Site-Specific Assessment of Node B using Key Service Quality Indicators over 3G/UMTS Networks from Outdoor Drive-Test Measurements

Periodic service quality monitoring of a deployed cellular communication network by means of an innovative expert-driven field test analysis provides an in-depth understanding of the status and performances of the network as well as of the statistical behaviour of the user population. Such knowledge allows for a better engineering and operation of the whole network, and specifically the early detection of hidden risks and emerging troubles. In this paper, an experimental performance assessment of Node B based on key quality parameters considered for design, planning and network optimization was carried out via drive test at Ugbor avenue, BIU Campus and Gapiona avenue, all located in G. R. A, Benin City. It was established that the Ec/lo range measured for BEN 035 (BIU) indicates that the BS will be able to support services demanded by more subscribers accessing the network. Proper tuning is required on this BTS to eliminate the possibility of noise interference by this BS on nearby BSs when the loading is low. It was discovered that the QoS is very poor in the environs of BEN026, with the result that UEs will not be able to access data due to rapid data rate decreases, network login difficulty, difficulty in call initiation, no network, and high call drop rate. Hence the CPICH power level should be adjusted so that base station can provide service to users; however this does not guarantee that the interference caused by other nearby base stations is within the acceptable range to establish the session. At BEN 098 (Gapiona avenue) the Ec/lo is below the standard which is -9 dB for data which low means the QoS at Gapiona avenue is very poor and can only serve for voice calls with no data capacity whatsoever. The bond between RSSI/RSCP and Ec/Io performance at the different measurement locations of Node B were also assessed. It was observed from the results that Ec/Io degrades when RSSI/RSCP decreases. Degrading Ec/Io can be an indication of increased other cell interference which will also increase the need for downlink traffic power

A PRACTICAL OPTIMISATION METHOD TO IMPROVE QOS AND GOS-BASED KEY PERFORMANCE INDICATORS IN GSM NETWORK CELL CLUSTER ENVIRONMENT

ABSTRACT The delivering of both good quality of service (QoS) 45%, 87.74%, and 92.85% to 99.05%, 95.38% and 99.03% respectively across the three GSM cell clusters. The GoS is reduced from 3. 33%, 6.60% and 2.38% to 0.00%, 3.70% and 0.00% respectively. Furthermore, ESA, which correspond end points service availability, has improved from 94.44%, 93.40% and 97.62% to 100%, 96.30% and 100% respectively. I

Investigating Predictive Capabilities of RBFNN, MLPNN and GRNN Models for LTE Cellular Network Radio Signal Power Datasets

Efficient radio frequency signal coverage planning with well configured transmitters and receivers’ communication channels, is the heart of any cost-effective cellular network design, deployment and operation. It ensures that both network quality and coverage are simultaneously make best use of (i.e. maximized). This work aim to appraise the adaptive learning and predictive capacity of three neural network models on spatial radio signal power datasets obtained from commercial LTE cellular networks. The neural network models are radial basis function neural network (RBFNN), multilayer perceptron neural network (MLPNN) trained with Bayesian regulation algorithms and general regression neural network (GRNN) models.  Largely, it is established from the results that ANN prediction methods can tolerate and adapt to measurement errors of attenuating LTE radio signals. Performance comparisons reveal that all the neural network models can predict the propagated LTE radio signals with considerable errors. Specifically, RBFNN delivered the overall best performance with the smallest mean absolute percentage error, root mean square error, mean absolute error and standard deviation values. The GRNN model also gave better prediction results with marginal errors compared to the MLPNN. Thus, the predictive abilities of RBFNN and GRNN models can be explored as a useful tool to successfully plan or fine-tune mobile radio signal coverage area. Keywords: Neural networks; Signal power; attenuating radio signals; radial basis function multilayer perceptron, general regression neural network, Adaptive signal predictio

Radio Field Strength Propagation Data and Pathloss calculation Methods in UMTS Network

The design of future-generation mobile communication systems depends critically on the pathloss prediction methods and their suitability to various signal propagation regions.  An accurate estimation of radio pathloss is useful for predicting coverage areas of base stations, frequency assignments, determination of electric field strength, interference analysis, handover optimization, and power level adjustment. The radio path loss will also affect other elements such as the required receiver sensitivity, the form of transmission used and several other factors. As a result, it is necessary to understand the reasons for radio path loss, and to be able to determine the levels of the signal loss for a given radio path. In this paper, we investigated the radio signal path attenuation behavior, by conducting an experimental measurement survey in a UMTS network transmitting at 2100MHz band in Government Reservation Area (GRA), Benin City. The measured field strength data collected at various distances from the base stations was used to estimate the pathloss. Firstly, the effect of different parameters, such as distance from base stations was studied and it is observed that path loss increases with distance from the signal source due to a corresponding decrease in field strength. Secondly, the calculated pathloss data have been compared with data from other existing pathloss prediction methods. We find that the Okumura-Hata model pathloss values were closest of all the propagation models considered classifying the environment into consideration. Thus, the performance of Okumura-Hata model shows its suitability for path attenuation loss prediction in UMTS networks in GRA

