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Zain Tanzania PowerPoint Presentation
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Zain Tanzania

Zain Tanzania

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Zain Tanzania

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  1. Quality and Performance snap-shot Draft 2.0 - Samples Zain Tanzania

  2. Highlight The sample presented data is only snap shot for the network quality and performance based on the data delivered by Zain Tanzania. Core data have been collected only, but based on the results and for further details more BSS data will be needed to indicate route causes for several problems. More investigation will be needed on the ground in order to point at the route cause for every deficiency and to define the owner for it.

  3. SS7 Signaling Load • SS7 Load Highlights: • High utilization in more than 22% of links • Most of the highly utilized links are showing high utilization almost all over the day. • Mainly the BSC links are highly utilized. • HLR link load is normal. • Some Links with BSC are highly overloaded, which may cause call loss. • In and out link load of some SCP links are not properly distributed ( Load sharing issue) • Link Busy hours found to be between 20:00 and 22:00 every day.

  4. SS7 Signaling Load • Proposed solutions: • Increase Link Capacity between BSCs and MSCs • Proper Load sharing mechanisms • Information required for more investigations: • SS7 network design • Signaling routing Plan

  5. SMS Overview

  6. SMS Overview • Highlights • Low Success rate which can impact revenue. • Lowest MS to MSC Success rate (72% to 66%) in MSS04 and MSS08 • Lowest MSC to MS delivery success rate in MSS05 More details will be required for route cause analysis by having data from the SMSC and additional traces on the SMSC links using Asteilia.

  7. Trunk Group Analysis

  8. Trunk Group Analysis

  9. Trunk Group Analysis TG with over 75% utilization: • Sample: • High incoming congestion (30% to 70%): B4BW3TI-B4BW3TO of MSS05 • High outgoing congestion (28% to 75%): B4BW3TI-B4BW3TO of MSS05 Due to the inconsistency in the data collected simplified list will be send to Operations team to provide more accurate data to go deep in routing analysis, load sharing and route causes.

  10. Trunk Group Analysis TG with over 75% utilization: • Sample: • High incoming congestion (30% to 70%): B4BW3TI-B4BW3TO of MSS05 • High outgoing congestion (28% to 75%): B4BW3TI-B4BW3TO of MSS05 Due to the inconsistency in the data collected simplified list will be send to Operations team to provide more accurate data to go deep in routing analysis, load sharing and route causes.

  11. Switching Capacity CP Loading is behaving normal in most of the elements, while strange behavior as per the stats collected show sharp drop in subs number in couple of Nodes, it can be wrong reading or re-homing activity but more details will be needed in order to know the reasons. Samples available in the next slide

  12. Switching Capacity

  13. Switching Capacity - BHCA Due to the short period of data collected it was difficult to determine the real trend of traffic model in the network, but it was clear based on the data provided that several nodes are holding traffic more than the capacity OR the licensed capacity of the node. Samples in the next slides More data will be needed to investigate more this behavior together with the sudden drop of subscribers appeared in the VLR’s

  14. Switching Capacity - BHCA

  15. Paging Failure Rate

  16. Paging Failure Rate

  17. Paging Failure Rate

  18. Paging Failure Rate • Highlights • Paging Failure rate compared to the market is very high, which will have direct impact on revenue. Average success rate is around 75% • Peak failures (MAX-PG-FAIL) happen during peak hours, while the min cases are during low pears i.e early morning. • Detailed investigation will be needed in terms of signaling, load, configuration and design to define the route cause.

  19. Location Update Success Rate

  20. Location Update Success Rate

  21. Location Update Success Rate • Highlights • LU Success rate is very low compared to the market, it varies between 83 and 95% in the peak hours • More investigation from the BSS interfaces will be needed to identify route cases and solutions.

  22. Handover Success Rate

  23. Handover Success Rate

  24. Handover Success Rate

  25. Handover Success Rate

  26. Location Update Success Rate • Highlights • Handover Success rate is very low compared to the market, it varies between 76 and 91% in the peak hours • More investigation will be needed to go in more details for the BSC, LAC, Cell level and define whether the reason is design or configuration i.e missing neighbors and wrong boarders.

  27. Summary of Highlights The slides are self explanatory and give an image about the QOS during the time window analyzed. The data presented is a snap shot analysis about the network quality in a certain time window and under certain circumstances, This do not give us or any auditor the chance to judge the network overall performance and quality because further detailed analysis per entity for longer periods will be needed, BUT it is clear from the snap shot taken that there is lot of things might which can be investigated and improved which will have direct impact on improving the quality and grade of services provided to the end user as well will help improving the revenues in the operation. IN data was not provided and it will not be tackled during this phases of the assessment, while additional list of data will be requested by Shabakkat in order to go in more details in the SS7 and Trunks problems as well the routing and congestion issues.