CrowdZine

Radio Network Analytics Using Crowdsource Data

BI Nordic presents a new way of thinking and working within the data analytics and artificial intelligence area. The amount of collected crowdsourced data is continuously growing. Companies are collecting this type of data as a part of their IoT solution. To be able to give as accurate and statistically confident results as possible, high volume of data is of key importance.


Know Your Network – Know Your Data


Benchmarking and trend analysis of deployed networks. QoE performance, cell density, features and coverage comparing network operators.


CrowdZine
Antennas
 

Installation Issues


Does your network work as expected? Network rollouts are done under time pressure, and human mistakes cannot be neglected. But how can you backtrack your installation issues? Crowdzine will give you information about findings such as Swapped antenna feeders or traffic picked up in the backlobe.


Fault Management


Pinpoint problems in your network, such as areas with no coverage.


Data Coverage
Network Optimization
 

Network Optimization


Does your network work as expected? Network rollouts are done under time pressure, and human mistakes cannot be neglected. But how can you backtrack your installation issues? Crowdzine will give you information about findings such as Swapped antenna feeders or traffic picked up in the backlobe.


5G Network Rollout


Does your network work as expected? Network rollouts are done under time pressure, and human mistakes cannot be neglected. But how can you backtrack your installation issues? Crowdzine will give you information about findings such as Swapped antenna feeders or traffic picked up in the backlobe.


5G

Features

Time and Cost Savings

Interactive 3D Map

Artificial Intelligence

Correlation

Using crowdsourced data, the operators can cut down on the cost for their currently expensive data collection investments compared to traditional methods.
An interactive 3-D web-based map solution visualizes network traffic. These state-of-the-art web components optimize the end-user experience. Extra layers showing site details and demographic data are added to the solution to help the end-user make correction decisions.
The crowdsourced data is pre-processed and fed into an AI engine with both geographical clustering and supervised learning capabilities. It is used to identify hotspots, trace traffic patterns, and facilitate fault detection in operations.
Crowdsourced data is collected when/where/how the user is using the network. This means that the collected data can reveal rush hours, high intense areas, as well as technologies used in the network.