A simulation agent for efficient network evaluation in 3G cellular mobile radio planning

An accurate procedure for evaluating the strong correlation and conflicting goals of coverage, capacity and Quality of Service (QoS) is required for efficient planning of 3G radio networks. In this paper, we explore and implement an agent-based simulation methodology which allows for the fine-tuning of the input model parameters to study and evaluate the performance of Code Division Multiple Access (CDMA) systems. The results obtained are represented graphically to show scenarios for both uplink and downlink limited CDMA. The analysis is extended to demonstrate how Tower Mounted Amplifiers (TMA) can be used to benefit the uplink performance of a 1× EV-D0 data system.Facultad de Informátic

Optimised MLP Neural Network Model for Optimum Prognostic Learning of out of School Children Trend in Africa: Implication for Guidance and Counselling

One crucial and intricate problem in the education sector that must be dealt with is children who initially enrolled in schools but later dropped out before finishing mandatory primary education. These children are generally referred to as out-of-school children. To contribute to the discuss, this paper presents the development of a robust Multilayer Perceptron (MLP) based Neural Network Model (NN) for optimal prognostic learning of out-of-school children trends in Africa. First, the Bayesian optimization algorithm has been engaged to determine the best MLP hyperparameters and their specific training values. Secondly, MLP-tuned hyperparameters were employed for optimal prognostic learning of different out-of-school children data trends in Africa. Thirdly, to assess the proposed MLP-NN model's prognostic performance, two error metrics were utilized, which are the Correlation coefficient (R) and Normalized root means square error (NRMSE). Among other things, a higher R and lower NRMSE values indicate a better MLP-NN precision performance. The all-inclusive results of the developed MLP-NN model indicate a satisfactory prediction capacity, attaining low NRMSE values between 0.017 - 0.310 during training and 0.034 - 0.233 during testing, respectively. In terms of correlation fits, the out-of-school children's data and the ones obtained with the developed MLP-NN model recorded high correlation precision training/testing performance values of 0.9968/0.9974, 0.9801/0.9373, 0.9977/0.9948 and 0.9957/0.9970, respectively. Thus, the MLP-NN model has made it possible to reliably predict the different patterns and trends rate of out-of-school children in Africa. One of the implications for counselling, among others, is that if every African government is seriously committed to funding education at the foundation level, there would be a reduction in the number of out-of-school children as observed in the out-of-school children data

Macrocellular Propagation Prediction for Wireless Communications in Urban Environments

In this paper, the signal propagation characteristics of urban environments are predicted by modeling important propagation parameters of the Code Division Multiple Access (CDMA) network for macrocells. The MOPEM propagation model has been selected as a model of choice due to its robustness in handling urban parameters. The model is simulated with some modifications using empirical data from the Visafone CDMA gathered from the Nigerian Telecommunications Limited (NITEL), Uyo, Nigeria and data from the field. From the simulation, we observe that propagation model parameters such as orientation angel, street width, building height, among others, has great influence on the system performance of CDMA wireless networks. We hope that with this research, systems designers could approach the installation of radio frequency equipment with some degree of confidence that the transmission link will effectively work, especially in urban areas.Facultad de Informátic

Statistical and Machine Learning Approach for Robust Assessment Modelling of Out-of-School Children Rate: Global Perspective

