Effect of Path Loss Propagation Model on the Position Estimation Accuracy of a 3-Dimensional Minimum Configuration Multilateration System

The 3-Dimensional (3-D) position estimation (PE) accuracy of a multilateration (MLAT) system depends on several factors one of which is the accuracy at which the time difference of arrival (TDOA) measurements are obtained. In this paper, signal attenuation is considered the major contributor to the TDOA estimation error and the effect of the signal attenuation based on path loss propagation model on the PE accuracy of the MLAT system is determined. The two path loss propagation models are considered namely: Okumura-Hata and the free space path loss (FSPL) model. The transmitter and receiver parameters used for the analysis are based on actual system used in the civil aviation. Monte Carlo simulation result based on square ground receiving station (GRS) configuration and at selected aircraft positions shows that the MLAT system with the Okumura-Hata model has the highest PE error. The horizontal coordinate and altitude error obtained with the Okumura-Hata are 2.5 km and 0.6 km respectively higher than that obtained with the FSPL mode

Future railway mobile communication system automated planning

Dissertação para obtenção do Grau de Mestre em Engenharia Electrónica e de TelecomunicaçõesThis project consists in the development of a didactic Radar system, a functional Com o fim do suporte da rede Global System for Mobile Communications Railway (GSM-R) à vista, é necessário que ocorra uma mudança na tencologia usada em comunicações ferroviárias. Para isso os operadores começaram a transição para Future Railway Mobile Communication System (FRMCS). O trabalho desenvolvido na presente tese demonstra a aplicação do conceito de algoritmos genéticos no planeamento de uma rede de telecomunicações. Onde o objetivo é perceber se os resultados obtidos são viáveis e de boa qualidade em comparação com os valores atualmente praticados. Para isso, é necessário o desenvolvimento de um algoritmo que, de forma eficiente permita obter a melhor solução possível para a colocação das antenas ao longo da linha, tendo em conta a cobertura da mesma. O trabalho desenvolvido incluí a construção deste mesmo algoritmo e de todas as suas fases. Utilizando a linha de Cascais como sujeito de teste e com o auxílio de dados disponibilizados pela empresa Solvit é possível obter diversos cenários variando quatro parâmetros, a dimensão da população, o número de gerações, a probabilidade de cruzamento e a probabilidade de mutação. Os resultados finais comprovam que o uso de algoritmos genéticos para a otimização de uma rede de telecomunicações em ferrovia pode ser uma ferramenta útil e poderosa, uma vez que os resultados obtidos apresentam um valor otimizados em comparação com o valor da solução atual com a parametrização usada.With the end of support for the Global System for Mobile Communications Railway (GSM-R) in sight, there needs to be a change in the technology used in railway communications. To achieve this, operators began the transition to Future Railway Mobile Communication System (FRMCS). The work developed in this thesis demonstrates the application of the concept of genetic algorithms in a telecommunications network planning. Where the objective is to understand whether the results obtained are viable comparing to the currently practiced values. To achieve this, it is necessary to develop an algorithm that allows to obtaining the best possible solution for the placement of antennas along the line in the most efficient way, taking into account its coverage. The work developed includes the construction of this same algorithm and all its phases. Using the Cascais line as a test subject and with the help of data made available by the company Solvit, it is possible to obtain different scenarios varying four parameters, the population size, the number of generations, the crossover probability and the mutation probability. The final results prove that the use of genetic algorithms to optimize a railway telecommunications network can be a useful and powerful tool, as the results obtained presents an optimized value compared to the current solution used by public operators.N/

Experimental Investigation of Land Mobile Prediction Methods and Modeling of Radio Planning Tool Parameters along Indian Rail Road Rural Zones

Mobile communication networks in rural zones were not given enough importance and emphasis unlike their urban counter parts due to the unattractive revenues and economic considerations for the cellular operators. In order to identify the suitable prediction methods for Indian rail road rural zones, train-based measurements were conducted in the northern and western rural zones along rail roads. These were carried out by recording the carriers emitted by the trackside base stations inside the moving train. The observed signal levels converted into path losses were compared initially with various conventional prediction methods. The observed results were also compared with the predicted results of radio planning tool utilizing digital terrain data. The constants of the model incorporated in the radio planning tool were tuned separately for north Indian and west Indian base stations based on the observed results. The suitability of the models has been evaluated in terms of standard statistical parameters

Diagnostic analysis of radio propagation in UMTS networks using high-resolution angle-of-arrival measurements

