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  • An improved capacity model based on radio measurements for a 4G and beyond wireless network
    Publication . Parracho, Diogo; Duarte, David; Pinto, Iola; Vieira, Pedro
    The mobile networks utilization is increasingly high, which implies a efficient resource network management coupled with a realistic capacity model. The aim of this paper is to present a capacity platform for Fourth Generation (4G) mobile networks, based on real measurements. The core of the proposed method is the deployment of a Multiple Linear Regression (MLR) model, based on propagation conditions, channel quality and delays for a specific cell. Information about how to locate the resource bottleneck and the related handling suggestions are provided. This approach outputs the maximum cell throughput at the busy hour, under realistic conditions. The method was developed using real data extracted from a live mobile network.
  • An enhanced capacity model based on network measurements for a multi-service 3G system
    Publication . Parracho, Diogo; Duarte, David; Pinto, Iola; Vieira, Pedro
    With the ongoing growth on mobile networks utilization, new challenges come up in order to achieve a better efficient resource network management. The purpose of this paper is to present a multi-service platform based on admission curves for Third Generation (3G) and beyond mobile networks, depending on some cell characteristics, which are calculated based on real measurements. The model considers admission curves based on the Multidimensional Erlang-B model, which defines the maximum limit of resource utilization for a given Quality of Service (QoS), and will manage traffic between several services. The proposed method takes different specific constraints for each traffic environment based on network performance. To estimate the cell characteristics, for Voice and Packet Switched (PS) Release 99 (R99) services, a method is proposed, based on the Multiple Linear Regression model and dependent on Key Performance Indicators (KPI) taken from a live mobile network. For High Speed Downlink Packet Access (HSDPA) service, a different approach is set since there is a well defined time to transmit data (Transmission Time Interval (TTI)) along with other important features, like Channel Quality Indicator (CQI) and Block Error Rate (BLER), to be considered.