The negative impact of out-of-school students' problems at the basic and high-school levels is always very weighty on the affected individuals, parents, and society at large. Owing to the weighty negative consequences, policymakers, different government agencies, educators and researchers have long been looking for how to effectively study and forecast the trends as a means of offering a concrete solution to the problem. This paper develops a better hybrid machine learning method, which combines the least square and support vector machine (LS-SVM) model for robust prediction improvement of out-of-school children trend patterns. Particularly, while other previous works only engaged some regional and few samples of out-of-school datasets, this paper focused on long-ranged global out-of-school datasets, collated by UNESCO between 1975- 2020. The proposed hybrid method exhibits the optimal precision accuracies with the LS-SVM model in comparison with ones made using the ordinary SVM model. The precision performance of both LS-SVM and SVM was quantified and a lower NRMSE value is preferred. From the results, the LS-SVM attained lower error values of 0.0164, 0.0221, 0.0268, 0.0209, 0.0158, 0.0201, 0.0147 and 0.0095 0.0188, compared to the SVM model that attained higher NRMSE values of 0.041, ,0.0628, 0.0381, 0.0490, 0.0501, 0.0493, 0.0514, 0.0617 and 0.0646, respectively. By engaging the MAPE indicator, which expresses the mean disconnection between the sourced and predicted values of the out-of-school data. By means of the MAPE, LS-SVM attained lower error values of 0.51, 1.88, 0.82, 2.38, 0.62, 2.55, 0.60, 0.60, 1.63 while SVM attained 1.83, 7.39, 1.79 7.01, 2.43, 8.79, 2.58, 4.13, 6.18. This implies that the LS-SVM model has better precision performance than the SVM model. The results attained in this work can serve as an excellent guide on how to explore hybrid machine-learning techniques to effectively study and predict out-of-school students among researchers and educators

Atmospheric Propagation Modelling for Terrestrial Radio Frequency Communication Links in a Tropical Wet and Dry Savanna Climate

Atmospheric impairment-induced attenuation is the prominent source of signal degradation in radio wave communication channels. The computation-based modeling of radio wave attenuation over the atmosphere is the stepwise application of relevant radio propagation models, data, and procedures to effectively and prognostically estimate the losses of the propagated radio signals that have been induced by atmospheric constituents. This contribution aims to perform a detailed prognostic evaluation of radio wave propagation attenuation due to rain, free space, gases, and cloud over the atmosphere at the ultra-high frequency band. This aim has been achieved by employing relevant empirical atmospheric data and suitable propagation models for robust prognostic modeling using experimental measurements. Additionally, the extrapolative attenuation estimation results and the performance analysis were accomplished by engaging different stepwise propagation models and computation parameters often utilized in Earth–satellite and terrestrial communications. Results indicate that steady attenuation loss levels rise with increasing signal carrier frequency where free space is more dominant. The attenuation levels attained due to rain, cloud, atmospheric gases, and free space are also dependent on droplet depths, sizes, composition, and statistical distribution. While moderate and heavy rain depths achieved 3 dB and 4 dB attenuations, the attenuation due to light rainfall attained a 2.5 dB level. The results also revealed that attenuation intensity levels induced by atmospheric gases and cloud effects are less than that of rain. The prognostic-based empirical attenuation modeling results can provide first-hand information to radio transmission engineers on link budgets concerning various atmospheric impairment effects during radio frequency network design, deployment, and management, essentially at the ultra-high frequency band

Machine Learning-Based Boosted Regression Ensemble Combined with Hyperparameter Tuning for Optimal Adaptive Learning

Over the past couple of decades, many telecommunication industries have passed through the different facets of the digital revolution by integrating artificial intelligence (AI) techniques into the way they run and define their processes. Relevant data acquisition, analysis, harnessing, and mining are now fully considered vital drivers for business growth in these industries. Machine learning, a subset of artificial intelligence (AI), can assist, particularly in learning patterns in big data chunks, intelligent extrapolative extraction of data and automatic decision-making in predictive learning. Firstly, in this paper, a detailed performance benchmarking of adaptive learning capacities of different key machine-learning-based regression models is provided for extrapolative analysis of throughput data acquired at the different user communication distances to the gNodeB transmitter in 5G new radio networks. Secondly, a random forest (RF)-based machine learning model combined with a least-squares boosting algorithm and Bayesian hyperparameter tuning method for further extrapolative analysis of the acquired throughput data is proposed. The proposed model is herein referred to as the RF-LS-BPT method. While the least-squares boosting algorithm is engaged to turn the possible RF weak learners to form stronger ones, resulting in a single strong prediction model, the Bayesian hyperparameter tuning automatically determines the best RF hyperparameter values, thereby enabling the proposed RF-LS-BPT model to obtain desired optimal prediction performance. The application of the proposed RF-LS-BPT method showed superior prediction accuracy over the ordinary random forest model and six other machine-learning-based regression models on the acquired throughput data. The coefficient of determination (Rsq) and mean absolute error (MAE) values obtained for the throughput prediction at different user locations using the proposed RF-LS-BPT method range from 0.9800 to 0.9999 and 0.42 to 4.24, respectively. The standard RF models attained 0.9644 to 0.9944 Rsq and 5.47 to 12.56 MAE values. The improved throughput prediction accuracy of the proposed RF-LS-BPT method demonstrates the significance of hyperparameter tuning/optimization in developing precise and reliable machine-learning-based regression models. The projected model would find valuable applications in throughput estimation and modeling in 5G and beyond 5G wireless communication systems