Performance Analysis of Fiber Attenuation in Passive Optical Networks

The introduction of Fiber Optics cables in broadband Internet distribution has been a game changer in bulk capacity delivery, speed, reliability and penetration. However, the uncurbed incessant existence of cuts and failures have threatened the growth of Internet connectivity as a whole. In this work, the impact of fiber cuts is investigated using a hybrid approach, encompassing both real-world data from a live GPON network and simulations using OptiSystem 12 for FTTH GPON scenarios. Fiber cuts and failures are emulated by introducing varying attenuation levels in the simulated network's feeder cable section within OptiSystem 12, while in the live GPON network, the attenuation is induced by introducing wrap bends in the last-mile patch cord. The findings reveal a consistent pattern in both simulated and live data for both downstream and upstream traffic scenarios. As attenuation levels increased, there was a corresponding decline in Q-factor, Eye Height, and optical power, coupled with a concurrent rise in the minimum BER. Thus, in the most severe scenario, fiber cuts can result in service degradation and eventual service outage. To mitigate this issue, the implementation of a typeB PON protection system with a wireless auto-failover technique is proposed. Adoption and deployment of the proposed technique and deliberate maintenance measures alongside thorough supervision are suggested to be possible solutions to fiber cuts in metropolitan parlance

Developing smart cities through optimal wireless mobile network

Wireless mobile communication has become the interconnecting technological platform through which seamless services of data, voice and other value added services can be deployed within local, national and global platforms. As a means to integrating smart services, the mobile network must be efficient in terms of coverage and quality of service. This paper therefore investigates large scale propagation models used to predict the signal strength with the aim of providing sufficient data required for radio frequency planning and optimization, which will engender flawless mobile network integration and consequent improved quality of service. Data analysis and optimization was carried out using Root Mean Square statistical tool for which the COST231 model was optimized to ensure proper mobile network planning and improved quality of service

Propagation Model Optimization Based on Measurement from Macrocell Sites in Ikorodu-Epe, South-Western Nigeria

To engage any nation in wealth creation, human capacity development and improved standard of living, the need arises to install trendy technological innovations and efficient infrastructures such as reliable and efficient telecommunication systems. This paper therefore, investigates large scale propagation models used to predict the mean signal strength for an arbitrary transmitter-receiver separation distance with the aim of improving the telecommunication system infrastructure which will engender sustainable infrastructure and technology. The study has been conducted in an urban settlement in the Ikorodu – Epe region to develop and optimize a suitable propagation model based on the existing propagation models. The proposed propagation models are the Free Space Path Loss Propagation Model, Okumura – Hata Model, COST 231 Model, SUI Model and Ericsson Model. The optimized COST 231 model showed better performance and is proposed for propagation prediction involving such terrain. Root Mean Square Error (RMSE) statistical tool was used to achieve the optimizatio

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). 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Dietary effect of Pleurotus pulmonaris treated cocoa bean shell meal on fibre fractions utilisation by the West African Dwarf goats

A 63-day study was conducted to evaluate the dietary effect of Pleurotus pulmonaris treated cocoa bean shell meal diets on fibre fraction utilization of West African Dwarf (WAD) goats (aged 9 -12 months) with an average live weight of 6.31±0.3 kg. Six diets were formulated such that wheat offal was replaced with ensiled Pleurotus pulmonaris treated cocoa bean shell meal at ratio 0 (A), 20% (B), 40% (C), 60% (D), 80% (E), 100% (F) in complete diets. The experimental diets were fed to 18 WAD goats in three replicates per treatment using completely randomized design. The determined parameters included; chemical composition of the diets, dry matter and fibre fractions intake, weight gains and feed to gain ratio. The dry matter of raw cocoa bean shells was 88.53% and dry matter of Pleurotus pulmonaris treated cocoa bean shells was 87.38%. The crude protein contents of raw bean shells and Pleurotus pulmonaris treated cocoa bean shells were 11.98 and 26.63% respectively. The dry matter of the diets ranged from 86.83 to 90.70%, diet B had the highest value while crude protein ranged between 19.73 (diet A) and 28.88% (diet F) and increased with increased inclusion of Pleurotus pulmonaris treated cocoa bean shell meal in the diets. The nutrients intake was significantly (P<0.05) influenced by the treatment except dry matter and cellulose. The crude fibre and fibre fractions were efficiently digested. Nitrogen balance, apparent digestibility and weight gain were significantly (P<0.05) influenced by the treatment. The goats fed diet A converted their feed to flesh better than other goats. However, goats fed diet F performed best compared to other goats fed diets B, C, D and E that contained Pleurotus pulmonaris treated cocoa bean shell meal. It can be concluded that Pleurotus pulmonaris treated cocoa bean shell meal incorporated in goat's diet could supply energy and protein tosustain the growth without adverse effect