Quality-based tuning of cell downlink load target and link power maxima in WCDMA

Hoglund, A.; Valkealahti, Kimmo

doi:10.1109/vetecf.2002.1040620

Cited by 11 publications

(10 citation statements)

References 9 publications

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“…Our previous work on WCDMA parameter optimization includes studies on pilot power optimization [40,41], optimization of admission control and power parameters [13,14] and multi-parameter optimization including optimization of handover parameters [39]. These studies form the basis for this paper.…”

Section: Previous Work On Wcdma Parameter Optimizationmentioning

confidence: 99%

“…Our previous studies on their optimization showing significant capacity gains were reported in References [13,14]. PrxTarget and PtxTarget determine the amount of traffic allowed in the cell.…”

Section: Power Parameters Prxtarget Ptxtarget Cpichtorefraboffset Amentioning

confidence: 99%

“…The simplified rule was that CPICHToRefRABOffset was increased or decreased by 0.5 dB if CostCongestion (DL) was higher or lower than CostQuality (DL) respectively. The rules actually implemented and described in Reference [13] were somewhat more complex but in practice not significantly different from the simple rule above.…”

Section: Power Parameters Prxtarget Ptxtarget Cpichtorefraboffset Amentioning

confidence: 99%

“…In Reference [13] we presented results for the optimization of CPICHToRefRABOffset and PtxTarget. Two fixed settings of CPICHToRefRABOffset were validated using the micro scenario in Figure 2.…”

Section: Automated Optimization Of Cpichtorefraboffset Ptxdlabsmax Amentioning

confidence: 99%

“…The adjustment of PtxTarget together with the autotuning of CPICHToRefRABOffset, made a significant increase in throughput possible (up to 39% compared with default parameter settings) in addition to a quality improvement [13].…”

Section: Automated Optimization Of Cpichtorefraboffset Ptxdlabsmax Amentioning

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: Previous Work On Wcdma Parameter Optimizationmentioning

confidence: 99%

Section: Power Parameters Prxtarget Ptxtarget Cpichtorefraboffset Amentioning

confidence: 99%

Section: Power Parameters Prxtarget Ptxtarget Cpichtorefraboffset Amentioning

confidence: 99%

Section: Automated Optimization Of Cpichtorefraboffset Ptxdlabsmax Amentioning

confidence: 99%

Section: Automated Optimization Of Cpichtorefraboffset Ptxdlabsmax Amentioning

confidence: 99%

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Automated optimization of key WCDMA parameters

Hoglund

Valkealahti

2004

Wirel. Commun. Mob. Comput.

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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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