Creating a Multi-Dimensional Map Stream You can create a two-dimensional map by dragging two parameters onto a map, then dragging one on top of the other in the legend. This will replace the two data streams with one composite stream. The color of the points in the resulting stream will be determined by the value of the stream that was dragged and the size of the points is determined by the value of the other stream. Close the current map.
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Actix assumes no responsibility or liability for any errors or inaccuracies that appear in this documentation. All rights reserved. All trademarks are hereby acknowledged. Technological advances in software and hardware usually enable these productivity improvements, although there is often a lag between the availability of the new technology, and its widespread acceptance and deployment by industry.
This gap is sometimes called the productivity lag factor. When the technology initially became available, it was only sparingly deployed, and the units were often placed inside bank buildings where the productivity enhancements they offered were limited. Likewise, unattended gasoline pump technology has been slow to roll out in Europe, but as the technology has become widely adapted, huge efficiency gains have been realized. The wireless industry is now at a similar point.
It understands that the traditional labor- intensive techniques for maximizing performance and capacity in wireless infrastructure are fundamentally limited by a lack of structured algorithms to determine improvements. Actix Analyzer offers the possibility to look at drive test data and scanner data to fully optimize a UMTS network. It allows the engineer to understand the causes and reasons for drop calls and access failures.
Analyzer offers an unprecedented capability to execute a detailed examination of message flows and automating statistical analyses of performance. Analyzer significantly accelerates the rollout, troubleshooting and optimization of the UMTS network.
Actix has embedded intelligence in the software to allow the RF engineer to visualize specific events and understand real problems occurring in the network. All of the lessons learned and the techniques developed over a year period have been incorporated into these powerful, vendor-independent solutions for UMTS infrastructure. This document provides an overview of the key benefits, applications and features of Analyzer.
For additional information, including white papers and other literature, please refer to www. All of the lessons learned and the techniques developed over a year period have been incorporated into these powerful, vendor- independent solutions for cdma, GPRS and UMTS infrastructure. Analyzer may be used in field trial, benchmarking and operational settings to automate the process of quantifying network performance, thereby mitigating the risk of performance problems during commercial operation.
Applications addressed by Analyzer first become pertinent during the new technology rollout, as shown in Figure 1. Then, as first-generation technology is rolled out for soft or commercial launches, Analyzer continues to address a number of critical challenges. As new sites are coming on air and more customers are accessing the network, the real challenge for the RF engineer is to maximize the coverage, capacity and quality of service.
Analyzer offers a number of applications applicable to these ongoing challenges. It gives a detailed analysis during the whole drive route. From missing neighbor to pilot pollution detection, the different embedded events give an absolute advantage to the RF engineer in understanding the source of different problems.
Figure 2 depicts some of the major processes performed by engineering teams during the initial rollout, immature buildout and mature growth phases; and indicates the key radio- link configuration tasks that are common across these processes. The following sections provide an outline plus additional details of the processes and tasks typically performed during those phases. Because Analyzer is based on an open architecture platform—which includes www.
Integration with Network Element Database to audit existing neighbor lists and suggest changes, and to correlate non-unique measured data attributes such as Scrambling Code with unique identifiers such as Sector ID. User definable binning is used to reduce the number of measurements points in each bin to create one value per bin — optionally, no binning at all can be applied and the analysis will run on the full data set.
At each point along the drive test, a list of prospective neighbors is accumulated as indicated in Figure If a neighbor signal is within a user-definable threshold of the best server in the active set, then it is considered as a potential neighbor. A symmetrical neighbor array is created in memory which records the number of times each sector ID is seen as a prospective neighbor of another sector ID as shown in Table 1.
Cell D is not within the required threshold and so is not counted as a prospective neighbor, nor is Cell E which did not have a measurable signal contribution at this point in the drive test. Statistics on handoff state may then be calculated and presented in a report format. Excessive handoff state reduces capacity and increase infrastructure costs for a given traffic level.
Figure 25 shows a sample set of scanner data for three individual SCs with color and vertical lines indicating transitions of pilots into and out of the Active Set.
For the engineer, it is an easy way to look at the conditions before the call started and the end result. For the engineer, it is an easy way to look at the conditions right before the call ended. For the engineer, it is an easy way to look at the average conditions during the call.
For the engineer, it is an easy way to look at the conditions right after the call ended. If it fails to reach the CC Connect, it should be pegged as a call failure and this module should give the reason for it. Refer to section 3. Figure 31 shows a typical analysis executed by the call setup status module. Figure Example of a log file analyzed by the call setup status module www. This module has the same structure and analysis as the call setup status module except for a few differences.
On the other hand, it gives the call sequence with detailed information on every call and the outcome of it. It gives the engineer the possibility to look at individual calls on a message-by-message basis. Figure Example of a log file analyzed by the call sequence analysis module www. This includes all calls, even failures. To be successful, a call needs to follow the call sequence as mentioned in section 3. The statistic is calculated on a per call basis and is the difference in time when the call ends and when the call starts.
Figure 34 shows the call sustainability statistics and the call duration distribution. Figure Example of a log file analyzed by the call sustainability module www. Figure Example of a log file analyzed by the call timing analysis module www.
Figure Example of a log file analyzed by the coverage summary module www. Please see section 3. The handoff state algorithm has the following components: Using the geographic information in the log file and the SC, the network element database is searched to identify the Sector and Cell IDs of the SC Handoff state is calculated by determining the configuration of the sectors in the Active Set as shown in Figure 23 — Section 3. It shows quickly the number of: Addition: Event 1a Removal: Event 1b Replacement: Event 1c Also, it reports the number of completion for each of those events and calculates a percentage of success.
Figure Example of a log file analyzed by the soft-handover performance module www. The maximum value The minimum value The average value for that particular call Figure Example of a log file analyzed by the BLER per call module www.
Figure 42 shows an example of those different events at different moments in time with the track at the top showing the SC. This information comes from the data testing information collected during the drive test.
The following parameters are represented for both the active and the monitored sets. Using the replay tool, the engineer can follow the drive test and analyze very quickly any particular events. Figure 51 shows an example of those different events at different moments in time with the colored track at the top showing the SC.
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Global leader In use at over operators Actix Analyzer is the most widely used tool in its class. This unique position means engineers can analyze their data wherever it comes from. The data is normalized, so the same analysis works across different data sources. Automated troubleshooting Actix Spotlight is the most reliable and trusted solution on the market for helping operators troubleshoot problems. It automates identification, analysis and diagnosis of common issues and provides detailed ad hoc analysis capabilities of the uncommon ones. Based on the Actix Analyzer core platform, it facilitates the batch loading and analysis of GBs of data in a single project. Automatic diagnosis of common failures is included, with analysis from both the cell-level and radio-level perspectives.