Statistical Learning in Automated Troubleshooting: Application to LTE Interference Mitigation

This paper presents a method for automated healing as part of off-line automated troubleshooting. The method combines statistical learning with constraint optimization. The automated healing aims at locally optimizing radio resource management (RRM) or system parameters of cells with poor performance in an iterative manner. The statistical learning processes the data using Logistic Regression (LR) to extract closed form (functional) relations between Key Performance Indicators (KPIs) and Radio Resource Management (RRM) parameters. These functional relations are then processed by an optimization engine which proposes new parameter values. The advantage of the proposed formulation is the small number of iterations required by the automated healing method to converge, making it suitable for off-line implementation. The proposed method is applied to heal an Inter-Cell Interference Coordination (ICIC) process in a 3G Long Term Evolution (LTE) network which is based on soft-frequency reuse scheme. Numerical simulations illustrate the benefits of the proposed approach.Comment: IEEE Transactions On Vehicular Technology 2010 IEEE transactions on vehicular technolog

Fixed Rank Kriging for Cellular Coverage Analysis

Coverage planning and optimization is one of the most crucial tasks for a radio network operator. Efficient coverage optimization requires accurate coverage estimation. This estimation relies on geo-located field measurements which are gathered today during highly expensive drive tests (DT); and will be reported in the near future by users' mobile devices thanks to the 3GPP Minimizing Drive Tests (MDT) feature~\cite{3GPPproposal}. This feature consists in an automatic reporting of the radio measurements associated with the geographic location of the user's mobile device. Such a solution is still costly in terms of battery consumption and signaling overhead. Therefore, predicting the coverage on a location where no measurements are available remains a key and challenging task. This paper describes a powerful tool that gives an accurate coverage prediction on the whole area of interest: it builds a coverage map by spatially interpolating geo-located measurements using the Kriging technique. The paper focuses on the reduction of the computational complexity of the Kriging algorithm by applying Fixed Rank Kriging (FRK). The performance evaluation of the FRK algorithm both on simulated measurements and real field measurements shows a good trade-off between prediction efficiency and computational complexity. In order to go a step further towards the operational application of the proposed algorithm, a multicellular use-case is studied. Simulation results show a good performance in terms of coverage prediction and detection of the best serving cell

Mobility-aware Scheduler in CoMP Systems

International audienceThe main weakness of coordination techniques in LTE-Advanced networks is the extra resource consumption incurred by the joint transmission from several base stations. In this paper, we propose a new scheduling policy that performs coordination primarily for users staying at the cell edge, without mobility. Other cell-edge users are likely to move and to be served in better radio conditions where cell coordination is not required. We compare the performance of this algorithm to other usual scheduling policies in the presence of elastic traffic through the analysis of flow-level traffic models

Particle Swarm Optimization for Mobility Load Balancing SON in LTE Networks

This paper presents a self-optimizing solution for Mobility Load Balancing (MLB). The MLB-SON is performed in two phases. In the first, a MLB controller is designed using Multi-Objective Particle Swarm Optimization (MO-PSO) which incorporates a priori expert knowledge to considerably reduce the search space and optimization time. The dynamicity of the optimization phase is addressed. In the second phase, the controller is pushed into the base stations to implement the MLB SON. The method is applied to dynamically adapt Handover Margin parameters of a large scale LTE network in order to balance traffic of the network eNodeBs. Numerical results illustrate the benefits of the proposed solution

Opportunistic gains of mobility in cellular data networks

International audienceIn this paper, we assess the performance gains of mobility on the downlink of cellular data networks. These gains are only due to the elastic nature of traffic and thus observed even under a blind, fair scheduling scheme: data are more likely transmitted when users are close to the base stations, in good radio conditions. This phenomenon is further amplified by opportunistic scheduling schemes that exploit multiuser diversity. The results are based on the analysis of flow-level traffic models and validated by system-level simulations

How Mobility Impacts the Performance of Inter-cell Coordination in Cellular Data Networks

International audienceIn this paper, we assess the performance of inter-cell coordination in the presence of mobility. This performance depends primarily on the resource allocation scheme. Indeed, a scheduling strategy which may seem efficient when users are static can lead to bad performance when users are mobile. Several scheduling policies are investigated. Their performance critically depends on their ability to predict users' mobility. The results are based on the analysis of flow-level traffic models