A., et al. (2014). Network densification: The dominant theme for wireless evolution into 5G. IEEE Communications Magazine, 52, 82–89.
Luebbers, R. (1984). Propagation prediction for hilly terrain using GTD wedge diffraction. IEEE Transactions on Antennas and Propagation, 32, 951–955.
Mohtashami, V., & Shishegar, A. (2012). Modified wavefront decomposition method for fast and accurate ray-tracing simulation. IET Microwaves, Antennas and Propagation, 6, 295–304.
Hufford, G. A. (1952). An integral equation approach to the problem of wave propagation over an irregular surface. Quarterly of Applied Mathematics, 9, 391–404.
Zelley, C. A., & Constantinou, C. C. (1999). A three-dimensional parabolic equation applied to VHF/UHF propagation over irregular terrain. IEEE Transactions on Antennas and Propagation, 47, 1586–1596.
Hata, M. (1980). Empirical formula for propagation loss in land mobile radio services. IEEE Transactions on Vehicular Technology, 29, 317–325.
Erceg, V., Greenstein, L. J., Tjandra, S. Y., Parkoff, S. R., Gupta, A., Kulic, B., Julius, A. A. & Bianchi, R. (1999). An empirically based path loss model for wireless channels in suburban environments. IEEE Journal on Selected Areas in Communications, 17(7), 1205–1211. https://doi.org/10.1109/49.778178.
Popoola, S. I., & Oseni, O. F. (2014). Performance evaluation of radio propagation models on GSM network in urban area of Lagos, Nigeria. International Journal of Scientific & Engineering Research, 5, 1212–1217.
Oseni, O. F., Popoola, S. I., Abolade, R. O., & Adegbola, O. A. (2014). Comparative analysis of received signal strength prediction models for radio network planning of GSM 900 MHz in Ilorin, Nigeria. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 4, 45–50.
Popoola, S. I., & Oseni, O. F. (2014). Empirical path loss models for GSM network deployment in Makurdi, Nigeria. International Refereed Journal of Engineering and Science, 3, 85–94.
Rath, H. K., Verma, S., Simha, A., & Karandikar, A. (2016). Path loss model for Indian terrain-empirical approach. In Communication (NCC), 2016 Twenty Second National Conference on, pp. 1–6.
Al Salameh, M. S., & Al-Zu’bi, M. M. (2015). Prediction of radiowave propagation for wireless cellular networks in Jordan. In Knowledge and Smart Technology (KST), 2015 7th International Conference on, pp. 149–154.
Nimavat, V. D., & Kulkarni, G. (2012). Simulation and performance evaluation of GSM propagation channel under the urban, suburban and rural environments. In Communication, Information & Computing Technology (ICCICT), 2012 International Conference on, pp. 1–5.
Ibhaze, A. E., Ajose, S. O., Atayero, A. A.-A., & Idachaba, F. E. (2016). Developing smart cities through optimal wireless mobile network. In Emerging Technologies and Innovative Business Practices for the Transformation of Societies (EmergiTech), IEEE International Conference on, pp. 118–123.
Faruk, N., Adediran, Y. A., & Ayeni, A. A. (2013). Error bounds of empirical path loss models at vhf/uhf bands in kwara state, Nigeria. In EUROCON, 2013 IEEE, pp. 602–607.
Faruk, N., Ayeni, A., & Adediran, Y. A. (2013). On the study of empirical path loss models for accurate prediction of TV signal for secondary users. Progress in Electromagnetics Research B, 49, 155–176.
Popoola, S. I., Atayero, A. A., Faruk, N., Calafate, C. T., Olawoyin, L. A., & Matthews, V. O. (2017). Standard propagation model tuning for path loss predictions in built-up environments. In International Conference on Computational Science and its Applications, pp. 363–375.
Popoola, S. I., Atayero, A. A., Faruk, N., Calafate, C. T., Adetiba, E., & Matthews, V. O. (2017). Calibrating the standard path loss model for urban environments using field measurements and geospatial data. In Proceedings of the World Congress on Engineering.
Bhuvaneshwari, A., Hemalatha, R., & Satyasavithri, T. (2017). Performance evaluation of dynamic neural networks for mobile radio path loss prediction, pp. 461–466.
Benmus, T. A., Abboud, R., & Shatter, M. K. (2016). Neural network approach to model the propagation path loss for great Tripoli area at 900, 1800, and 2100 MHz bands, pp. 793–798.
Zineb, A. B., & Ayadi, M. (2016). A multi-wall and multi-frequency indoor path loss prediction model using artificial neural networks. Arabian Journal for Science and Engineering, 41, 987–996.
Angeles, J. C. D., & Dadios, E. P. (2015). Neural network-based path loss prediction for digital TV macrocells. In 2015 international conference on humanoid, nanotechnology, information technology, communication and control, environment and management (HNICEM), Cebu City (pp. 1–9).
Zaarour, N., Affes, S., Kandil, N., & Hakem, N. (2015). Comparative study on a 60 GHz path loss channel modeling in a mine environment using neural networks. In 2015 IEEE International conference on ubiquitous wireless broadband (ICUWB), Montreal, QC (pp. 1–4). https://doi.org/10.1109/ICUWB.2015.7324427.
