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Slide 1 New Concepts for Optimization Enhanced Approaches Using Measured Data presenter Richard Edge Contents Developing a new concept Veritune Validating the Veritune concept Developing a practical s[.]
New Concepts for Optimization Enhanced Approaches Using Measured Data presenter Richard Edge Contents Developing a new concept - Veritune Validating the Veritune concept Developing a practical solution Conclusions Developing a new concept - Veritune Is this really the best way of doing things? (Picture – Testing Golf Clubs by Heath-Robinson) The importance of antenna optimization in UMTS If a cell causes too much interference in a UMTS / WCDMA network… … frequency planning is no longer an option… …the only options are power and antenna optimisation Downtilt, azimuth and power changes control are used to control radio propagation Antenna optimisation in UMTS is both more frequent and important than in GSM networks A typical antenna optimization process Changes identified by: Predicted data ‘Ranging’ spreadsheets Experience… Antenna changes in field KPI analysis cost Uncertain results Time-consuming Field drive drive 03 Optimized! Validation 02 01 Identify changes to antenna coverage time How we speed up the process? Reducing the loops is key to reducing time to revenue: Get an acceptable solution as early as possible How we improve the certainty that our changes will work? Field drive Validation Drive Test drive 02 01 Time consuming Antenna changes in field KPI analysis Relatively quick Identify changes to antenna coverage Some principles for a new concept in optimisation to reduce the number of loops Improve prediction accuracy by simplifying the problem (rather than increasing the complexity of the model) Wherever possible use real data (rather than predicting measurable information) Guide the user towards the solution (rather than prescribing a solution automatically) Present results in the same format as measured data (rather than presenting a grid of data) (Picture – NASA Space Pen) Review – how a planning tool predicts network performance Antenna Masking Required inputs: Map database, antenna performance and configuration Interfering Cells Modified Cell Link Budget Required inputs: Equipment performance & configuration Path Loss Required inputs: Map database, model diffraction/penetration/ reflection assumptions Interference Generation Required inputs: Map database, model diffraction/penetration/ reflection assumptions, antenna performance and configuration, traffic geographical distribution, building penetration, interferer link budget, traffic modelling, power control, RRM modelling, mobile location in buildings The planning tool prediction process Traffic Model Map Data Pathloss Prediction Propagation Model Antenna Masking Model Pilot EcNo Site Configuration Channel Model Traffic Map Pilot RSCP RSSI Service EbNo Served Users Applying the principles - Veritune Antenna Masking Required Inputs: Map database, antenna performance and configuration Interfering Cells Modified Cell Link Budget Not Required Path Loss Measure Signal Strength Interference Generation Required Inputs: Map database, antenna performance and configuration of changed interfering cells An improved antenna optimisation process with Veritune Optimized! Field Validation drive drive 02 01 Optimisation loops are taken out of the field onto the desktop cost Antenna changes in field KPI analysis Less cycles saves time, reduces cost time Traditional process With Actix Veritune Actix Veritune on desktop Validating the Veritune concept Validation approach Actix Veritune Before Drive Configuration Changes Traditiona l Tuning Synthetic Drive Comparison After Drive • Validation was carried out using historically collected data • Synthetic drives calculated off ‘before’ measured drives were compared to ‘after’ measured drives, and the error at each data bin calculated Input measurements Trials of clusters (33 sector changes, 20 control sectors) have been carried out across a variety of rural, suburban and urban environments, in flat and hilly terrain A total of 8545 data bins were considered, approximately 427km driving No data filtering was carried out: Sector heights ranged between 12m and 48m Measurements both within and beyond the main lobe by bearing were considered All ranges were considered up to 14km (including less than 500m) The validation results Standard deviations significantly improve upon typical propagation model accuracies (benchmark 8dB – top of the below graph) The mean error was within 1dB of the control set for all clusters Standard Deviation (dB) Control Veritune Rural/Suburban A Rural/Suburban B Urban Hilly dense urban 4.8dB without outlying sectors How consistent are the results? A majority of sectors exhibited consistent performance: 20 Number of Sectors 18 16 standard deviation 14 mean 12 10 0 to 1 to 2 to 3 to 4 to 5 to 6 to 7 to 8 to 9 to 10 Range (dB) Outlying sectors Distribution of error with distance Error Plot with Distance 30.00 20.00 Error (dB) 10.00 0.00 Rural -10.00 -20.00 -30.00 2000 4000 6000 8000 Distance (m) 10000 12000 14000 Developing a practical solution Implementing the principles in practice Enabling easy steps to fast, in-office optimization: Step Identify poorly performing areas Step Review guidance towards possible solution Step Simulate the effect of antenna changes