This work describes high-resolution propagation measurements performed as a diagnostic survey in an operational UMTS network. The results were obtained using the measurement system previously presented in. Measurements were performed in a dense urban environment in Amsterdam, the Netherlands. Results showed that the measurement approach can be used to create a setup that is similar to the actual network scenario, and that is capable of accurately identifying the dominant propagation effects while moving through the environment. The results are especially important for mobile-system operators, because they revealed some of the causes of inadequate propagation prediction. This underlined the limitations of propagation-prediction models currently used by most mobile-system operators, and the importance of accurate propagation information to obtain the optimal network configuration

Wireless Communication Systems for Urban Transport

This chapter describes the main features of the wireless communication systems of urban rail and related applications. The perspective will be complete: application, network and physical layers will be discussed. Moreover, to properly address some of the challenges that these systems face, we will provide a deep insight into propagation issues related to tunnels and urban areas. Finally, a detailed survey on the directions of research on all these topics will be provided

Optimal Model for Path Loss Predictions using Feed-Forward Neural Networks

[EN] In this paper, an optimal model is developed for path loss predictions using the Feed-Forward Neural Network (FFNN) algorithm. Drive test measurements were carried out in Canaanland Ota, Nigeria and Ilorin, Nigeria to obtain path loss data at varying distances from 11 different 1,800 MHz base station transmitters. Single-layered FFNNs were trained with normalized terrain profile data (longitude, latitude, elevation, altitude, clutter height) and normalized distances to produce the corresponding path loss values based on the Levenberg-Marquardt algorithm. The number of neurons in the hidden layer was varied (1-50) to determine the Artificial Neural Network (ANN) model with the best prediction accuracy. The performance of the ANN models was evaluated based on different metrics: Mean Absolute error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), standard deviation, and regression coefficient (R). Results of the machine learning processes show that the FNN architecture adopting a tangent activation function and 48 hidden neurons produced the least prediction error, with MAE, MSE, RMSE, standard deviation, and R values of 4.21 dB, 30.99 dB, 5.56 dB, 5.56 dB, and 0.89, respectively. Regarding generalization ability, the predictions of the optimal ANN model yielded MAE, MSE, RMSE, standard deviation, and R values of 4.74 dB, 39.38 dB, 6.27 dB, 6.27 dB, and 0.86, respectively, when tested with new data not previously included in the training process. Compared to the Hata, COST 231, ECC-33, and Egli models, the developed ANN model performed better in terms of prediction accuracy and generalization ability.This work was supported by Covenant University [grant number CUCRID-SMARTCU-000343].Popoola, SI.; Adetiba, E.; Atayero, AA.; Faruk, N.; Tavares De Araujo Cesariny Calafate, CM. (2018). Optimal Model for Path Loss Predictions using Feed-Forward Neural Networks. Cogent Engineering. 5:1-19. https://doi.org/10.1080/23311916.2018.1444345S1195Adetiba, E., Iweanya, V. C., Popoola, S. I., Adetiba, J. N., & Menon, C. (2017). Automated detection of heart defects in athletes based on electrocardiography and artificial neural network. Cogent Engineering, 4(1). doi:10.1080/23311916.2017.1411220Adetiba, E., & Olugbara, O. O. (2015). Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features. The Scientific World Journal, 2015, 1-17. doi:10.1155/2015/786013Adeyemo, Z. K., Ogunremi, O. K., & Ojedokun, I. A. (2016). Optimization of Okumura-Hata Model for Long Term Evolution Network Deployment in Lagos, Nigeria. International Journal on Communications Antenna and Propagation (IRECAP), 6(3), 146. doi:10.15866/irecap.v6i3.9012Akhoondzadeh-Asl, L., & Noori, N. (2007). Modification and Tuning of the Universal Okumura-Hata Model for Radio Wave Propagation Predictions. 2007 Asia-Pacific Microwave Conference. doi:10.1109/apmc.2007.4554925Al Salameh, M. S., & Al-Zu’bi, M. M. (2015). Prediction of radiowave propagation for wireless cellular networks in Jordan.Paper presented at the Knowledge and Smart Technology (KST), 2015 7th International Conference on.Alamoud, M. A., & Schutz, W. (2012). Okumura-hata model tuning for TETRA mobile radio networks in Saudi Arabia. 2012 2nd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA). doi:10.1109/ictea.2012.6462901Armenta, A., Serrano, A., Cabrera, M., & Conte, R. (2011). The new digital divide: the confluence of broadband penetration, sustainable development, technology adoption and community participation. Information Technology for Development, 18(4), 345-353. doi:10.1080/02681102.2011.625925Begovic, P., Behlilovic, N., & Avdic, E. (2012). Applicability evaluation of Okumura, Ericsson 9999 and winner propagation models for coverage planning in 3.5 GHZ WiMAX systems.Erceg, V., Greenstein, L. J., Tjandra, S. Y., Parkoff, S. R., Gupta, A., Kulic, B., … 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. doi:10.1109/49.778178Farhoud, M., El-Keyi, A., & Sultan, A. (2013). Empirical correction of the Okumura-Hata model for the 900 MHz band in Egypt. 