Automatic selection of window add in a WCDMA radio network based on cost function minimization

Flanagan, Adrian; Novosád, Tomáš

doi:10.1109/isssta.2002.1049344

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…Comparisons among different optimization methods were not made, expect for one case. The rule-based approach is not necessarily superior to conventional optimization methods, such as the gradient-descent minimization [5][6][7] in terms of convergence speed, stability or robustness. The gradient-descent algorithm utilizes stochastic search, that is, random perturbations of parameters to find the optimum values.…”

Section: Discussionmentioning

confidence: 99%

“…Study in Reference [45] proposed a method to reduce hot spot problems with the control of pilot power based on cross-correlation measures. Studies in References [3,5,7] presented methods and results on soft-handover parameter optimization. In Reference [3] the soft-handover PrxTotal denotes the total UL wideband received power, for which PrxNoise, the noise floor, is the minimum level.…”

Section: Previous Work On Wcdma Parameter Optimizationmentioning

confidence: 99%

“…Studies in References [5,7] optimized the soft-handover parameters with a gradient-descent minimization of a cost function that described blocking and total base station power. A more general study on soft-handover gains and their impact on WCDMA network performance was presented in Reference [34].…”

Section: Previous Work On Wcdma Parameter Optimizationmentioning

confidence: 99%

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Automated optimization of key WCDMA parameters

Hoglund

Valkealahti

2004

Wirel. Commun. Mob. Comput.

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SummaryThis paper validates the feasibility of automated optimization of key wideband code division multiple access (WCDMA) radio resource management parameters using control methods. The parameters are regularly adjusted in order to improve performance. The parameters examined in this study include the total cell transmission power target, the received total interference target, the downlink radio link power maximums, the handover windows and the pilot channel powers. The control was based on expert-defined rules, which applied specific trade-off policies and statistics of poor quality calls, blocking, packet queuing, power and interference levels and terminal measurements to qualify the parameter values. The approach was validated using a dynamic WCDMA system simulator with a deployment of macro and micro cells on a city region. Results on automated optimization of single parameters on cell level and results on simultaneous multi-parameter optimization on cell-cluster level are presented in this paper. The use of the automated parameter optimization methods was shown to result in a significant increase of capacity in comparison to the default parameter settings.

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Section: Discussionmentioning

confidence: 99%

Section: Previous Work On Wcdma Parameter Optimizationmentioning

confidence: 99%

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Automated optimization of key WCDMA parameters

Hoglund

Valkealahti

2004

Wirel. Commun. Mob. Comput.

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“…In [3], [5] and [13] examples of 3G optimization cases are represented. In general the availability of references related to 3G analysis and optimization is limited.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Mobile Radio Access Network Using the Self-Organizing Map

Raivio

Simula

Laiho

et al. 2003

Integrated Network Management VIII

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Abstract:Keywords:Mobile networks produce a huge amount of spatia-temporal data. The data consists of parameters of base stations and quality information of calls. The Self-Organizing Map (SOM) is an efficient tool for visualization and clustering of multidimensional data. It transforms the input vectors on two-dimensional grid of prototype vectors and orders them. The ordered prototype vectors are easier to visualize and explore than the original data. There are two possible ways to start the analysis. We can build either a model of the network using state vectors with parameters from all mobile cells or a general one cell model trained using one cell state vectors from all cells. In both methods further analysis is needed. In the first method the distributions of parameters of one cell can be compared with the others and in the second it can be compared how well the general model represents each cell.Neural networks, self-organizing map, cellular network, performance optimisation. IntroductionAs the launch of third generation technology approaches, operators are forming strategies for the deployment of their networks. These strategies must be supported by realistic business plans both in terms of future service demand estimates and the requirement for investment in network infrastructure.When provisioning 3G services the control for the access part can be divided into three levels. Two lowest layers are radio resource management (RRM) functionalities and the highest hierarchy level is control performed by the network management system (NMS). More about this control hierarchy can be found in [10]. The scope of this paper is the NMS level. The role of NMS is essential owing to the fact that major enhancements or new service roll-outs are planned by utilizing the measured long term performance data from existing network.The multidimensional performance space in future cellular networks force the tra-

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Analysis of mobile radio access network using the self-organizing map

Raivio¹,

Simula²,

Laiho³

et al.

IFIP/IEEE Eighth International Symposium on Integrated Network Management, 2003.

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Mobile networks produce a huge amount of spatiMemporal data. The data consists of parameters of base stations and quality information of calls. The Self-organizing Map (SOM) is an efficient mol for visualization and clustering of multidimensional data. It transforms the input vectors on two-dimensional grid of prototype veeors and orden them. The ordered prototype yectDrs are easier to visualize and explore than the original data. Then are two possible ways to start the analysis. We can build either a model Of the network using state vectors with parameters from all mobile cells or a general one cell model trained using one cell state vectors from all cells. In both methods huther analysis is needed. In the Bnt method the distributions of parameters of one cell can be compared with the others and in the second it can be compared how well the general model represents each cell.

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Automatic selection of window add in a WCDMA radio network based on cost function minimization

Cited by 4 publications

References 5 publications

Automated optimization of key WCDMA parameters

Automated optimization of key WCDMA parameters

Analysis of Mobile Radio Access Network Using the Self-Organizing Map

Analysis of mobile radio access network using the self-organizing map

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