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 DynOptim

 

 

DynOptim

Automatic Optimisation Tool for 3G Radio Access Networks

UMTS optimisation challenges

The UMTS networks are based on W-CDMA, a new radio access technology.  Even for existing 2G mobile operators, deployment of W-CDMA networks requires a completely new radio network infrastructure resulting in huge investments.

At a very early stage of the deployment process, optimisation becomes a challenge in order to decrease CAPEX, e.g. to minimise the number of sites to be initially deployed, still ensuring the requested coverage with expected quality of service for the subscribers.

As UMTS networks are expanding, network configuration should regularly be updated to track increase of traffic, customer behaviour change, introduction of new services, etc… At this stage, optimisation process should be used to reshape coverage and balancing of traffic among active cells while minimising additional CAPEX (new sites) and OPEX (e.g. intervention on existing sites).

DynOptim key features

DynOptim associates a high performance dynamic system level simulator with state-of-the-art optimisation techniques to achieve automatic RF and RRM optimisation  of 3G networks.

Benefits from  dynamic system simulator

  • ·Assess performance of 3G network for expected business models

  • ·Please refer to Dynamo data sheet for more information

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Benefits from  optimisation engine

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  • ·Minimise OPEX and CAPEX by

    • ·maximising network capacity

    • ·maximising network coverage

    • ·maximising the service capabilities and grade of service offered to the end-user

  • ·Improved CPU performance through proprietary techniques

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User interface

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  • ·Optimisation parameters and cost functions are configured through a user-friendly interface

  • Monitoring tools are available during optimisation to monitor in real-time, the value of initial, current or optimised value of parameters but also the evolution of cost function

 

DynOptim specifications

Fast and reliable evaluation of grade of service

Grade of Service (GoS) is the percentage of satisfied users, i.e. users that are not outed, blocked, dropped and that have a good radio conditions for the whole life time of the call. GoS  is computed by a large scale dynamic simulator. The engine of the simulator can simulate the physical layer of thousands of mobiles simultaneously. Thus, GoS is the result of a meaningful simulation relevant to the specific customer’s use case.

Composite cost function definition

DynOptim maximises a user-defined cost function that can be a combination of several basic cost functions (typically grade of service, number of active cells, number of active sites, …). Each basic cost function that contributes to the composite cost function is allocated a weight according to the actual operator’s CAPEX and OPEX structure.

Optimisation of different parameters simultaneously

DynOptim enables simultaneous optimisation of many parameters, that can be related to RF and/or RRM (Radio Resource Management). For each parameter to optimise, the user can define a range of values allowed for this parameter, when applicable. The parameters values can be calculated in absolute or relative value.

Multi stage problem definition and analysis

DynOptim brings flexibility in optimisation process definition. It is possible to differentiate three different set of cells:  the set of cells where parameters have to be optimised , the set of cells where cost function will be calculated and finally the set of cells where cost function is evaluated for on-line monitoring purpose, however not taken into account in the computation of the global cost function.

Hierarchical Optimisation Engine (HOPE)

DynOptim performance is boosted by our proprietary optimisation algorithm from the knowledge of local cost functions provided by the simulation engine at each cell level.

Trimming

This proprietary  technique improves CPU performance. Simulation engine evaluates cost function at different steps, that can be used to discard poor solutions at an early stage. When intermediate value of cost function is felt poor according to some user-defined criteria, it is possible to stop the evaluation process and to reject the solution under evaluation.

Flexible optimisation workflow

DynOptim can achieve joint optimisation of a set of parameters in one optimisation run. One optimisation process can be realised with successive optimisation runs related to different set of parameters.

Backup of all optimum solutions

DynOptim saves all optimum configurations found since the beginning of one optimisation process.

Plug-and-Play with Dynamo

All optimisation configurations that have been stored by DynOptim can be synchronised with Dynamo. The replay of one configuration with Dynamo enables full and real-time observation of the living radio network for some specific optimised parameter.

 

Optimisation process

DynOptim features

Optimisation methods

  • Genetic algorithm including clone avoidance and cost-controlled hybridation

  • Simulated Annealing

  • Local gradient search

Optimisation parameters

The following parameters can be optimised on a per cell level

  • RF parameters (azimuth, mechanical and/or electrical tilt, site activity, cell activity)

  • Power (CPICH power)

  • Cell selection (quality measure criterion, max UL Tx power, Qqualmin, Qrxlevmin, Qoffset1s,n, Qoffset2s,n)

  • Call admission control (report criterion and range, forbid impact on reporting range, maximum active set size, W, hysteresis H1A/H1B, cell offset, reporting deactivation / replacement activation thresholds)

  • Timer (dropping timer T313)

Optimisation cost function

  • Multi-criterion optimisation, with flexible weighting of many elementary cost functions (KPIs)

  • Examples of supported (KPIs) : 

  • GoS statistics

  • Traffic statistics

  • Coverage statistics

  • Number of additional cells/sites

  • Number of on-site interventions

  • Number of remote interventions

Stop criteria

  • Both interactive and user-defined stop criteria

  • Possibility to stop the optimisation process at any time with a backup of the optimisation parameters values that has given the best configuration (optimised cost function) until this time

  • Detailed real-time monitoring of optimisation algorithm execution

Optimisation process performance

  • Trimming techniques for fast detection of low-performance simulations

Project management

  • Save all the optima since the beginning of the optimisation

  • Tree visualisation of project history

  • Possibility to synchronise Dynamo with these optima

Configuration flexibility

  • Graphical selection of parametrisation cells, cost optimisation cells and adjacent cost observation cells

  • Multi-parameter joint optimisation

Multi-processing

  • Optimisation engine and GUI can be mapped on different platforms (e.g. Linux and Windows)

  • Multi-thread design

  • Easy migration path to multi-processors

Hardware configuration

  • Minimum configuration: 2 GHz Pentium IV PC, RAM 512 Mo, Windows or Linux OS

  • Recommended configuration: 3.2 GHz Pentium IV hyper-thread PC, RAM 1 Go, Windows