Updating analysis of key performance indicators of 4G LTE network with the prediction of missing values of critical network parameters based on experimental data from a dense urban environment

Prioritizing action plans to save resources and better achieve municipal solid waste management KPIs: An urban case study

The management of municipal solid waste (MSW) in cities is one of the most complex tasks facing local administrations. For this reason, waste management performance measurement structures are increasingly implemented at local and national levels. These performance structures usually contain strategic objectives and associated action plans, as well as key performance indicators (KPIs) for organizations investing their resources in action plans. This study presents the results of applying a methodology to find a quantitative-based prioritization of MSW action plans for the City Council of Castelló de la Plana in Spain. In doing so, cause-effect relationships between the KPIs have been identified by applying the principal component analysis technique, and from these relationships it was possible to identify those action plans which should be addressed first to manage public services more efficiently. This study can be useful as a tool for local administrations when addressing the actions included in their local waste plans as it can lead to financial savings.Implications: This paper introduces and implements a methodology that uses principal component analysis to analyze real data from waste management KPIs and provide municipal solid waste managers with a decision-making tool for prioritizing action plans. The methodology saves financial resources and time, as well as reinforcing the probability of reaching the meta values of the main performance system KPIs.

Development of a Multilayer Perception Neural Network for Optimal Predictive Modeling in Urban Microcellular Radio Environments

Modern cellular communication networks are already being perturbed by large and steadily increasing mobile subscribers in high demand for better service quality. To constantly and reliably deploy and optimally manage such mobile cellular networks, the radio signal attenuation loss between the path lengths of a base transmitter and the mobile station receiver must be appropriately estimated. Although many log-distance-based linear models for path loss prediction in wireless cellular networks exist, radio frequency planning requires advanced non-linear models for more accurate predictive path loss estimation, particularly for complex microcellular environments. The precision of the conventional models on path loss prediction has been reported in several works, generally ranging from 8–12 dB in terms of Root Mean Square Error (RMSE), which is too high compared to the acceptable error limit between 0 and 6 dB. Toward this end, the need for near-precise machine learning-based path loss prediction models becomes imperative. This work develops a distinctive multi-layer perception (MLP) neural network-based path loss model with well-structured implementation network architecture, empowered with the grid search-based hyperparameter tuning method. The proposed model is designed for optimal path loss approximation between mobile station and base station. The hyperparameters examined include the neuron number, learning rate and hidden layers number. In detail, the developed MLP model prediction accuracy level using different learning and training algorithms with the tuned best values of the hyperparameters have been applied for extensive path loss experimental datasets. The experimental path loss data is acquired via a field drive test conducted over an operational 4G LTE network in an urban microcellular environment. The results were assessed using several first-order statistical performance indicators. The results show that prediction errors of the proposed MLP model compared favourably with measured data and were better than those obtained using conventional log-distance-based path loss models.