/ WHERE IT MATTERS

REAL-TIME
REAL-TIME
We use big data to analyse mobile network performance every 15 mins, measuring 4G and 5G network capacity and coverage.

BIG DATA
Big Data
We process this data to provide in- depth analysis and understanding of how networks are performing in any location.

MODELLING
MODELLING
DenseWare enables us to visualise network weaknesses, provide recommended small cell deployment locations and predicts improvements to end users.

REAL-TIME
We use big data to analyse mobile network performance every 15 mins, measuring 4G and 5G network capacity and coverage.

BIG DATA
We process this data to provide in- depth analysis and understanding of how networks are performing in any location.

MODELLING
DenseWare enables us to visualise network weaknesses, provide recommended small cell deployment locations and predicts improvements to end users.
PINPOINTING OPTIMAL DEPLOYMENT LOCATIONS
Dense Air analyses network performance on a building by building basis using Big Data and our Network Quality Test (NQT) application, providing recommended locations for the deployment of small cells via our Denseware portal.
UNDERSTANDING NETWORK LIFECYCLES
DenseWare analysis is available via an internet portal, providing specific results that allow our clients and potentially end users to identify the improvements that can be made to the network using small cells, street by street, building by building, and even country by country. Users can also view where Dense Air have already deployed small cell assets that can be rapidly activated, using the Neutral Host capabilities, to improve network performance.

INFORMED NETWORK ENHANCEMENT
Denseware and NQT is a holistic suite of tools that informs where investment is needed and how targeted deployment of small cells running on dedicated spectrum can drastically improve end user experience, while at the same time improve the overall network efficiencies and ultimately the cost per bit of any mobile network.
Determine Expected Network Performance in any location. Globally
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Identify Target Areas
Applied Machine Learning
Pre-Deployment Network Characteristics
Calculate Network Improvements
In modelling the expected outcome of small cell deployments, Denseware allows operators to be much more efficient in how they expand or improve their network.
Denseware can predict the improvement a single small cell may have when deployed using UE Relay in cell edge conditions, or with a dedicated Fibre backhaul. It can also factor in the improvement this small cell will make to the surrounding Macro network, by reducing the load and increasing spectral efficiency.
Combined Metrics to provide insight into End User Experience
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How NQT Works
Rather than relying on traditional network parameters such as SINR, RSRP and throughputs as a gauge of network performance, our indicators measure end user experience at application level – providing a more valuable insight into Quality of Experience (QoE).
NQT Score
As NQT carries out various tests during its cycle it will assign different NQT scores based on the ability for the network to supporting different interactions, such as streaming video, making a call or uploading content to the web. These scores are shown using a simple colour scheme which means:
Grey: score of less than 1.0 means your “smartphone apps basically don’t work…”
Red: score of 1.0 to 2.0 means your “smartphone apps work poorly, some will, some won’t…”
Orange: score of 2.0 to 3.0 means your “smartphone apps all work, but some take longer to respond…”
Yellow: score of 3.0 to 4.0 means your “smartphone apps all work well, as intended, like Green: you are connected to a super fast Wi-Fi network”
Green: score of 4.0 to 5.0 means your “smartphone apps all work as well as is possible… Perfect network performance”
From the Ground to the Cloud
For operators this means that with more people testing, providing network insights, the more informed they can be about future deployment decisions.
NQT Denseware Cycle
Using this data we can then predict the network improvement that can be made through the deployment of one or more small cells. This cycle of test, deploy, analyse accelerates the machine learning process, fine tuning future network deployments and creating a more cost effective, responsive and agile network.
DOWNLOAD
Download NQT to test and understand your network
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