2013 Third International Conference on Communications and Information Technology (ICCIT). doi:10.1109/iccitechnology.2013.6579585Faruk, N., Adediran, Y. A., & Ayeni, A. A. (2013). Error bounds of empirical path loss models at VHF/UHF bands in Kwara State, Nigeria. Eurocon 2013. doi:10.1109/eurocon.2013.6625043Faruk, 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. doi:10.2528/pierb13011306Hata, M. (1980). Empirical formula for propagation loss in land mobile radio services. IEEE Transactions on Vehicular Technology, 29(3), 317-325. doi:10.1109/t-vt.1980.23859Hufford, G. A. (1952). An integral equation approach to the problem of wave propagation over an irregular surface. Quarterly of Applied Mathematics, 9(4), 391-404. doi:10.1090/qam/44350Ibhaze, A. E., Ajose, S. O., Atayero, A. A.-A., & Idachaba, F. E. (2016). Developing smart cities through optimal wireless mobile network.Paper presented at the emerging technologies and innovative business practices for the transformation of societies (EmergiTech), IEEE international conference on.Luebbers, R. (1984). Propagation prediction for hilly terrain using GTD wedge diffraction. IEEE Transactions on Antennas and Propagation, 32(9), 951-955. doi:10.1109/tap.1984.1143449Matthews, V. O., Osuoyah, Q., Popoola, S. I., Adetiba, E., & Atayero, A. A. (2017, July 5–7). C-BRIG: A network architecture for real-time information exchange in smart and connected campuses. In Lecture notes in engineering and computer science: Proceedings of the world congress on engineering 2017 (pp. 398–401). London.Medeisis, A., & Kajackas, A. (s. f.). On the use of the universal Okumura-Hata propagation prediction model in rural areas. VTC2000-Spring. 2000 IEEE 51st Vehicular Technology Conference Proceedings (Cat. No.00CH37026). doi:10.1109/vetecs.2000.851585Mohtashami, V., & Shishegar, A. A. (2012). Modified wavefront decomposition method for fast and accurate ray-tracing simulation. IET Microwaves, Antennas & Propagation, 6(3), 295. doi:10.1049/iet-map.2011.0264Nimavat, V. D., & Kulkarni, G. (2012). Simulation and performance evaluation of GSM propagation channel under the urban, suburban and rural environments.Paper presented at the communication, information & computing technology (ICCICT), 2012 international conference on.. O. F. O. (2014). RADIO FREQUENCY OPTIMIZATION OF MOBILE NETWORKS IN ABEOKUTA, NIGERIA FOR IMPROVED QUALITY OF SERVICE. International Journal of Research in Engineering and Technology, 03(08), 174-180. doi:10.15623/ijret.2014.0308027Phillips, C., Sicker, D., & Grunwald, D. (2013). A Survey of Wireless Path Loss Prediction and Coverage Mapping Methods. IEEE Communications Surveys & Tutorials, 15(1), 255-270. doi:10.1109/surv.2012.022412.00172Popoola, S. I., Atayero, A. A., Badejo, J. A., John, T. M., Odukoya, J. A., & Omole, D. O. (2018). Learning analytics for smart campus: Data on academic performances of engineering undergraduates in Nigerian private university. Data in Brief, 17, 76-94. doi:10.1016/j.dib.2017.12.059Popoola, S. I., Atayero, A. A., & Faruk, N. (2018). Received signal strength and local terrain profile data for radio network planning and optimization at GSM frequency bands. Data in Brief, 16, 972-981. doi:10.1016/j.dib.2017.12.036Popoola, S. I., Atayero, A. A., Faruk, N., & Badejo, J. A. (2018). Data on the key performance indicators for quality of service of GSM networks in Nigeria. Data in Brief, 16, 914-928. doi:10.1016/j.dib.2017.12.005Popoola, S. I., Atayero, A. A., Faruk, N., Calafate, C. T., Adetiba, E., & Matthews, V. O. (2017, July 5–7). Calibrating the standard path loss model for urban environments using field measurements and geospatial data.Paper presented at the Lecture notes in engineering and computer science: Proceedings of the world congress on engineering 2017 (pp. 513–518). London.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.Paper presented at the International Conference on Computational Science and Its Applications.Popoola, S. I., Atayero, A. A., Okanlawon, T. T., Omopariola, B. I., & Takpor, O. A. (2018). Smart campus: Data on energy consumption in an ICT-driven university. Data in Brief, 16, 780-793. doi:10.1016/j.dib.2017.11.091Popoola, S. I., Badejo, J. A., Ojewande, S. O., & Atayero, A. (2017, October 25–27). Statistical evaluation of quality of service offered by GSM network operators in Nigeria. In Lecture notes in engineering and computer science: Proceedings of the world congress on engineering and computer science 2017 (pp. 69–73). San Francisco.Popoola, S. I., Misra, S., & Atayero, A. A. (2018). Outdoor path loss predictions based on extreme learning machine. Wireless Personal Communications, 1–20.Rath, H. K., Verma, S., Simha, A., & Karandikar, A. (2016). Path Loss model for Indian terrain-empirical approach.Paper presented at the communication (NCC), 2016 twenty second national conference on.Salman, M. A., Popoola, S. I., Faruk, N., Surajudeen-Bakinde, N., Oloyede, A. A., & Olawoyin, L. A. (2017). Adaptive neuro-fuzzy model for path loss prediction in the VHF band.Paper presented at the computing networking and informatics (ICCNI), 2017 international conference on.Schneider, I., Lambrecht, F., & Baier, A. (s. f.). Enhancement of the Okumura-Hata propagation model using detailed morphological and building data. Proceedings of PIMRC ’96 - 7th International Symposium on Personal, Indoor, and Mobile Communications. doi:10.1109/pimrc.1996.567508Sotiroudis, 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(10), 1453-1463. doi:10.1016/j.aeue.2015.06.014Zelley, 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(10), 1586-1596. doi:10.1109/8.80590