Sotiroudis, S. P., & Siakavara, K. (2015). Mobile radio propagation path loss prediction using artificial neural networks with optimal input information for urban environments. AEU—International Journal of Electronics and Communications, 69, 1453–1463.
Sotiroudis, S. P., Goudos, S. K., Gotsis, K. A., Siakavara, K., & Sahalos, J. N. (2013). Optimal artificial neural network design for propagation path-loss prediction using adaptive evolutionary algorithms. In 2013 7th European conference on antennas and propagation (EuCAP), Gothenburg (pp. 3795–3799).
Fernández Anitzine, I., Romo Argota, J. A., & Fontán, F. P. (2012). Influence of training set selection in artificial neural network-based propagation path loss predictions. International Journal of Antennas and Propagation, 2012, 1–7. https://dx.doi.org/10.1155/2012/351487.
Hagan, M. T., & Menhaj, M. B. (1994). Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5, 989–993.
Wilamowski, B. M., & Yu, H. (2010). Neural network learning without backpropagation. IEEE Transactions on Neural Networks, 21, 1793–1803.
Chen, S., Cowan, C. F., & Grant, P. M. (1991). Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks, 2, 302–309.
Li, K., Peng, J.-X., & Irwin, G. W. (2005). A fast nonlinear model identification method. IEEE Transactions on Automatic Control, 50, 1211–1216.
Branke, J. (1995). Evolutionary algorithms for neural network design and training. In Proceedings of the First Nordic Workshop on Genetic Algorithms and its Applications, 1995.
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2004). Extreme Learning Machine: A new learning scheme of feedforward neural networks. In Neural Networks, 2004. Proceedings 2004 IEEE International Joint Conference on, 2004, pp. 985–990.
Huang, G.-B., Zhou, H., Ding, X., & Zhang, R. (2012). Extreme Learning Machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42, 513–529.
Huang, G., Huang, G.-B., Song, S., & You, K. (2015). Trends in Extreme Learning Machines: A review. Neural Networks, 61, 32–48.
Huang, G.-B., & Chen, L. (2007). Convex incremental Extreme Learning Machine. Neurocomputing, 70, 3056–3062.
Huang, G.-B., Chen, L., & Siew, C. K. (2006). Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transaction on Neural Networks, 17, 879–892.
Liu, X., Lin, S., Fang, J., & Xu, Z. (2015). Is Extreme Learning Machine feasible? A theoretical assessment (part I). IEEE Transactions on Neural Networks and Learning Systems, 26, 7–20.
Huang, G., Song, S., & Wu, C. (2012). Orthogonal least squares algorithm for training cascade neural networks. IEEE Transactions on Circuits and Systems I: Regular Papers, 59, 2629–2637.
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006). Extreme Learning Machine: Theory and applications. Neurocomputing, 70, 489–501.
Wang, G., Zhao, Y., & Wang, D. (2008). A protein secondary structure prediction framework based on the Extreme Learning Machine. Neurocomputing, 72, 262–268.
Lan, Y., Soh, Y. C., & Huang, G. B. (2008). Extreme Learning Machine based bacterial protein subcellular localization prediction. In IEEE international joint conference on neural networks (IEEE world congress on computational intelligence), Hong Kong (pp. 1859–1863).
Bhat, A. U., Merchant, S. S., & Bhagwat, S. S. (2008). Prediction of melting points of organic compounds using Extreme Learning Machines. Industrial and Engineering Chemistry Research, 47, 920–925.
Yadav, B., Ch, S., Mathur, S., & Adamowski, J. (2017). Assessing the suitability of Extreme Learning Machines (ELM) for groundwater level prediction. Journal of Water and Land Development, 32, 103–112.
Zhang, J., & Ding, W. (2017). Prediction of air pollutants concentration based on an Extreme Learning Machine: The case of Hong Kong. International Journal of Environmental Research and Public Health, 14, 114. https://doi.org/10.3390/ijerph14020114.
Dong, F., Liu, J., He, L., Hu, X., & Liu, H. (2016). Channel estimation based on Extreme Learning Machine for high speed environments. In Proceedings of ELM-2015 (Vol. 1, pp. 159–167), Springer.
Liu, J., Jin, X., Dong, F., He, L., & Liu, H. (2017). Fading channel modelling using single-hidden layer feedforward neural networks. Multidimensional Systems and Signal Processing, 28, 885–903.
Evans, G., Joslin, B., Vinson, L., & Foose, B. (1997). The optimization and application of the wcy lee propagation model in the 1900 mhz frequency band. In Vehicular Technology Conference, 1997, IEEE 47th, pp. 87–91.
Powers, D. M. (2012). ROC-ConCert: ROC-based measurement of consistency and certainty. In Engineering and Technology (S-CET), 2012 Spring Congress on, pp. 1–4.
Acknowledgement
This work was carried out under the IoT-Enabled Smart and Connected Communities (SmartCU) research cluster at Covenant University, Ota, Nigeria. This research is fully sponsored by Covenant University Centre for Research, Innovation and Development (CUCRID), Covenant University, Ota, Nigeria.
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Popoola, S.I., Misra, S. & Atayero, A.A. Outdoor Path Loss Predictions Based on Extreme Learning Machine. Wireless Pers Commun 99, 441–460 (2018). https://doi.org/10.1007/s11277-017-5119-x
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DOI: https://doi.org/10.1007/s11277-017-5119-x