Energy efficiency in cellular wireless networks

Energy efficiency of Long Term Evolution (LTE) cellular communication networks has become a major concern for network operators, not only to reduce the operational costs, but also to reduce their environmental effects. Within LTE cellular networks, base stations are responsible for most of the energy consumption, consuming 70-95% or more of the network power depending on the network topology, configuration, radio technology and data rates that are used. Power control is an important function in cellular wireless networks and refers to setting the output power levels of transmitters, termed eNodeB in the downlink and user equipment (UEs) in the uplink. LTE utilizes two different mechanisms for uplink power control: Open Loop Power Control (OLPC) and Closed Loop Power Control (CLPC). Uplink OLPC is performed by the UE following eNodeB configuration and can compensate for long term channel variation such as path loss and shadowing. The uplink CLPC mechanism attempts to improve power control performance by compensating fast channel variations due to multipath fading. In CLPC the eNodeB sends Transmit Power Control (TPC) commands to the UE to adjust the UE’s transmit power. This thesis focuses on an Open Loop Power Control (OLPC) scheme for LTE uplink by using the Okumura-Hata propagation path loss model to set the User Equipment (UE) uplink transmit power control parameters in order to reduce the UE energy consumption. In general, the UE requires more power to connect to distant base stations than closer base stations and therefore this thesis analyses the required power levels using the Okumura-Hata propagation path loss model. Estimation of path loss is very important in initial deployment of wireless network and cell planning. This thesis analyses the Okumura-Hata propagation path loss in different receiver antenna heights (

An Improved White Space Prediction Algorithm for Cognitive Radio Systems

Cognitive radio (CR) is a promising technology to enhance the current low usage of limited frequency resources. TV white space (TVWS) - TV bands at a particular time in a particular geographic area that are not being used by licensed services - is perceived as the most suitable frequency bands for CR. This paper proposes a new prediction TVWS algorithm for CR systems based on the ITU 1546.1 and the Okumura-Hata models. The proposed algorithm is verified with the data of 22 provinces in the South of Vietnam. The numerical results confirm the advantage of the proposed algorithm as well as the possibility of TVWS CR networks